system

Abstract

The system according to this embodiment aims to efficiently manage food items in a refrigerator, track expiration dates, and suggest recipes. [Solution] The system according to the embodiment comprises a camera unit, an analysis unit, a notification unit, a provision unit, and a suggestion unit. The camera unit identifies the food items inside the refrigerator. The analysis unit analyzes the information collected by the camera unit. The notification unit notifies the user of the expiration date and remaining quantity based on the information analyzed by the analysis unit. The provision unit provides information about the food items based on the information analyzed by the analysis unit. The suggestion unit proposes recipes based on the information analyzed by the analysis unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there is a problem that it is troublesome to manage the food ingredients in the refrigerator, and the expiration date cannot be well grasped and recipes cannot be sufficiently proposed.

[0005] The system according to the embodiment aims to efficiently manage the food ingredients in the refrigerator and to grasp the expiration date and propose recipes.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a camera unit, an analysis unit, a notification unit, a supply unit, and a suggestion unit. The camera unit identifies the food items inside the refrigerator. The analysis unit analyzes the information collected by the camera unit. The notification unit notifies the user of the expiration date and remaining quantity based on the information analyzed by the analysis unit. The supply unit provides information about the food items based on the information analyzed by the analysis unit. The suggestion unit proposes recipes based on the information analyzed by the analysis unit. [Effects of the Invention]

[0007] The system according to this embodiment can efficiently manage food items in a refrigerator, track expiration dates, and suggest recipes. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

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

[0028] (Example of form 1) An embodiment of the present invention describes a refrigerator food management system that uses a device such as a small camera installed inside the refrigerator to allow an AI camera to monitor all the food items inside the refrigerator. The refrigerator food management system utilizes a dedicated smartphone app equipped with conversational AI to not only check the camera feed, but also notify users of food items nearing their expiration date or with low remaining quantities, and can even tell users how long items have been in their refrigerators if they send a photo of the items. It can also provide recipes if multiple food names are sent. For example, the refrigerator food management system installs a small camera inside the refrigerator. This camera uses AI to monitor all the food items inside the refrigerator. The AI ​​camera recognizes the types and quantities of food items inside the refrigerator and collects data. For example, it identifies vegetables, fruits, meats, etc., inside the refrigerator and determines their expiration dates and remaining quantities. Next, the refrigerator food management system allows users to check the status of food items inside the refrigerator using a dedicated smartphone app. The app is equipped with conversational AI, and when the user asks a question, the AI ​​answers. For example, if the user asks, "What are the expiration dates of the food items in the refrigerator?", the AI ​​will notify them of food items nearing their expiration date. Furthermore, if you ask, "How much of the food is left in the refrigerator?", the AI ​​will notify you of any ingredients that are running low. In addition, if you send a photo of the contents of your refrigerator to the app, the AI ​​will tell you how long that ingredient has been in the refrigerator. For example, if you send a photo of milk in your refrigerator, the AI ​​will notify you that "this milk has been in the refrigerator for 3 days." Also, if you send the names of multiple ingredients to the app, the AI ​​will suggest recipes using those ingredients. For example, if you ask, "What are some recipes using tomatoes and cheese?", the AI ​​will suggest recipes such as "tomato and cheese salad" or "tomato and cheese pasta." This system makes it easy to manage the ingredients in your refrigerator and reduces waste of ingredients that are nearing their expiration date. It also allows users to use the ingredients in their refrigerator efficiently and reduce food waste. Moreover, by using conversational AI, users can easily understand the status of the ingredients in their refrigerator and get cooking ideas. In summary, this refrigerator ingredient management system allows for efficient management of ingredients in the refrigerator and reduces waste of ingredients that are nearing their expiration date.

[0029] The refrigerator food management system according to this embodiment comprises a camera unit, an analysis unit, a notification unit, a supply unit, and a suggestion unit. The camera unit grasps the food inside the refrigerator. The camera unit recognizes, for example, the type and quantity of food inside the refrigerator and collects data. The camera unit identifies food using, for example, image recognition technology. The camera unit can also identify food using barcode scanning technology. Furthermore, the camera unit can measure the quantity of food using sensor technology. The analysis unit analyzes the information collected by the camera unit. The analysis unit analyzes the type and quantity of food using, for example, an image analysis algorithm. The analysis unit can also obtain information about food using database referencing technology. Furthermore, the analysis unit can predict the expiration date of food using a machine learning algorithm. The notification unit notifies the expiration date and remaining quantity based on the information analyzed by the analysis unit. The notification unit makes, for example, a push notification to a smartphone. The notification unit can also display the information on the refrigerator's display. Furthermore, the notification unit can also make notifications using a voice assistant. The supply unit provides information about food based on the information analyzed by the analysis unit. The provision unit provides, for example, nutritional information about ingredients. The provision unit can also provide information on how to store ingredients. Furthermore, the provision unit can also provide information on the expiration date of ingredients. The suggestion unit proposes recipes based on the information analyzed by the analysis unit. The suggestion unit proposes recipes based on, for example, the names of multiple ingredients. Furthermore, the suggestion unit can also propose recipes based on the user's preferences. Furthermore, the suggestion unit can also propose recipes appropriate for the season. As a result, the refrigerator ingredient management system according to this embodiment can efficiently manage ingredients in the refrigerator and reduce waste of ingredients nearing their expiration date.

[0030] The camera unit monitors the contents of the refrigerator. For example, the camera unit recognizes the type and quantity of food inside the refrigerator and collects data. Specifically, the camera unit places multiple cameras inside the refrigerator, and each camera photographs the food from a different angle to grasp the overall picture of the food. This allows for accurate recognition of the location and arrangement of food inside the refrigerator. The camera unit identifies food using image recognition technology. For example, it applies an image recognition algorithm using deep learning to identify food based on its shape, color, and packaging design. The camera unit can also identify food using barcode scanning technology. A barcode reader installed inside the refrigerator scans the barcode of the food and compares it with a database to obtain detailed information about the food. Furthermore, the camera unit can measure the quantity of food using sensor technology. For example, by combining weight sensors and volume sensors, it can accurately measure the remaining amount of food and collect the data. As a result, the camera unit can understand the type and quantity of food inside the refrigerator in detail and support efficient management.

[0031] The analysis unit analyzes the information collected by the camera unit. For example, the analysis unit uses image analysis algorithms to analyze the type and quantity of food items. Specifically, it preprocesses the collected image data, performs noise reduction and image correction, and then classifies the type of food item using a deep learning model. The analysis unit can also obtain information about food items using database referencing technology. For example, it can obtain detailed information and nutritional components of food items from online databases based on the barcode information of the food items. Furthermore, the analysis unit can predict the expiration date of food items using machine learning algorithms. Using a model that has learned past consumption data and the characteristics of food items, it predicts the expiration date of each food item with high accuracy and notifies the user. In this way, the analysis unit can analyze the collected data from multiple angles and make food item management in the refrigerator more efficient. Furthermore, the analysis unit can analyze food item consumption patterns and usage frequency, and provide information that is useful for improving the user's purchasing behavior and dietary habits.

[0032] The notification unit notifies users of expiration dates and remaining quantities based on information analyzed by the analysis unit. For example, the notification unit sends push notifications to smartphones. Specifically, it notifies users in real time about ingredients nearing their expiration date or with low remaining quantities through a dedicated application. The notification unit can also display information on the refrigerator's display. Information such as expiration dates, remaining quantities, and storage methods for ingredients can be displayed on a screen on the refrigerator door, allowing users to check this information each time they open the refrigerator. Furthermore, the notification unit can also use a voice assistant for notifications. For example, a voice assistant built into the refrigerator can provide voice notifications about ingredients nearing their expiration date or with low remaining quantities, alerting the user. This allows the notification unit to provide information to users in diverse ways, reducing food waste. Additionally, the notification unit can customize the frequency and content of notifications according to user settings, enabling flexible responses tailored to individual needs.

