system

Abstract

The system according to this embodiment aims to automate everything from managing ingredients in the refrigerator to suggesting recipes, recommending suppliers, and placing orders. [Solution] The system according to the embodiment comprises a collection unit, a suggestion unit, a confirmation unit, a recommendation unit, and an order unit. The collection unit automatically scans and manages the food items in the refrigerator. The suggestion unit suggests recipes that can be used in order of the items with the nearest expiration date, based on the food item information collected by the collection unit. The confirmation unit remotely checks the status of the food items in the refrigerator based on the recipes suggested by the suggestion unit. The recommendation unit recommends a place to buy and products based on the food item information confirmed by the confirmation unit. The order unit automatically orders the products recommended by the recommendation unit according to the user's purchase intention.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that management of food ingredients in a refrigerator, grasping of expiration dates, proposal of optimal recipes, recommendation of purchase places, etc. are performed manually and are not efficient.

[0005] The system according to the embodiment aims to automate from management of food ingredients in a refrigerator to recipe proposal, recommendation of purchase places, and ordering.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, a suggestion unit, a confirmation unit, a recommendation unit, and an order unit. The collection unit automatically scans and manages the food items in the refrigerator. The suggestion unit suggests recipes that can be used in order of the items with the nearest expiration date, based on the food item information collected by the collection unit. The confirmation unit remotely checks the status of the food items in the refrigerator based on the recipes suggested by the suggestion unit. The recommendation unit recommends a place to buy and products based on the food item information confirmed by the confirmation unit. The order unit automatically orders the products recommended by the recommendation unit according to the user's purchase intention. [Effects of the Invention]

[0007] The system according to this embodiment can automate everything from managing ingredients in the refrigerator to suggesting recipes, recommending suppliers, and placing orders. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The refrigerator management system according to an embodiment of the present invention is a system that automatically scans and manages ingredients in a refrigerator and suggests recipes that use ingredients in order of their expiration date. This refrigerator management system automatically scans and manages ingredients in a refrigerator and suggests recipes that use ingredients in order of their expiration date. In addition, the refrigerator management system can remotely check the status of ingredients in the refrigerator by linking with a smartphone app. Furthermore, when searching for recipes, the refrigerator management system suggests recipes that use only ingredients and seasonings that are already in the home. Moreover, the refrigerator management system not only manages inventory but also compares prices from nearby supermarkets and online stores to recommend the best place to buy and products, and provides a mechanism that automatically places orders according to the user's purchase intention. For example, the refrigerator management system uses cameras and sensors installed inside the refrigerator to acquire information such as the type and quantity of ingredients and their expiration dates. For example, it automatically scans information on vegetables, meat, seasonings, etc. inside the refrigerator and registers it in a database. This allows the system to constantly monitor the status of ingredients inside the refrigerator. Next, based on the acquired ingredient information, the refrigerator management system suggests recipes that prioritize the use of ingredients that are nearing their expiration date. For example, it suggests salad and soup recipes that use vegetables that are nearing their expiration date. This reduces food waste and allows for efficient use. Furthermore, the refrigerator management system can be linked with a smartphone app to remotely check the status of food inside the refrigerator. Users can check information such as the type and quantity of food in the refrigerator and its expiration date through the smartphone app. For example, they can check the status of food in the refrigerator and purchase necessary ingredients even when they are out. The refrigerator management system also suggests recipes that use only the ingredients and seasonings that are already in the home. For example, it suggests simple recipes using the vegetables, meat, and seasonings that are in the refrigerator. This allows users to cook without using ingredients or seasonings that they don't have at home. In addition to inventory management, the refrigerator management system also provides information on special offers at nearby supermarkets and compares prices at online stores to recommend the best place to buy and the best products. For example, it recommends vegetables that are on sale at nearby supermarkets or seasonings that can be purchased cheaply at online stores. This allows users to shop efficiently.Finally, the refrigerator management system provides a mechanism that automatically places orders based on the user's purchase intentions. For example, when a user indicates a purchase intention through a smartphone app, the refrigerator management system automatically places an order and delivers it to the specified address. This allows users to obtain necessary ingredients and seasonings without any hassle. In this way, the refrigerator management system efficiently manages the ingredients in the refrigerator, suggests recipes using ingredients that are nearing their expiration date, remotely checks the status of ingredients, recommends the best suppliers and products, and places orders automatically.

[0029] The refrigerator management system according to this embodiment comprises a collection unit, a suggestion unit, a confirmation unit, a recommendation unit, and an order unit. The collection unit automatically scans and manages the food items in the refrigerator. The collection unit acquires information such as the type and quantity of food items and their expiration dates using, for example, cameras and sensors installed inside the refrigerator. For example, the collection unit automatically scans information on vegetables, meat, seasonings, etc., inside the refrigerator and registers it in a database. The collection unit can also constantly monitor the status of the food items inside the refrigerator. For example, the collection unit can periodically scan and update information such as the type and quantity of food items and their expiration dates. The suggestion unit proposes recipes that use food items in order of their expiration date, based on the food item information collected by the collection unit. For example, the suggestion unit proposes recipes that prioritize the use of food items with approaching expiration dates, based on the acquired food item information. For example, the suggestion unit proposes salad and soup recipes using vegetables with approaching expiration dates. The suggestion unit can also propose recipes for efficiently using food items with approaching expiration dates. For example, the suggestion unit proposes recipes for dishes that combine food items with approaching expiration dates. The verification unit remotely checks the status of ingredients in the refrigerator based on the recipe proposed by the suggestion unit. The verification unit can, for example, check information such as the type, quantity, and expiration date of ingredients in the refrigerator via a smartphone app. For example, the verification unit can check the status of ingredients in the refrigerator and purchase necessary ingredients even when away from home. The verification unit can also check the status of ingredients in the refrigerator in real time. For example, the verification unit displays information such as the type, quantity, and expiration date of ingredients in the refrigerator in real time. The recommendation unit recommends the best place to buy and products based on the ingredient information confirmed by the verification unit. The recommendation unit collects information such as special offers from nearby supermarkets and price information from online stores to recommend the best place to buy and products. For example, the recommendation unit recommends vegetables on sale at nearby supermarkets or seasonings that can be purchased cheaply at online stores. The recommendation unit can also recommend the best products according to the user's purchase intention. For example, the recommendation unit confirms the user's purchase intention and recommends necessary ingredients and seasonings. The ordering unit automatically orders the products recommended by the recommendation unit according to the user's purchase intention.The ordering unit, for example, automatically places an order and delivers it to the specified address when a user indicates their intention to purchase via a smartphone app. For example, the ordering unit confirms the user's purchase intention and automatically orders the necessary ingredients and seasonings. The ordering unit can also adjust the order contents according to the user's purchase intention. For example, the ordering unit can change the order contents based on the user's purchase intention. As a result, the refrigerator management system according to this embodiment can efficiently manage the ingredients in the refrigerator, suggest recipes that use ingredients in order of their expiration date, remotely check the status of ingredients, recommend the optimal supplier and products, and place orders automatically.

[0030] The collection unit automatically scans and manages the food items inside the refrigerator. For example, it uses cameras and sensors installed inside the refrigerator to acquire information such as the type, quantity, and expiration date of the food items. Specifically, high-resolution cameras placed on each shelf and drawer inside the refrigerator capture images of the food items, and image recognition technology is used to identify the type of food. Weight sensors and RFID tags are used to measure the quantity and weight of the food items, and expiration dates are obtained by scanning barcodes or 2D codes (e.g., QR codes). This data is transmitted in real time to a central database, ensuring that the information is always up-to-date. Furthermore, the collection unit also monitors the temperature and humidity inside the refrigerator, providing information to maintain optimal food storage conditions. For example, a temperature sensor monitors the temperature inside the refrigerator and issues an alert if an abnormality is detected. A humidity sensor monitors the humidity level and makes adjustments to maintain appropriate humidity. This allows the collection unit to comprehensively manage not only the type, quantity, and expiration date of food items inside the refrigerator, but also their storage conditions. Additionally, the collection unit automatically scans and updates the database when the user adds new food items. For example, when a user puts new food items into the refrigerator, a camera recognizes the items, sensors measure their quantity and weight, and scans their expiration dates to register them in the database. This allows the collection unit to always maintain up-to-date food information, enabling efficient management.