[0033] The provision unit provides information about ingredients based on the data analyzed by the analysis unit. For example, the provision unit provides nutritional information about ingredients. Specifically, it displays detailed nutritional information such as calories, protein, fat, and carbohydrates for each ingredient, providing reference information to help users maintain a healthy diet. The provision unit can also provide information on how to store ingredients. For example, it advises users on the best storage methods for ingredients that are suitable for refrigeration or those that require freezing. Furthermore, the provision unit can provide expiration dates for ingredients. Based on the expiration dates predicted by the analysis unit, it displays a list of expiration dates for each ingredient, supporting users in consuming ingredients efficiently. In this way, the provision unit can provide users with detailed information about ingredients and streamline ingredient management. In addition, the provision unit can accumulate historical data on users' ingredient management and provide advice and suggestions based on past data.

[0034] The suggestion unit proposes recipes based on information analyzed by the analysis unit. For example, the suggestion unit proposes recipes based on multiple ingredient names. Specifically, it searches for recipes that can be made by combining ingredients found in the refrigerator and proposes them to the user. The suggestion unit can also propose recipes based on the user's preferences. It learns from the user's past choices and evaluations and prioritizes suggesting recipes that match their preferences. Furthermore, the suggestion unit can also propose recipes appropriate for the season. For example, it can suggest cold or light dishes in the summer, and warm or stewed dishes in the winter. In this way, the suggestion unit can provide users with a variety of recipes, reduce food waste, and support a more fulfilling diet. In addition, the suggestion unit can propose recipes that take into account the user's allergy information and dietary restrictions, enabling personalized meal suggestions based on individual health conditions.

[0035] The camera unit can recognize the types and quantities of food items inside the refrigerator and collect data. The camera unit can identify food items using, for example, image recognition technology. The camera unit can also identify food items using, for example, barcode scanning technology. The camera unit can also measure the quantity of food items using, for example, sensor technology. This allows for an accurate understanding of the types and quantities of food items inside the refrigerator. Some or all of the above-described processes in the camera unit may be performed using, for example, AI, or not using AI. For example, the camera unit can input data acquired using image recognition technology into a generating AI, and have the generating AI perform the recognition of the types and quantities of food items.

[0036] The notification unit can notify users of ingredients that are nearing their expiration date or have only a small amount remaining. The notification unit can, for example, send push notifications to smartphones. The notification unit can also, for example, display information on the refrigerator's display. The notification unit can also, for example, send notifications using a voice assistant. This reduces waste by notifying users of ingredients that are nearing their expiration date or have only a small amount remaining. Some or all of the above-described processes in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input information analyzed by the analysis unit into a generation AI and have the generation AI generate the notification content.

[0037] The service provider can analyze photos of food items submitted by the user and tell the user how long the food items have been in the refrigerator. The service provider can analyze photos of food items using, for example, image analysis algorithms. The service provider can also obtain information about food items using, for example, database lookup technology. The service provider can also predict the shelf life of food items using, for example, machine learning algorithms. This allows the user to understand the shelf life of the food items. Some or all of the above processing in the service provider may be performed using, for example, AI, or not using AI. For example, the service provider can input photos of food items submitted by the user into a generating AI and have the generating AI perform the analysis of the shelf life.

[0038] The suggestion unit can suggest recipes based on multiple ingredient names. For example, the suggestion unit can receive multiple ingredient names as input and suggest recipes based on them. The suggestion unit can also suggest recipes based on user preferences. The suggestion unit can also suggest recipes appropriate for the season. This allows users to efficiently utilize the ingredients in their refrigerators. Some or all of the above-described processes in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input multiple ingredient names into a generation AI and have the generation AI generate recipe suggestions.

[0039] The camera unit can recognize the type and quantity of ingredients while simultaneously evaluating their freshness and quality. For example, the camera unit can analyze the color and shape of the ingredients to evaluate their freshness. For example, the camera unit can analyze the surface condition of the ingredients to evaluate their quality. For example, the camera unit can consider the storage period of the ingredients to comprehensively evaluate freshness and quality. This allows for more accurate ingredient management by evaluating the freshness and quality of the ingredients. Some or all of the above processing in the camera unit may be performed using AI, for example, or without AI. For example, the camera unit can input data on the color and shape of the ingredients into a generating AI and have the generating AI perform the evaluation of freshness and quality.

[0040] The camera unit automatically adjusts its position to photograph all food items inside the refrigerator at the optimal angle. For example, the camera unit analyzes the arrangement of food items inside the refrigerator and automatically sets the optimal shooting angle. For example, the camera unit considers the height and position of the shelves inside the refrigerator and adjusts the optimal shooting position. For example, the camera unit analyzes the lighting conditions inside the refrigerator and sets the optimal shooting conditions. This improves the accuracy of food identification by photographing all food items inside the refrigerator at the optimal angle. Some or all of the above processing in the camera unit may be performed using AI, for example, or without AI. For example, the camera unit can input data on the arrangement of food items inside the refrigerator into a generating AI and have the generating AI set the optimal shooting angle.

[0041] The camera unit can simultaneously monitor the temperature and humidity inside the refrigerator and evaluate the preservation status of food. For example, the camera unit can work in conjunction with the temperature sensor inside the refrigerator to evaluate the preservation status of food. For example, the camera unit can work in conjunction with the humidity sensor inside the refrigerator to evaluate the preservation status of food. For example, the camera unit can comprehensively analyze temperature and humidity data to evaluate the optimal preservation status of food. In this way, by monitoring the temperature and humidity inside the refrigerator, the preservation status of food can be accurately evaluated. Some or all of the above processing in the camera unit may be performed using AI, for example, or without AI. For example, the camera unit can input temperature and humidity data into a generating AI and have the generating AI perform the evaluation of the preservation status.

[0042] The camera unit can detect and notify of abnormalities inside the refrigerator (e.g., the door being left open). For example, the camera unit monitors the open / closed state of the refrigerator door and detects abnormalities. For example, the camera unit monitors temperature changes inside the refrigerator and detects if the door is left open. For example, the camera unit monitors the lighting status inside the refrigerator and detects if the door is left open. By detecting and notifying of abnormalities inside the refrigerator, it is possible to prevent food spoilage. Some or all of the above processing in the camera unit may be performed using AI, for example, or without AI. For example, the camera unit can input temperature change data inside the refrigerator into a generating AI and have the generating AI perform abnormality detection.

[0043] The analysis unit can analyze not only the type and quantity of ingredients, but also their nutritional value and calories. For example, the analysis unit can refer to a nutritional value database of ingredients and analyze their nutritional value. For example, the analysis unit can refer to a calorie database of ingredients and analyze their calories. For example, the analysis unit can analyze the overall nutritional value and calories based on the type and quantity of ingredients. By analyzing the nutritional value and calories of ingredients, the analysis unit can provide users with information to help them lead a healthy diet. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input nutritional value data of ingredients into a generating AI and have the generating AI perform the analysis of nutritional value and calories.

[0044] The analysis unit can propose the optimal storage method for food ingredients based on the analysis results. For example, the analysis unit can propose the optimal storage temperature based on the type and freshness of the food ingredients. For example, the analysis unit can propose the optimal storage humidity based on the type and quality of the food ingredients. For example, the analysis unit can propose the optimal storage method based on the storage period of the food ingredients. By proposing the optimal storage method for food ingredients, deterioration of the food ingredients can be prevented. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input food ingredient storage data into a generating AI and have the generating AI propose the optimal storage method.

[0045] The analysis unit can make suggestions to optimize the arrangement of food items in the refrigerator. For example, the analysis unit can suggest the optimal arrangement based on the type and quantity of food items. For example, the analysis unit can suggest the optimal arrangement based on the freshness and quality of the food items. For example, the analysis unit can suggest an arrangement that makes the most of the space in the refrigerator. By optimizing the arrangement of food items in the refrigerator, it becomes possible to make effective use of space. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input food arrangement data into a generating AI and have the generating AI make suggestions for the optimal arrangement.