[0031] The suggestion department proposes recipes that use ingredients in order of their expiration date, based on the ingredient information collected by the collection department. For example, the suggestion department proposes recipes that prioritize the use of ingredients nearing their expiration date, based on the acquired ingredient information. Specifically, the suggestion department uses AI to analyze ingredient information and generate recipes that efficiently use ingredients nearing their expiration date. For example, the AI ​​proposes recipes for salads, soups, and main dishes based on information about vegetables, meat, and seasonings in the refrigerator. The suggestion department can also propose customized recipes considering the user's preferences and past cooking history. For example, it proposes the optimal recipe based on dishes the user has previously enjoyed making and allergy information. Furthermore, the suggestion department also provides information on ingredient combinations and cooking methods to support users in cooking efficiently. For example, the suggestion department proposes recipes that combine ingredients nearing their expiration date and also provides information on cooking procedures and necessary cooking utensils. In this way, the suggestion department helps users use the ingredients in their refrigerator without waste and cook efficiently.

[0032] The verification unit remotely checks the status of ingredients in the refrigerator based on recipes proposed by the suggestion unit. The verification unit can, for example, check information such as the type, quantity, and expiration date of ingredients in the refrigerator via a smartphone app. Specifically, the verification unit provides a smartphone app so that users can check the status of ingredients in the refrigerator even when they are away from home. The app displays the latest ingredient information transmitted from the collection unit, which users can use as a reference when purchasing necessary ingredients. The verification unit can also check the status of ingredients in the refrigerator in real time. For example, when a user opens the refrigerator, the camera and sensors automatically scan and display the latest ingredient information in the app. This allows users to always know information such as the type, quantity, and expiration date of ingredients in the refrigerator. Furthermore, the verification unit records the consumption status and purchase history of ingredients, supporting users in efficiently managing their ingredients. For example, the verification unit records which ingredients the user consumed and when, which can be used as a reference for future purchases. In this way, the verification unit helps users efficiently manage the ingredients in their refrigerator and reduce waste.

[0033] The recommendation department recommends the best place to buy and products based on ingredient information verified by the verification department. For example, the recommendation department collects information on sales at nearby supermarkets and prices at online stores to recommend the best place to buy and products. Specifically, the recommendation department uses AI to analyze price information from nearby supermarkets and online stores to identify the most economical place to buy for the user. For example, the AI ​​collects information on sales at nearby supermarkets and recommends the information if the ingredients the user needs are on sale. It also analyzes price information from online stores and recommends products that the user can buy cheaply. Furthermore, the recommendation department can recommend the best products according to the user's purchasing intentions. For example, if the user prioritizes a particular brand or quality, it will recommend products that meet those conditions. The recommendation department can also provide customized recommendations by considering the user's past purchase history and preferences. For example, it will recommend the best products based on products the user has purchased in the past and brands they prefer. In this way, the recommendation department helps users purchase ingredients efficiently and make economical choices.

[0034] The ordering department automatically places orders for products recommended by the recommendation department based on the user's purchase intent. For example, when a user indicates their intention to purchase through a smartphone app, the ordering department automatically places the order and delivers it to the specified address. Specifically, when a user presses the purchase button on the app, the ordering department automatically handles the ordering process for the recommended products. For example, based on the user's delivery address and payment information, the ordering department sends the order to the online store and handles the product delivery process. The ordering department can also adjust the order details according to the user's purchase intent. For example, if the user adds a specific product or changes the quantity, the ordering department reflects these changes in the order. Furthermore, the ordering department can track the progress of the order in real time and notify the user. For example, it sends a notification when the order is accepted and provides updates on the shipping and delivery status of the products. In this way, the ordering department helps users efficiently purchase groceries and receive them smoothly.