[0046] The analysis unit can refer to the purchase history of ingredients and predict the timing of the next purchase. For example, the analysis unit predicts the timing of the next purchase based on the user's past purchase history. For example, the analysis unit predicts the timing of the next purchase by analyzing the rate of consumption of ingredients. For example, the analysis unit predicts the timing of the next purchase by analyzing the consumption pattern of ingredients. By predicting the timing of the next purchase, food waste can be reduced. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input purchase history data into a generating AI and have the generating AI perform the prediction of the timing of the next purchase.

[0047] The notification unit can include not only the expiration date of the ingredients but also the optimal method of consumption in its notifications. For example, the notification unit may suggest the optimal cooking method along with the expiration date of the ingredients. For example, the notification unit may suggest storage methods along with the expiration date of the ingredients. For example, the notification unit may suggest the optimal timing for consumption along with the expiration date of the ingredients. By suggesting methods of consumption, food waste can be reduced. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit may input expiration date data into a generating AI and have the generating AI perform the task of suggesting the optimal method of consumption.

[0048] The notification unit can also suggest substitutes or supplements for ingredients when it sends a notification. For example, the notification unit can suggest substitutes for ingredients that are nearing their expiration date. For example, the notification unit can suggest supplements for ingredients that are running low on stock. For example, the notification unit can suggest substitutes or supplements for ingredients and provide purchase links. This allows users to use ingredients more efficiently by suggesting substitutes and supplements. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input ingredient substitute data into a generating AI and have the generating AI suggest substitutes and supplements.

[0049] The notification unit can refer to the user's schedule and select the optimal notification timing. For example, the notification unit can refer to the user's calendar information and select the optimal notification timing. For example, the notification unit can analyze the user's schedule and adjust the notification timing. For example, the notification unit can optimize the notification timing to match the user's schedule. This improves user convenience by adjusting the notification timing to match the user's schedule. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input schedule data into a generating AI and have the generating AI select the optimal notification timing.

[0050] The notification unit can analyze the user's past consumption patterns and customize the optimal notification content. For example, the notification unit can suggest the optimal notification content based on the user's past consumption patterns. For example, the notification unit can analyze the user's consumption history and provide customized notification content. For example, the notification unit can analyze the user's consumption patterns and customize the optimal notification content. This improves user convenience by customizing notification content based on the user's consumption patterns. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input consumption pattern data into a generating AI and have the generating AI perform the customization of the optimal notification content.

[0051] The supply unit can suggest the optimal timing for consumption based on the storage condition and freshness of the ingredients. For example, the supply unit suggests the optimal timing for consumption based on the freshness of the ingredients. For example, the supply unit suggests the optimal timing for consumption based on the storage condition of the ingredients. For example, the supply unit suggests the optimal timing for consumption based on the expiration date of the ingredients. This reduces food waste by suggesting the optimal timing for consumption based on the storage condition and freshness of the ingredients. Some or all of the above processing in the supply unit may be performed using AI, for example, or without AI. For example, the supply unit can input storage condition data into a generating AI and have the generating AI suggest the optimal timing for consumption.

[0052] The information provider can also provide nutritional value and calorie information for ingredients. For example, the information provider can refer to a nutritional value database for ingredients and provide nutritional value information. For example, the information provider can refer to a calorie database for ingredients and provide calorie information. For example, the information provider can provide overall nutritional value and calorie information based on the type and quantity of ingredients. By providing nutritional value and calorie information for ingredients, users can obtain information to lead a healthy diet. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the information provider can input nutritional value data into a generating AI and have the generating AI perform the provision of nutritional value and calorie information.

[0053] The service provider can refer to the user's past consumption history and customize the most suitable information. For example, the service provider can suggest the most suitable information based on the user's past consumption history. For example, the service provider can analyze the user's consumption patterns and provide customized information. For example, the service provider can analyze the user's consumption history and customize the most suitable information. This improves user convenience by customizing information based on the user's consumption history. Some or all of the above processes in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input consumption history data into a generating AI and have the generating AI perform the customization of the most suitable information.

[0054] The service provider can also provide information on how to preserve and cook ingredients. For example, the service provider can suggest the optimal preservation method based on the type and freshness of the ingredients. For example, the service provider can suggest the optimal cooking method based on the type and quality of the ingredients. For example, the service provider can suggest the optimal preservation and cooking methods based on the shelf life of the ingredients. By providing information on how to preserve and cook ingredients, users can utilize ingredients efficiently. Some or all of the above-described processes in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input preservation method data into a generating AI and have the generating AI suggest the optimal preservation and cooking methods.

[0055] The suggestion unit can propose healthy recipes based on the nutritional value and calories of ingredients. For example, the suggestion unit can refer to a nutritional value database of ingredients and propose healthy recipes. For example, the suggestion unit can refer to a calorie database of ingredients and propose low-calorie recipes. For example, the suggestion unit can propose balanced recipes based on the type and quantity of ingredients. In this way, by proposing healthy recipes based on the nutritional value and calories of ingredients, users can obtain information to lead a healthy diet. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input nutritional data into a generating AI and have the generating AI produce healthy recipe suggestions.

[0056] The suggestion unit can refer to the user's past cooking history and suggest recipes that suit their preferences. For example, the suggestion unit suggests recipes that suit the user's preferences based on the user's past cooking history. For example, the suggestion unit analyzes the user's cooking patterns and provides customized recipes. For example, the suggestion unit analyzes the user's cooking history and customizes the optimal recipe. In this way, by suggesting recipes based on the user's past cooking history, it is possible to provide dishes that suit the user's preferences. Some or all of the above processes in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input cooking history data into a generating AI and have the generating AI suggest recipes that suit the user's preferences.

[0057] The suggestion unit can refer to the user's schedule and suggest recipes based on cooking time. For example, the suggestion unit can refer to the user's calendar information and suggest recipes based on cooking time. For example, the suggestion unit can analyze the user's schedule and suggest a recipe with the optimal cooking time. For example, the suggestion unit can suggest a recipe with optimized cooking time to match the user's schedule. This improves user convenience by suggesting recipes that match the user's schedule. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input schedule data into a generating AI and have the generating AI perform the task of suggesting recipes based on cooking time.

[0058] The suggestion unit can propose the optimal recipe based on the storage condition and freshness of the ingredients. For example, the suggestion unit can propose the optimal recipe based on the freshness of the ingredients. For example, the suggestion unit can propose the optimal recipe based on the storage condition of the ingredients. For example, the suggestion unit can propose the optimal recipe based on the expiration date of the ingredients. This reduces food waste by suggesting the optimal recipe based on the storage condition and freshness of the ingredients. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input storage condition data into a generating AI and have the generating AI propose the optimal recipe.

[0059] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0060] The refrigerator food management system can further analyze the nutritional value of ingredients and suggest foods based on the user's health condition. For example, if a user is on a diet, it will prioritize suggesting low-calorie foods. It can also suggest foods rich in specific nutrients if the user needs to consume those nutrients. Furthermore, it can suggest adjusting food intake based on the user's health condition. This allows users to select foods appropriate for their health and maintain a healthy diet.

[0061] The refrigerator food management system can monitor the storage condition of food in real time and notify users if the condition deteriorates. For example, if the temperature inside the refrigerator is not appropriate, it will notify the user to adjust the temperature. It can also notify users to consume food as soon as possible if its freshness has decreased. Furthermore, it can automatically remove food that has deteriorated from the list and suggest alternatives. This reduces food waste and ensures that fresh food is always available.

[0062] The refrigerator food management system can predict the next purchase timing based on the user's purchase history and notify them accordingly. For example, if the user's regularly purchased food items are running low, the system can notify them of the next purchase timing. It can also analyze the consumption rate of specific food items and suggest the appropriate timing for purchase. Furthermore, it can analyze the user's past purchase patterns to improve prediction accuracy. As a result, users can purchase the necessary food items at the right time, reducing food waste.

[0063] A refrigerator food management system can analyze how food is stored and suggest the optimal storage method. For example, it can suggest the optimal temperature and humidity for specific foods. It can also suggest the selection of storage containers according to the type of food. Furthermore, it can suggest specific methods for extending the shelf life. This optimizes the storage conditions of food and prevents spoilage.

[0064] The refrigerator food management system can monitor the freshness of ingredients in real time and suggest substitutes when freshness deteriorates. For example, if a particular ingredient loses its freshness, it can suggest a substitute. It can also suggest recipes to help consume the spoiled ingredients quickly. Furthermore, it can provide purchase links for substitutes, making it easy for users to buy them. This reduces food waste and ensures that fresh ingredients are always available.

[0065] The following briefly describes the processing flow for example form 1.

[0066] Step 1: The camera unit identifies the food items inside the refrigerator. For example, the camera unit recognizes the type and quantity of food items inside the refrigerator and collects data. The camera unit can also identify food items using image recognition technology or barcode scanning technology, and measure the quantity of food items using sensor technology. Step 2: The analysis unit analyzes the information collected by the camera unit. The analysis unit can analyze the type and quantity of ingredients using image analysis algorithms and database referencing technology, and can also predict the expiration date of the ingredients using machine learning algorithms. Step 3: The notification unit notifies users of the expiration date and remaining quantity based on the information analyzed by the analysis unit. The notification unit can send push notifications to smartphones, display information on the refrigerator's display, or use voice assistants. Step 4: The supply unit provides information about the ingredients based on the information analyzed by the analysis unit. The supply unit can provide nutritional information, storage instructions, and expiration date for the ingredients. Step 5: The suggestion unit proposes recipes based on the information analyzed by the analysis unit. The suggestion unit can propose recipes based on multiple ingredient names and can suggest recipes that suit the user's preferences and the season.

[0067] (Example of form 2) An embodiment of the present invention describes a refrigerator food management system that uses a device such as a small camera installed inside the refrigerator to allow an AI camera to monitor all the food items inside the refrigerator. The refrigerator food management system utilizes a dedicated smartphone app equipped with conversational AI to not only check the camera feed, but also notify users of food items nearing their expiration date or with low remaining quantities, and can even tell users how long items have been in their refrigerators if they send a photo of the items. It can also provide recipes if multiple food names are sent. For example, the refrigerator food management system installs a small camera inside the refrigerator. This camera uses AI to monitor all the food items inside the refrigerator. The AI ​​camera recognizes the types and quantities of food items inside the refrigerator and collects data. For example, it identifies vegetables, fruits, meats, etc., inside the refrigerator and determines their expiration dates and remaining quantities. Next, the refrigerator food management system allows users to check the status of food items inside the refrigerator using a dedicated smartphone app. The app is equipped with conversational AI, and when the user asks a question, the AI ​​answers. For example, if the user asks, "What are the expiration dates of the food items in the refrigerator?", the AI ​​will notify them of food items nearing their expiration date. Furthermore, if you ask, "How much of the food is left in the refrigerator?", the AI ​​will notify you of any ingredients that are running low. In addition, if you send a photo of the contents of your refrigerator to the app, the AI ​​will tell you how long that ingredient has been in the refrigerator. For example, if you send a photo of milk in your refrigerator, the AI ​​will notify you that "this milk has been in the refrigerator for 3 days." Also, if you send the names of multiple ingredients to the app, the AI ​​will suggest recipes using those ingredients. For example, if you ask, "What are some recipes using tomatoes and cheese?", the AI ​​will suggest recipes such as "tomato and cheese salad" or "tomato and cheese pasta." This system makes it easy to manage the ingredients in your refrigerator and reduces waste of ingredients that are nearing their expiration date. It also allows users to use the ingredients in their refrigerator efficiently and reduce food waste. Moreover, by using conversational AI, users can easily understand the status of the ingredients in their refrigerator and get cooking ideas. In summary, this refrigerator ingredient management system allows for efficient management of ingredients in the refrigerator and reduces waste of ingredients that are nearing their expiration date.

[0068] The refrigerator food management system according to this embodiment comprises a camera unit, an analysis unit, a notification unit, a supply unit, and a suggestion unit. The camera unit grasps the food inside the refrigerator. The camera unit recognizes, for example, the type and quantity of food inside the refrigerator and collects data. The camera unit identifies food using, for example, image recognition technology. The camera unit can also identify food using barcode scanning technology. Furthermore, the camera unit can measure the quantity of food using sensor technology. The analysis unit analyzes the information collected by the camera unit. The analysis unit analyzes the type and quantity of food using, for example, an image analysis algorithm. The analysis unit can also obtain information about food using database referencing technology. Furthermore, the analysis unit can predict the expiration date of food using a machine learning algorithm. The notification unit notifies the expiration date and remaining quantity based on the information analyzed by the analysis unit. The notification unit makes, for example, a push notification to a smartphone. The notification unit can also display the information on the refrigerator's display. Furthermore, the notification unit can also make notifications using a voice assistant. The supply unit provides information about food based on the information analyzed by the analysis unit. The provision unit provides, for example, nutritional information about ingredients. The provision unit can also provide information on how to store ingredients. Furthermore, the provision unit can also provide information on the expiration date of ingredients. The suggestion unit proposes recipes based on the information analyzed by the analysis unit. The suggestion unit proposes recipes based on, for example, the names of multiple ingredients. Furthermore, the suggestion unit can also propose recipes based on the user's preferences. Furthermore, the suggestion unit can also propose recipes appropriate for the season. As a result, the refrigerator ingredient management system according to this embodiment can efficiently manage ingredients in the refrigerator and reduce waste of ingredients nearing their expiration date.

[0069] The camera unit monitors the contents of the refrigerator. For example, the camera unit recognizes the type and quantity of food inside the refrigerator and collects data. Specifically, the camera unit places multiple cameras inside the refrigerator, and each camera photographs the food from a different angle to grasp the overall picture of the food. This allows for accurate recognition of the location and arrangement of food inside the refrigerator. The camera unit identifies food using image recognition technology. For example, it applies an image recognition algorithm using deep learning to identify food based on its shape, color, and packaging design. The camera unit can also identify food using barcode scanning technology. A barcode reader installed inside the refrigerator scans the barcode of the food and compares it with a database to obtain detailed information about the food. Furthermore, the camera unit can measure the quantity of food using sensor technology. For example, by combining weight sensors and volume sensors, it can accurately measure the remaining amount of food and collect the data. As a result, the camera unit can understand the type and quantity of food inside the refrigerator in detail and support efficient management.

[0070] The analysis unit analyzes the information collected by the camera unit. For example, the analysis unit uses image analysis algorithms to analyze the type and quantity of food items. Specifically, it preprocesses the collected image data, performs noise reduction and image correction, and then classifies the type of food item using a deep learning model. The analysis unit can also obtain information about food items using database referencing technology. For example, it can obtain detailed information and nutritional components of food items from online databases based on the barcode information of the food items. Furthermore, the analysis unit can predict the expiration date of food items using machine learning algorithms. Using a model that has learned past consumption data and the characteristics of food items, it predicts the expiration date of each food item with high accuracy and notifies the user. In this way, the analysis unit can analyze the collected data from multiple angles and make food item management in the refrigerator more efficient. Furthermore, the analysis unit can analyze food item consumption patterns and usage frequency, and provide information that is useful for improving the user's purchasing behavior and dietary habits.

[0071] The notification unit notifies users of expiration dates and remaining quantities based on information analyzed by the analysis unit. For example, the notification unit sends push notifications to smartphones. Specifically, it notifies users in real time about ingredients nearing their expiration date or with low remaining quantities through a dedicated application. The notification unit can also display information on the refrigerator's display. Information such as expiration dates, remaining quantities, and storage methods for ingredients can be displayed on a screen on the refrigerator door, allowing users to check this information each time they open the refrigerator. Furthermore, the notification unit can also use a voice assistant for notifications. For example, a voice assistant built into the refrigerator can provide voice notifications about ingredients nearing their expiration date or with low remaining quantities, alerting the user. This allows the notification unit to provide information to users in diverse ways, reducing food waste. Additionally, the notification unit can customize the frequency and content of notifications according to user settings, enabling flexible responses tailored to individual needs.