[0035] The collection unit can acquire information such as the type and quantity of food items and their expiration dates using at least one of the cameras and sensors installed inside the refrigerator. For example, the collection unit can acquire information such as the type and quantity of food items and their expiration dates using a camera installed inside the refrigerator. For example, the collection unit can acquire information such as the type and quantity of food items and their expiration dates using a sensor installed inside the refrigerator. The collection unit can also acquire information about food items by combining cameras and sensors. For example, the collection unit can acquire images of food items with a camera and measure the weight of food items with a sensor. This allows the collection unit to accurately acquire information about food items inside the refrigerator. Cameras and sensors include, but are not limited to, CCD cameras and infrared sensors. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or without AI. For example, the collection unit can input image data acquired by the camera into a generating AI and have the generating AI process the image data to generate information such as the type and quantity of food items and their expiration dates.

[0036] The suggestion unit can propose recipes that prioritize the use of ingredients nearing their expiration date, based on the acquired ingredient information. For example, the suggestion unit can propose recipes for salads and soups using vegetables nearing their expiration date. The suggestion unit can also propose recipes for efficiently using ingredients nearing their expiration date. For example, the suggestion unit can propose recipes for dishes that combine ingredients nearing their expiration date. This allows the suggestion unit to reduce food waste by prioritizing the use of ingredients nearing their expiration date. Specific criteria for ingredients nearing their expiration date include, but are not limited to, the number of days remaining until the expiration date. Some or all of the above processing in the suggestion unit may be performed using, for example, AI, or not using AI. For example, the suggestion unit can input acquired ingredient information into a generation AI and have the generation AI propose recipes that prioritize the use of ingredients nearing their expiration date.

[0037] The verification unit can remotely check the status of food items in the refrigerator via a smartphone app. For example, the verification unit can check information such as the type, quantity, and expiration date of food items in the refrigerator via a smartphone app. For example, the verification unit can check the status of food items in the refrigerator and purchase necessary items even when away from home. Furthermore, the verification unit can check the status of food items in the refrigerator in real time. For example, the verification unit displays information such as the type, quantity, and expiration date of food items in the refrigerator in real time. This allows the verification unit to remotely check the status of food items in the refrigerator. Specific methods and technologies for remote verification include, but are not limited to, the devices and applications used. Some or all of the above-described processes in the verification unit may be performed using AI, for example, or without AI. For example, the verification unit can input food information acquired via a smartphone app into a generating AI and have the generating AI perform the remote verification.

[0038] The recommendation unit can collect sale information from nearby supermarkets and price information from online stores to recommend places to buy and products. For example, the recommendation unit can collect sale information from nearby supermarkets and recommend the best place to buy and products. For example, the recommendation unit can collect price information from online stores and recommend the best place to buy and products. The recommendation unit can also combine sale information from nearby supermarkets and price information from online stores to recommend the best place to buy and products. For example, the recommendation unit can recommend vegetables on sale at nearby supermarkets or seasonings that can be purchased cheaply at online stores. In this way, the recommendation unit can help users shop efficiently by recommending the best places to buy and products. Specific methods and criteria for collecting sale information and price information include, but are not limited to, data sources and update frequency. Some or all of the above processing in the recommendation unit may be performed using, for example, AI, or not using AI. For example, the recommendation unit can input sale information and price information into a generating AI and have the generating AI perform recommendations for the best places to buy and products.

[0039] The ordering system can confirm the user's purchase intent and automatically order the necessary ingredients and seasonings. For example, when a user indicates their purchase intent through a smartphone app, the ordering system automatically places an order and delivers it to the specified address. For example, the ordering system confirms the user's purchase intent and automatically orders the necessary ingredients and seasonings. The ordering system can also adjust the order contents according to the user's purchase intent. For example, the ordering system can change the order contents based on the user's purchase intent. This allows the ordering system to automatically order according to the user's purchase intent, enabling users to obtain the necessary ingredients and seasonings without any effort. Methods for confirming purchase intent include, but are not limited to, a user interface and a confirmation process. Some or all of the above-described processes in the ordering system may be performed using, for example, AI, or not using AI. For example, the ordering system can input the user's purchase intent into a generating AI and have the generating AI perform the confirmation of purchase intent and the execution of the order.