[0072] The provision unit provides information about ingredients based on the data analyzed by the analysis unit. For example, the provision unit provides nutritional information about ingredients. Specifically, it displays detailed nutritional information such as calories, protein, fat, and carbohydrates for each ingredient, providing reference information to help users maintain a healthy diet. The provision unit can also provide information on how to store ingredients. For example, it advises users on the best storage methods for ingredients that are suitable for refrigeration or those that require freezing. Furthermore, the provision unit can provide expiration dates for ingredients. Based on the expiration dates predicted by the analysis unit, it displays a list of expiration dates for each ingredient, supporting users in consuming ingredients efficiently. In this way, the provision unit can provide users with detailed information about ingredients and streamline ingredient management. In addition, the provision unit can accumulate historical data on users' ingredient management and provide advice and suggestions based on past data.

[0073] The suggestion unit proposes recipes based on information analyzed by the analysis unit. For example, the suggestion unit proposes recipes based on multiple ingredient names. Specifically, it searches for recipes that can be made by combining ingredients found in the refrigerator and proposes them to the user. The suggestion unit can also propose recipes based on the user's preferences. It learns from the user's past choices and evaluations and prioritizes suggesting recipes that match their preferences. Furthermore, the suggestion unit can also propose recipes appropriate for the season. For example, it can suggest cold or light dishes in the summer, and warm or stewed dishes in the winter. In this way, the suggestion unit can provide users with a variety of recipes, reduce food waste, and support a more fulfilling diet. In addition, the suggestion unit can propose recipes that take into account the user's allergy information and dietary restrictions, enabling personalized meal suggestions based on individual health conditions.

[0074] The camera unit can recognize the types and quantities of food items inside the refrigerator and collect data. The camera unit can identify food items using, for example, image recognition technology. The camera unit can also identify food items using, for example, barcode scanning technology. The camera unit can also measure the quantity of food items using, for example, sensor technology. This allows for an accurate understanding of the types and quantities of food items inside the refrigerator. Some or all of the above-described processes in the camera unit may be performed using, for example, AI, or not using AI. For example, the camera unit can input data acquired using image recognition technology into a generating AI, and have the generating AI perform the recognition of the types and quantities of food items.

[0075] The notification unit can notify users of ingredients that are nearing their expiration date or have only a small amount remaining. The notification unit can, for example, send push notifications to smartphones. The notification unit can also, for example, display information on the refrigerator's display. The notification unit can also, for example, send notifications using a voice assistant. This reduces waste by notifying users of ingredients that are nearing their expiration date or have only a small amount remaining. Some or all of the above-described processes in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input information analyzed by the analysis unit into a generation AI and have the generation AI generate the notification content.

[0076] The service provider can analyze photos of food items submitted by the user and tell the user how long the food items have been in the refrigerator. The service provider can analyze photos of food items using, for example, image analysis algorithms. The service provider can also obtain information about food items using, for example, database lookup technology. The service provider can also predict the shelf life of food items using, for example, machine learning algorithms. This allows the user to understand the shelf life of the food items. Some or all of the above processing in the service provider may be performed using, for example, AI, or not using AI. For example, the service provider can input photos of food items submitted by the user into a generating AI and have the generating AI perform the analysis of the shelf life.

[0077] The suggestion unit can suggest recipes based on multiple ingredient names. For example, the suggestion unit can receive multiple ingredient names as input and suggest recipes based on them. The suggestion unit can also suggest recipes based on user preferences. The suggestion unit can also suggest recipes appropriate for the season. This allows users to efficiently utilize the ingredients in their refrigerators. Some or all of the above-described processes in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input multiple ingredient names into a generation AI and have the generation AI generate recipe suggestions.

[0078] The camera unit can estimate the user's emotions and adjust the camera's shooting frequency based on the estimated emotions. For example, if the user is stressed, the camera unit can lower the camera's shooting frequency and reduce the frequency of notifications. For example, if the user is relaxed, the camera unit can raise the camera's shooting frequency and provide detailed information. For example, if the user is in a hurry, the camera unit can optimize the camera's shooting frequency and quickly provide only the necessary information. In this way, by adjusting the camera's shooting frequency according to the user's emotions, user stress can be reduced. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the camera unit may be performed using AI, for example, or without AI. For example, the camera unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0079] The camera unit can recognize the type and quantity of ingredients while simultaneously evaluating their freshness and quality. For example, the camera unit can analyze the color and shape of the ingredients to evaluate their freshness. For example, the camera unit can analyze the surface condition of the ingredients to evaluate their quality. For example, the camera unit can consider the storage period of the ingredients to comprehensively evaluate freshness and quality. This allows for more accurate ingredient management by evaluating the freshness and quality of the ingredients. Some or all of the above processing in the camera unit may be performed using AI, for example, or without AI. For example, the camera unit can input data on the color and shape of the ingredients into a generating AI and have the generating AI perform the evaluation of freshness and quality.

[0080] The camera unit automatically adjusts its position to photograph all food items inside the refrigerator at the optimal angle. For example, the camera unit analyzes the arrangement of food items inside the refrigerator and automatically sets the optimal shooting angle. For example, the camera unit considers the height and position of the shelves inside the refrigerator and adjusts the optimal shooting position. For example, the camera unit analyzes the lighting conditions inside the refrigerator and sets the optimal shooting conditions. This improves the accuracy of food identification by photographing all food items inside the refrigerator at the optimal angle. Some or all of the above processing in the camera unit may be performed using AI, for example, or without AI. For example, the camera unit can input data on the arrangement of food items inside the refrigerator into a generating AI and have the generating AI set the optimal shooting angle.

[0081] The camera unit can estimate the user's emotions and determine the timing of camera captures based on the estimated emotions. For example, if the user is stressed, the camera unit will reduce the timing of captures and lower the frequency of notifications. For example, if the user is relaxed, the camera unit will increase the timing of captures and provide more detailed information. For example, if the user is in a hurry, the camera unit will optimize the timing of captures and quickly provide only the necessary information. In this way, by adjusting the timing of camera captures according to the user's emotions, user stress can be reduced. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the camera unit may be performed using AI, for example, or without AI. For example, the camera unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0082] The camera unit can simultaneously monitor the temperature and humidity inside the refrigerator and evaluate the preservation status of food. For example, the camera unit can work in conjunction with the temperature sensor inside the refrigerator to evaluate the preservation status of food. For example, the camera unit can work in conjunction with the humidity sensor inside the refrigerator to evaluate the preservation status of food. For example, the camera unit can comprehensively analyze temperature and humidity data to evaluate the optimal preservation status of food. In this way, by monitoring the temperature and humidity inside the refrigerator, the preservation status of food can be accurately evaluated. Some or all of the above processing in the camera unit may be performed using AI, for example, or without AI. For example, the camera unit can input temperature and humidity data into a generating AI and have the generating AI perform the evaluation of the preservation status.

[0083] The camera unit can detect and notify of abnormalities inside the refrigerator (e.g., the door being left open). For example, the camera unit monitors the open / closed state of the refrigerator door and detects abnormalities. For example, the camera unit monitors temperature changes inside the refrigerator and detects if the door is left open. For example, the camera unit monitors the lighting status inside the refrigerator and detects if the door is left open. By detecting and notifying of abnormalities inside the refrigerator, it is possible to prevent food spoilage. Some or all of the above processing in the camera unit may be performed using AI, for example, or without AI. For example, the camera unit can input temperature change data inside the refrigerator into a generating AI and have the generating AI perform abnormality detection.

[0084] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is tense, the analysis unit provides a simple and highly visible display method. For example, if the user is relaxed, the analysis unit provides a display method that includes detailed information. For example, if the user is in a hurry, the analysis unit provides a display method that gets straight to the point. In this way, by adjusting the display method of the analysis results according to the user's emotions, the user's stress can be reduced. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's emotion data into a generative AI and have the generative AI perform emotion estimation.