[0040] The collection unit can evaluate the storage condition of food items based on the temperature and humidity inside the refrigerator when collecting them. For example, if the temperature inside the refrigerator is high, the collection unit can detect food deterioration early and notify the user to use the food promptly. For example, if the humidity inside the refrigerator is high, the collection unit will pay particular attention to evaluating the storage condition of moisture-sensitive food items. Furthermore, if there are large fluctuations in temperature and humidity, the collection unit can frequently check the storage condition of food items and suggest appropriate storage methods. In this way, the collection unit can detect food deterioration early by evaluating the storage condition of food items while considering the temperature and humidity inside the refrigerator. Specific measurement methods and criteria for temperature and humidity include, but are not limited to, the sensors used and the measurement frequency. Some or all of the above processing in the collection unit may be performed using, for example, AI, or not using AI. For example, the collection unit can input temperature and humidity data into a generating AI and have the generating AI perform the evaluation of the storage condition.

[0041] The collection unit can analyze the shape and color of ingredients to determine their freshness when they are collected. For example, if the color of an ingredient has changed, the collection unit can determine that its freshness has decreased and notify the user to use it as soon as possible. For example, if the shape of an ingredient has changed, the collection unit can determine that it has deteriorated and encourage its use. The collection unit can also periodically scan for changes in the color and shape of ingredients and evaluate changes in freshness in real time. This allows the collection unit to detect deterioration of ingredients early by analyzing their shape and color to determine their freshness. Specific methods and criteria for analyzing shape and color include, but are not limited to, image analysis algorithms and color criteria. Some or all of the above-described processes in the collection unit may be performed using, for example, AI, or not using AI. For example, the collection unit can input image data of ingredients into a generating AI and have the generating AI perform shape and color analysis.

[0042] The collection unit can efficiently scan for food items based on their placement within the refrigerator. For example, the collection unit can optimize the scanning order based on the placement information. For example, the collection unit can limit the scanning range considering the placement information to collect items efficiently. The collection unit can also adjust the scanning frequency based on the placement information to collect items efficiently. In this way, the collection unit can improve the efficiency of food collection by scanning efficiently while considering the placement information within the refrigerator. Specific methods and criteria for collecting placement information include, but are not limited to, camera position and object recognition technology. Some or all of the above-described processes in the collection unit may be performed using, for example, AI, or without AI. For example, the collection unit can input the placement information within the refrigerator into a generating AI and have the generating AI optimize the scanning order and range.

[0043] The collection unit can identify newly added ingredients based on the user's purchase history when collecting ingredients. For example, the collection unit can identify newly purchased ingredients based on the user's purchase history and register them in the database. For example, the collection unit can refer to the purchase history and prioritize scanning for newly added ingredients in the refrigerator. The collection unit can also automatically set the expiration date of newly added ingredients based on the purchase history. This allows the collection unit to improve the accuracy of ingredient management by identifying newly added ingredients by referring to the user's purchase history. Specific methods for collecting and using purchase history include, but are not limited to, the database structure and privacy protection. Some or all of the above processing in the collection unit may be performed using, for example, AI, or not using AI. For example, the collection unit can input user purchase history data into a generating AI and have the generating AI identify newly added ingredients.

[0044] The suggestion unit can suggest recipes that suit the user's preferences based on their past cooking history. For example, the suggestion unit can suggest recipes that suit the user's preferences based on dishes the user has made in the past. For example, the suggestion unit can suggest recipes using ingredients that the user likes based on their past cooking history. The suggestion unit can also analyze the user's past cooking history and suggest similar recipes. In this way, the suggestion unit can improve user satisfaction by suggesting recipes that suit the user's preferences by referring to their past cooking history. Specific methods for collecting and using past cooking history include, but are not limited to, database structure and privacy protection. 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 the user's past cooking history data into a generating AI and have the generating AI suggest recipes that suit the user's preferences.

[0045] The suggestion unit can suggest healthy recipes based on the nutritional value of ingredients when suggesting recipes. For example, the suggestion unit can suggest balanced recipes based on the nutritional value of ingredients. For example, the suggestion unit can suggest healthy recipes using highly nutritious ingredients. Furthermore, the suggestion unit can suggest recipes with appropriate nutritional value, taking into account the user's health condition. In this way, the suggestion unit can support the user's health by suggesting healthy recipes that take into account the nutritional value of ingredients. Specific evaluation methods and criteria for nutritional value include, but are not limited to, nutritional information tables and calorie calculations. 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 value data of ingredients into a generating AI and have the generating AI suggest healthy recipes.

[0046] The suggestion unit can suggest safe recipes based on the user's allergy information when suggesting recipes. For example, the suggestion unit can suggest recipes that do not contain allergens based on the user's allergy information. For example, the suggestion unit can suggest recipes that use safe ingredients, taking allergy information into consideration. The suggestion unit can also suggest recipes that avoid allergens by referring to the user's allergy information. In this way, the suggestion unit can protect the user's health by suggesting safe recipes that take the user's allergy information into consideration. Specific methods for collecting and using allergy information include, but are not limited to, user input and database structure. Some or all of the above processing in the suggestion unit may be performed using, for example, AI, or not using AI. For example, the suggestion unit can input the user's allergy information into a generating AI and have the generating AI suggest safe recipes.

[0047] The suggestion unit can suggest appropriate recipes based on the user's dietary restrictions when suggesting recipes. For example, the suggestion unit can suggest appropriate recipes based on the user's dietary restrictions. For example, the suggestion unit can suggest recipes using appropriate ingredients, taking into account dietary restrictions such as vegetarianism or gluten-free diets. The suggestion unit can also refer to the user's dietary restrictions and suggest recipes that meet those restrictions. In this way, the suggestion unit can support the user's health by suggesting appropriate recipes that take the user's dietary restrictions into account. Specific content and criteria of dietary restrictions include, but are not limited to, calorie restrictions or restrictions on specific nutrients. Some or all of the above processing in the suggestion unit may be performed using, for example, AI, or not using AI. For example, the suggestion unit can input the user's dietary restriction data into a generating AI and have the generating AI suggest appropriate recipes.

[0048] The verification unit can explicitly display the expiration dates of food items in the refrigerator during verification. For example, the verification unit can highlight food items nearing their expiration date and encourage them to be used sooner. For example, the verification unit can display a warning for food items that have expired and encourage them to be discarded. The verification unit can also color-code food items nearing their expiration date to make them visually easier to understand. In this way, the verification unit can reduce food waste by highlighting the expiration dates of food items in the refrigerator. Specific methods and criteria for explicitly displaying expiration dates include, but are not limited to, display format and highlighting method. Some or all of the above processing in the verification unit may be performed using, for example, AI, or not using AI. For example, the verification unit can input expiration date data into a generating AI and have the generating AI execute the explicit display of expiration dates.

[0049] The verification unit can evaluate the frequency of use of ingredients during verification and display them preferentially. For example, the verification unit can prioritize the display of frequently used ingredients to manage them efficiently. For example, the verification unit can make less frequently used ingredients stand out to encourage their use. The verification unit can also analyze usage frequency and provide the optimal display method. In this way, the verification unit can efficiently manage ingredients by analyzing their usage frequency and displaying them preferentially. Specific methods and criteria for evaluating usage frequency include, but are not limited to, the number of uses and the duration of use. Some or all of the above processing in the verification unit may be performed using, for example, AI, or not using AI. For example, the verification unit can input ingredient usage frequency data into a generating AI and have the generating AI perform the evaluation and display of usage frequency.

[0050] The verification unit can send notifications at the optimal time based on the user's smartphone location information during verification. For example, if the user is at home, the verification unit can notify the user of the status of food in the refrigerator in real time. For example, if the user is out, the verification unit can notify only the necessary information, allowing the user to check the details later. The verification unit can also send notifications at the optimal time based on the user's location information. In this way, the verification unit can improve user convenience by sending notifications at the optimal time considering the user's smartphone location information. Specific methods for collecting and using location information include, but are not limited to, GPS data and privacy protection. Some or all of the above processing in the verification unit may be performed using, for example, AI, or not using AI. For example, the verification unit can input the user's location information data into a generating AI and have the generating AI execute a notification at the optimal time.