[0085] The analysis unit can analyze not only the type and quantity of ingredients, but also their nutritional value and calories. For example, the analysis unit can refer to a nutritional value database of ingredients and analyze their nutritional value. For example, the analysis unit can refer to a calorie database of ingredients and analyze their calories. For example, the analysis unit can analyze the overall nutritional value and calories based on the type and quantity of ingredients. By analyzing the nutritional value and calories of ingredients, the analysis unit can provide users with information to help them lead a healthy diet. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input nutritional value data of ingredients into a generating AI and have the generating AI perform the analysis of nutritional value and calories.

[0086] The analysis unit can propose the optimal storage method for food ingredients based on the analysis results. For example, the analysis unit can propose the optimal storage temperature based on the type and freshness of the food ingredients. For example, the analysis unit can propose the optimal storage humidity based on the type and quality of the food ingredients. For example, the analysis unit can propose the optimal storage method based on the storage period of the food ingredients. By proposing the optimal storage method for food ingredients, deterioration of the food ingredients can be prevented. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input food ingredient storage data into a generating AI and have the generating AI propose the optimal storage method.

[0087] The analysis unit can estimate the user's emotions and determine the priority of analysis based on the estimated emotions. For example, if the user is stressed, the analysis unit will prioritize displaying important analysis results. For example, if the user is relaxed, the analysis unit will display detailed analysis results. For example, if the user is in a hurry, the analysis unit will quickly display the necessary analysis results. This reduces user stress by prioritizing analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0088] The analysis unit can make suggestions to optimize the arrangement of food items in the refrigerator. For example, the analysis unit can suggest the optimal arrangement based on the type and quantity of food items. For example, the analysis unit can suggest the optimal arrangement based on the freshness and quality of the food items. For example, the analysis unit can suggest an arrangement that makes the most of the space in the refrigerator. By optimizing the arrangement of food items in the refrigerator, it becomes possible to make effective use of space. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input food arrangement data into a generating AI and have the generating AI make suggestions for the optimal arrangement.

[0089] The analysis unit can refer to the purchase history of ingredients and predict the timing of the next purchase. For example, the analysis unit predicts the timing of the next purchase based on the user's past purchase history. For example, the analysis unit predicts the timing of the next purchase by analyzing the rate of consumption of ingredients. For example, the analysis unit predicts the timing of the next purchase by analyzing the consumption pattern of ingredients. By predicting the timing of the next purchase, food waste can be reduced. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input purchase history data into a generating AI and have the generating AI perform the prediction of the timing of the next purchase.

[0090] The notification unit can estimate the user's emotions and adjust the timing of notifications based on the estimated emotions. For example, if the user is stressed, the notification unit can reduce the frequency of notifications and only send important notifications. For example, if the user is relaxed, the notification unit can send detailed notifications frequently. For example, if the user is in a hurry, the notification unit can send necessary notifications quickly. In this way, by adjusting the timing of notifications according to the user's emotions, the user's stress can be reduced. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the notification unit may be performed using AI, for example, or not using AI. For example, the notification unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0091] The notification unit can include not only the expiration date of the ingredients but also the optimal method of consumption in its notifications. For example, the notification unit may suggest the optimal cooking method along with the expiration date of the ingredients. For example, the notification unit may suggest storage methods along with the expiration date of the ingredients. For example, the notification unit may suggest the optimal timing for consumption along with the expiration date of the ingredients. By suggesting methods of consumption, food waste can be reduced. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit may input expiration date data into a generating AI and have the generating AI perform the task of suggesting the optimal method of consumption.

[0092] The notification unit can also suggest substitutes or supplements for ingredients when it sends a notification. For example, the notification unit can suggest substitutes for ingredients that are nearing their expiration date. For example, the notification unit can suggest supplements for ingredients that are running low on stock. For example, the notification unit can suggest substitutes or supplements for ingredients and provide purchase links. This allows users to use ingredients more efficiently by suggesting substitutes and supplements. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input ingredient substitute data into a generating AI and have the generating AI suggest substitutes and supplements.

[0093] The notification unit can estimate the user's emotions and determine the priority of notifications based on the estimated emotions. For example, if the user is stressed, the notification unit will prioritize important notifications. For example, if the user is relaxed, the notification unit will provide detailed notifications. For example, if the user is in a hurry, the notification unit will provide necessary notifications quickly. This reduces user stress by prioritizing notifications according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the notification unit may be performed using AI or not using AI. For example, the notification unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0094] The notification unit can refer to the user's schedule and select the optimal notification timing. For example, the notification unit can refer to the user's calendar information and select the optimal notification timing. For example, the notification unit can analyze the user's schedule and adjust the notification timing. For example, the notification unit can optimize the notification timing to match the user's schedule. This improves user convenience by adjusting the notification timing to match the user's schedule. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input schedule data into a generating AI and have the generating AI select the optimal notification timing.

[0095] The notification unit can analyze the user's past consumption patterns and customize the optimal notification content. For example, the notification unit can suggest the optimal notification content based on the user's past consumption patterns. For example, the notification unit can analyze the user's consumption history and provide customized notification content. For example, the notification unit can analyze the user's consumption patterns and customize the optimal notification content. This improves user convenience by customizing notification content based on the user's consumption patterns. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input consumption pattern data into a generating AI and have the generating AI perform the customization of the optimal notification content.

[0096] The service provider can estimate the user's emotions and adjust the level of detail of the information provided based on the estimated emotions. For example, if the user is stressed, the service provider provides simple and easily visible information. For example, if the user is relaxed, the service provider provides detailed information. For example, if the user is in a hurry, the service provider provides concise information. By adjusting the level of detail of information according to the user's emotions, the service provider can reduce the user's stress. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not using AI. For example, the service provider can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0097] The supply unit can suggest the optimal timing for consumption based on the storage condition and freshness of the ingredients. For example, the supply unit suggests the optimal timing for consumption based on the freshness of the ingredients. For example, the supply unit suggests the optimal timing for consumption based on the storage condition of the ingredients. For example, the supply unit suggests the optimal timing for consumption based on the expiration date of the ingredients. This reduces food waste by suggesting the optimal timing for consumption based on the storage condition and freshness of the ingredients. Some or all of the above processing in the supply unit may be performed using AI, for example, or without AI. For example, the supply unit can input storage condition data into a generating AI and have the generating AI suggest the optimal timing for consumption.

[0098] The information provider can also provide nutritional value and calorie information for ingredients. For example, the information provider can refer to a nutritional value database for ingredients and provide nutritional value information. For example, the information provider can refer to a calorie database for ingredients and provide calorie information. For example, the information provider can provide overall nutritional value and calorie information based on the type and quantity of ingredients. By providing nutritional value and calorie information for ingredients, users can obtain information to lead a healthy diet. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the information provider can input nutritional value data into a generating AI and have the generating AI perform the provision of nutritional value and calorie information.

[0099] The service provider can estimate the user's emotions and determine the priority of the information to be provided based on the estimated emotions. For example, if the user is stressed, the service provider will prioritize providing important information. For example, if the user is relaxed, the service provider will provide detailed information. For example, if the user is in a hurry, the service provider will quickly provide the necessary information. This reduces user stress by prioritizing information according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not using AI. For example, the service provider can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0100] The service provider can refer to the user's past consumption history and customize the most suitable information. For example, the service provider can suggest the most suitable information based on the user's past consumption history. For example, the service provider can analyze the user's consumption patterns and provide customized information. For example, the service provider can analyze the user's consumption history and customize the most suitable information. This improves user convenience by customizing information based on the user's consumption history. Some or all of the above processes in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input consumption history data into a generating AI and have the generating AI perform the customization of the most suitable information.

[0101] The service provider can also provide information on how to preserve and cook ingredients. For example, the service provider can suggest the optimal preservation method based on the type and freshness of the ingredients. For example, the service provider can suggest the optimal cooking method based on the type and quality of the ingredients. For example, the service provider can suggest the optimal preservation and cooking methods based on the shelf life of the ingredients. By providing information on how to preserve and cook ingredients, users can utilize ingredients efficiently. Some or all of the above-described processes in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input preservation method data into a generating AI and have the generating AI suggest the optimal preservation and cooking methods.