[0051] The verification unit can predict food consumption based on the user's family structure during verification. For example, the verification unit predicts food consumption based on family structure and displays an appropriate amount. For example, the verification unit predicts food consumption considering family structure and reduces waste. The verification unit can also predict food consumption based on family structure and provide an optimal management method. In this way, the verification unit can reduce waste by predicting food consumption considering the user's family structure. Specific methods for collecting and using family structure include, but are not limited to, user input and database structure. Some or all of the above processing in the verification unit may be performed using, for example, AI, or without AI. For example, the verification unit can input the user's family structure data into a generating AI and have the generating AI perform consumption prediction.

[0052] The recommendation system can recommend products that match the user's preferences based on their past purchase history. For example, the recommendation system can recommend products that match the user's preferences based on products the user has purchased in the past. For example, the recommendation system can recommend products from brands the user likes based on their past purchase history. The recommendation system can also analyze the user's past purchase history and recommend similar products. In this way, the recommendation system can improve user satisfaction by recommending products that match the user's preferences by referring to their past purchase history. Specific methods for collecting and using past purchase history include, but are not limited to, database structure and privacy protection. Some or all of the above processing in the recommendation system may be performed using, for example, AI, or not using AI. For example, the recommendation system can input the user's past purchase history data into a generating AI and have the generating AI recommend products that match the user's preferences.

[0053] The recommendation department can recommend highly reliable products based on product reviews and ratings. For example, the recommendation department can recommend highly-rated products based on product reviews. For example, the recommendation department can recommend highly-rated products that match the user's preferences. The recommendation department can also analyze product ratings and recommend highly reliable products. In this way, the recommendation department can improve user satisfaction by recommending highly reliable products that take product reviews and ratings into consideration. Specific methods and criteria for collecting reviews and ratings include, but are not limited to, review sites and rating criteria. Some or all of the above processing in the recommendation department may be performed using AI, for example, or not using AI. For example, the recommendation department can input product review and rating data into a generating AI and have the generating AI perform recommendations for highly reliable products.

[0054] The recommendation system can recommend the most suitable products based on the user's budget. For example, the recommendation system can recommend products that fit within a price range based on the user's budget. For example, the recommendation system can recommend the most cost-effective product within the budget. The recommendation system can also recommend the most suitable products while considering the user's budget. In this way, the recommendation system can improve user satisfaction by recommending the most suitable products while considering the user's budget. Specific methods for collecting and using budgets include, but are not limited to, user input and database structure. Some or all of the above processes in the recommendation system may be performed using, for example, AI, or not using AI. For example, the recommendation system can input user budget data into a generating AI and have the generating AI perform the optimal product recommendation.

[0055] The recommendation system can evaluate the user's purchasing patterns and suggest efficient shopping routes during the recommendation process. For example, the recommendation system can suggest efficient shopping routes based on the user's purchasing patterns. For example, the recommendation system can analyze purchasing patterns and suggest the shortest route to shop. The recommendation system can also suggest efficient shopping routes considering the user's purchasing patterns. In this way, the recommendation system can improve shopping efficiency by analyzing the user's purchasing patterns and suggesting efficient shopping routes. Specific methods and criteria for analyzing purchasing patterns include, but are not limited to, purchase frequency and purchase timing. Some or all of the above processing in the recommendation system may be performed using, for example, AI, or not using AI. For example, the recommendation system can input user purchasing pattern data into a generating AI and have the generating AI suggest efficient shopping routes.

[0056] The ordering department can select the optimal ordering method by referring to the user's past order history when an order is placed. For example, the ordering department can select the optimal ordering method based on the user's past order history. For example, the ordering department can prioritize suggesting ordering methods preferred by the user based on past order history. The ordering department can also analyze the user's past order history and select the optimal ordering method. In this way, the ordering department can improve user satisfaction by selecting the optimal ordering method by referring to the user's past order history. Specific methods for collecting and using past order history include, but are not limited to, database structure and privacy protection. Some or all of the above processing in the ordering department may be performed using AI, for example, or not using AI. For example, the ordering department can input the user's past order history data into a generating AI and have the generating AI select the optimal ordering method.