[0102] The suggestion unit can estimate the user's emotions and adjust the type of recipe it suggests based on the estimated emotions. For example, if the user is relaxed, the suggestion unit suggests a recipe for a time-consuming dish. If the user is in a hurry, the suggestion unit suggests a recipe that can be made quickly. If the user is stressed, the suggestion unit suggests a simple and easy recipe. By adjusting the type of recipe according to the user's emotions, the user's stress can be reduced. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not using AI. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0103] The suggestion unit can propose healthy recipes based on the nutritional value and calories of ingredients. For example, the suggestion unit can refer to a nutritional value database of ingredients and propose healthy recipes. For example, the suggestion unit can refer to a calorie database of ingredients and propose low-calorie recipes. For example, the suggestion unit can propose balanced recipes based on the type and quantity of ingredients. In this way, by proposing healthy recipes based on the nutritional value and calories of ingredients, users can obtain information to lead a healthy diet. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input nutritional data into a generating AI and have the generating AI produce healthy recipe suggestions.

[0104] The suggestion unit can refer to the user's past cooking history and suggest recipes that suit their preferences. For example, the suggestion unit suggests recipes that suit the user's preferences based on the user's past cooking history. For example, the suggestion unit analyzes the user's cooking patterns and provides customized recipes. For example, the suggestion unit analyzes the user's cooking history and customizes the optimal recipe. In this way, by suggesting recipes based on the user's past cooking history, it is possible to provide dishes that suit the user's preferences. Some or all of the above processes in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input cooking history data into a generating AI and have the generating AI suggest recipes that suit the user's preferences.

[0105] The suggestion unit can estimate the user's emotions and determine the priority of suggested recipes based on the estimated emotions. For example, if the user is stressed, the suggestion unit will prioritize simple and easy-to-make recipes. If the user is relaxed, the suggestion unit will suggest recipes that require more time. If the user is in a hurry, the suggestion unit will prioritize recipes that can be made quickly. By prioritizing recipes according to the user's emotions, the user's stress can be reduced. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0106] The suggestion unit can refer to the user's schedule and suggest recipes based on cooking time. For example, the suggestion unit can refer to the user's calendar information and suggest recipes based on cooking time. For example, the suggestion unit can analyze the user's schedule and suggest a recipe with the optimal cooking time. For example, the suggestion unit can suggest a recipe with optimized cooking time to match the user's schedule. This improves user convenience by suggesting recipes that match the user's schedule. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input schedule data into a generating AI and have the generating AI perform the task of suggesting recipes based on cooking time.

[0107] The suggestion unit can propose the optimal recipe based on the storage condition and freshness of the ingredients. For example, the suggestion unit can propose the optimal recipe based on the freshness of the ingredients. For example, the suggestion unit can propose the optimal recipe based on the storage condition of the ingredients. For example, the suggestion unit can propose the optimal recipe based on the expiration date of the ingredients. This reduces food waste by suggesting the optimal recipe based on the storage condition and freshness of the ingredients. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input storage condition data into a generating AI and have the generating AI propose the optimal recipe.

[0108] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0109] The refrigerator food management system can further analyze the nutritional value of ingredients and suggest foods based on the user's health condition. For example, if a user is on a diet, it will prioritize suggesting low-calorie foods. It can also suggest foods rich in specific nutrients if the user needs to consume those nutrients. Furthermore, it can suggest adjusting food intake based on the user's health condition. This allows users to select foods appropriate for their health and maintain a healthy diet.

[0110] The refrigerator food management system can estimate the user's emotions and suggest food items based on those emotions. For example, if the user is stressed, it can suggest foods that have a relaxing effect. If the user is tired, it can suggest foods suitable for replenishing energy. Furthermore, if the user is happy, it can suggest foods suitable for special dishes. By suggesting foods that match the user's emotions, this system can improve user satisfaction.

[0111] The refrigerator food management system can monitor the storage condition of food in real time and notify users if the condition deteriorates. For example, if the temperature inside the refrigerator is not appropriate, it will notify the user to adjust the temperature. It can also notify users to consume food as soon as possible if its freshness has decreased. Furthermore, it can automatically remove food that has deteriorated from the list and suggest alternatives. This reduces food waste and ensures that fresh food is always available.

[0112] The refrigerator food management system can estimate the user's emotions and customize notifications based on those emotions. For example, if the user is stressed, it will only send concise and essential information. If the user is relaxed, it can send notifications with more detailed information. Furthermore, if the user is in a hurry, it can prioritize notifications for information requiring immediate attention. By providing notifications tailored to the user's emotions, it can reduce user stress and improve convenience.

[0113] The refrigerator food management system can predict the next purchase timing based on the user's purchase history and notify them accordingly. For example, if the user's regularly purchased food items are running low, the system can notify them of the next purchase timing. It can also analyze the consumption rate of specific food items and suggest the appropriate timing for purchase. Furthermore, it can analyze the user's past purchase patterns to improve prediction accuracy. As a result, users can purchase the necessary food items at the right time, reducing food waste.

[0114] The refrigerator food management system can estimate the user's emotions and suggest recipes based on those emotions. For example, if the user is relaxed, it can suggest recipes that require more time to prepare. If the user is in a hurry, it can suggest recipes that can be made quickly. Furthermore, if the user is stressed, it can suggest easy and convenient recipes. By suggesting recipes that match the user's emotions, the system can improve the user's cooking experience.

[0115] A refrigerator food management system can analyze how food is stored and suggest the optimal storage method. For example, it can suggest the optimal temperature and humidity for specific foods. It can also suggest the selection of storage containers according to the type of food. Furthermore, it can suggest specific methods for extending the shelf life. This optimizes the storage conditions of food and prevents spoilage.

[0116] The refrigerator food management system can estimate the user's emotions and suggest ways to consume food based on those emotions. For example, if the user is relaxed, it can suggest time-consuming cooking methods. If the user is in a hurry, it can suggest simple and quick cooking methods. Furthermore, if the user is stressed, it can suggest easy-to-prepare dishes. By suggesting consumption methods that match the user's emotions, this system can improve user satisfaction.

[0117] The refrigerator food management system can monitor the freshness of ingredients in real time and suggest substitutes when freshness deteriorates. For example, if a particular ingredient loses its freshness, it can suggest a substitute. It can also suggest recipes to help consume the spoiled ingredients quickly. Furthermore, it can provide purchase links for substitutes, making it easy for users to buy them. This reduces food waste and ensures that fresh ingredients are always available.

[0118] The refrigerator food management system can estimate the user's emotions and evaluate the condition of food based on those emotions. For example, if the user is stressed, it can suggest consuming spoiled food as soon as possible. If the user is relaxed, it can suggest ways to preserve well-preserved food for longer periods. Furthermore, if the user is in a hurry, it can suggest ways to quickly consume spoiled food. By evaluating food condition in accordance with the user's emotions, it reduces food waste and allows for more efficient use.

[0119] The following briefly describes the processing flow for example form 2.

[0120] Step 1: The camera unit identifies the food items inside the refrigerator. For example, the camera unit recognizes the type and quantity of food items inside the refrigerator and collects data. The camera unit can also identify food items using image recognition technology or barcode scanning technology, and measure the quantity of food items using sensor technology. Step 2: The analysis unit analyzes the information collected by the camera unit. The analysis unit can analyze the type and quantity of ingredients using image analysis algorithms and database referencing technology, and can also predict the expiration date of the ingredients using machine learning algorithms. Step 3: The notification unit notifies users of the expiration date and remaining quantity based on the information analyzed by the analysis unit. The notification unit can send push notifications to smartphones, display information on the refrigerator's display, or use voice assistants. Step 4: The supply unit provides information about the ingredients based on the information analyzed by the analysis unit. The supply unit can provide nutritional information, storage instructions, and expiration date for the ingredients. Step 5: The suggestion unit proposes recipes based on the information analyzed by the analysis unit. The suggestion unit can propose recipes based on multiple ingredient names and can suggest recipes that suit the user's preferences and the season.

[0121] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0122] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0123] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0124] Each of the multiple elements described above, including the camera unit, analysis unit, notification unit, provision unit, and suggestion unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the camera unit is implemented by the camera 42 of the smart device 14 and identifies the food items in the refrigerator. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the information collected by the camera unit. The notification unit is implemented by the control unit 46A of the smart device 14 and notifies the user of the expiration date and remaining amount based on the analyzed information. The provision unit is implemented by the identification processing unit 290 of the data processing unit 12 and provides information about the food items. The suggestion unit is implemented by the control unit 46A of the smart device 14 and suggests a recipe based on the analyzed information. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0125] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0126] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0127] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0128] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0129] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0130] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0131] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0132] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0133] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0134] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0135] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0136] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0137] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0138] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0139] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0140] Each of the multiple elements described above, including the camera unit, analysis unit, notification unit, provision unit, and suggestion unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the camera unit is implemented by the camera 42 of the smart glasses 214 and identifies the food items in the refrigerator. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the information collected by the camera unit. The notification unit is implemented by the control unit 46A of the smart glasses 214 and notifies the user of the expiration date and remaining amount based on the analyzed information. The provision unit is implemented by the identification processing unit 290 of the data processing unit 12 and provides information about the food items. The suggestion unit is implemented by the control unit 46A of the smart glasses 214 and suggests a recipe based on the analyzed information. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0141] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0142] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0143] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0144] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0145] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0146] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0147] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0148] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0149] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0150] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0151] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0152] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0153] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0154] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0155] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0156] Each of the multiple elements described above, including the camera unit, analysis unit, notification unit, provision unit, and suggestion unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the camera unit is implemented by the camera 42 of the headset terminal 314 and identifies the food items in the refrigerator. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the information collected by the camera unit. The notification unit is implemented by the control unit 46A of the headset terminal 314 and notifies the user of the expiration date and remaining amount based on the analyzed information. The provision unit is implemented by the identification processing unit 290 of the data processing unit 12 and provides information about the food items. The suggestion unit is implemented by the control unit 46A of the headset terminal 314 and suggests a recipe based on the analyzed information. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0157] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0158] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0159] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0160] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0161] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0162] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0163] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0164] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0165] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0166] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0167] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0168] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0169] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0170] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0171] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0172] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0173] Each of the multiple elements described above, including the camera unit, analysis unit, notification unit, provision unit, and suggestion unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the camera unit is implemented by the camera 42 of the robot 414 and grasps the contents of the refrigerator. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the information collected by the camera unit. The notification unit is implemented by the control unit 46A of the robot 414 and notifies the expiration date and remaining quantity based on the analyzed information. The provision unit is implemented by the identification processing unit 290 of the data processing unit 12 and provides information about the contents. The suggestion unit is implemented by the control unit 46A of the robot 414 and suggests a recipe based on the analyzed information. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0174] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0175] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0176] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0177] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0178] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0179] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0180] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0181] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0182] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0183] 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.

[0184] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0185] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0186] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0187] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0188] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0189] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0190] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0191] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0192] (Note 1) A camera unit that keeps track of the food inside the refrigerator, An analysis unit that analyzes the information collected by the camera unit, A notification unit that notifies the expiration date and remaining amount based on the information analyzed by the aforementioned analysis unit, A providing unit that provides information on ingredients based on the information analyzed by the aforementioned analysis unit, The system includes a suggestion unit that proposes a recipe based on the information analyzed by the analysis unit. A system characterized by the following features. (Note 2) The aforementioned camera unit is It recognizes the types and quantities of food items inside the refrigerator and collects data. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned notification unit, Notify you of ingredients that are nearing their expiration date or have only a small amount remaining. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned supply unit is, The system analyzes photos of food items submitted by users and tells them how long those items have been in the refrigerator. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned proposal section is, Recipes are suggested based on the names of multiple ingredients. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned camera unit is It estimates the user's emotions and adjusts the camera's shooting frequency based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned camera unit is When recognizing the type and quantity of ingredients, the freshness and quality of the ingredients are also evaluated simultaneously. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned camera unit is The camera automatically adjusts its position to capture all the food items inside the refrigerator at the optimal angle. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned camera unit is The system estimates the user's emotions and determines the camera's timing for taking a picture based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned camera unit is The temperature and humidity inside the refrigerator are also monitored simultaneously to evaluate the preservation status of the food. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned camera unit is Detects and notifies of abnormalities inside the refrigerator. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, It analyzes not only the types and quantities of ingredients, but also their nutritional value and calories. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, Based on the analysis results, we propose the optimal method for preserving food ingredients. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, The system estimates the user's emotions and determines the priority of analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, We offer suggestions for optimizing the placement of food items inside the refrigerator. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, Refer to your grocery purchase history to predict when you should buy next. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned notification unit, It estimates the user's emotions and adjusts the timing of notifications based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned notification unit, The notification should include not only the expiration date of the ingredients, but also the best way to consume them. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned notification unit, When notifying, suggestions for substitutes or supplements for ingredients will also be provided. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned notification unit, It estimates the user's emotions and prioritizes notifications based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned notification unit, Refer to the user's schedule and select the optimal notification timing. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned notification unit, Analyze the user's past consumption patterns and customize the most suitable notifications. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned supply unit is, It estimates the user's emotions and adjusts the level of detail of the information provided based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is, We suggest the optimal timing for consumption based on the storage conditions and freshness of the ingredients. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned supply unit is, We also provide nutritional value and calorie information for ingredients. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned supply unit is, It estimates the user's emotions and prioritizes the information provided based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned supply unit is, Referencing the user's past consumption history, we customize the information to best suit their needs. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned supply unit is, We also provide information on how to store and cook ingredients. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned proposal section is, It estimates the user's emotions and adjusts the types of recipes suggested based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned proposal section is, We propose healthy recipes based on the nutritional value and calorie content of the ingredients. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned proposal section is, It refers to the user's past cooking history and suggests recipes that suit their preferences. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned proposal section is, It estimates the user's emotions and prioritizes the suggested recipes based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned proposal section is, It refers to the user's schedule and suggests recipes that match the cooking time. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned proposal section is, We suggest the optimal recipe based on the storage conditions and freshness of the ingredients. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0193] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A camera unit that keeps track of the food inside the refrigerator, An analysis unit that analyzes the information collected by the camera unit, A notification unit that notifies the expiration date and remaining amount based on the information analyzed by the aforementioned analysis unit, A providing unit that provides information on ingredients based on the information analyzed by the aforementioned analysis unit, The system includes a suggestion unit that proposes a recipe based on the information analyzed by the analysis unit. A system characterized by the following features.

2. The aforementioned camera unit is It recognizes the types and quantities of food items inside the refrigerator and collects data. The system according to feature 1.

3. The aforementioned notification unit, Notify you of ingredients that are nearing their expiration date or have only a small amount remaining. The system according to feature 1.

4. The aforementioned supply unit is, The system analyzes photos of food items submitted by users and tells them how long those items have been in the refrigerator. The system according to feature 1.

5. The aforementioned proposal section is, Recipes are suggested based on the names of multiple ingredients. The system according to feature 1.

6. The aforementioned camera unit is It estimates the user's emotions and adjusts the camera's shooting frequency based on the estimated emotions. The system according to feature 1.

7. The aforementioned camera unit is When recognizing the type and quantity of ingredients, the freshness and quality of the ingredients are also evaluated simultaneously. The system according to feature 1.

8. The aforementioned camera unit is The camera automatically adjusts its position to capture all the food items inside the refrigerator at the optimal angle. The system according to feature 1.

9. The aforementioned camera unit is The system estimates the user's emotions and determines the camera's timing for taking a picture based on those emotions. The system according to feature 1.

10. The aforementioned camera unit is The temperature and humidity inside the refrigerator are also monitored simultaneously to evaluate the preservation status of the food. The system according to feature 1.

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