[0057] The order department can select the most suitable delivery company based on delivery time at the time of order. For example, the order department can select the most suitable delivery company based on delivery time. For example, the order department can select the most suitable delivery company to match the user's desired delivery time. The order department can also select the most suitable delivery company considering delivery time. In this way, the order department can improve user satisfaction by selecting the most suitable delivery company considering delivery time. Specific evaluation methods and criteria for delivery time include, but are not limited to, the delivery company's schedule and delivery distance. Some or all of the above processing in the order department may be performed using AI, for example, or not using AI. For example, the order department can input delivery time data into a generating AI and have the generating AI perform the selection of the most suitable delivery company.

[0058] The order processing unit can suggest the most suitable payment option based on the user's payment method at the time of ordering. For example, the order processing unit can suggest the most suitable payment option based on the user's payment method. For example, the order processing unit can suggest a payment option preferred by the user, taking the payment method into consideration. The order processing unit can also suggest the most suitable payment option by referring to the user's payment method. In this way, the order processing unit can improve user satisfaction by suggesting the most suitable payment option considering the user's payment method. Specific methods for collecting and using payment methods include, but are not limited to, credit cards and electronic money. Some or all of the above processing in the order processing unit may be performed using, for example, AI, or not using AI. For example, the order processing unit can input the user's payment method data into a generating AI and have the generating AI suggest the most suitable payment option.

[0059] The order processing unit can select the optimal delivery route based on the user's address information at the time of order. For example, the order processing unit can select the optimal delivery route based on the user's address information. For example, the order processing unit can take the address information into consideration to ensure delivery via the shortest route. The order processing unit can also refer to the user's address information to select the optimal delivery route. This allows the order processing unit to improve delivery efficiency by selecting the optimal delivery route while considering the user's address information. Specific methods for collecting and using address information include, but are not limited to, user input and database structure. Some or all of the above-described processes in the order processing unit may be performed using, for example, AI, or not. For example, the order processing unit can input user address information data into a generating AI and have the generating AI select the optimal delivery route.

[0060] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0061] The refrigerator management system can also be equipped with a voice recognition unit. The voice recognition unit can recognize the user's voice commands and provide information about the ingredients in the refrigerator by voice. For example, if the user says, "Tell me what vegetables are in the refrigerator," the voice recognition unit will answer by voice the types and quantities of vegetables in the refrigerator. The voice recognition unit can also suggest recipes based on the user's voice commands. For example, if the user says, "Tell me a recipe for tonight's dinner," the voice recognition unit will suggest an appropriate recipe by voice based on the information about the ingredients in the refrigerator. Furthermore, the voice recognition unit can suggest purchasing ingredients based on the user's voice commands. For example, if the user says, "We're running low on milk, please buy some," the voice recognition unit will suggest purchasing milk and can automatically place an order if necessary. In this way, the refrigerator management system can improve user convenience by using voice recognition functionality.

[0062] The refrigerator management system can also include a health management unit. This unit can suggest ingredients and recipes based on the user's health status. For example, if a user is on a diet, the health management unit can suggest low-calorie ingredients and recipes. If a user needs to consume a specific nutrient, the health management unit can suggest ingredients and recipes rich in that nutrient. Furthermore, the health management unit can manage the expiration dates of ingredients based on the user's health status. For example, if a user needs to consume a particular ingredient, the unit can notify them when its expiration date is approaching. In this way, the refrigerator management system can support the user's health.

[0063] The refrigerator management system can also include an energy management unit. This unit can make suggestions for optimizing the refrigerator's energy consumption. For example, it can suggest improving cooling efficiency by optimizing the placement of food items inside the refrigerator. It can also monitor the frequency of refrigerator opening and closing and suggest ways to reduce unnecessary energy consumption. Furthermore, it can suggest reducing energy consumption by optimizing the refrigerator's temperature settings. In this way, the refrigerator management system can optimize energy consumption and support environmentally conscious operation.

[0064] The refrigerator management system can also include a food exchange function. This function can propose food exchanges between users. For example, it can suggest that users exchange surplus food with other users. Furthermore, it can provide a platform to facilitate food exchanges between nearby users. Additionally, it can manage exchanged food and record the exchange history. This allows the refrigerator management system to reduce food waste and promote the efficient use of food within the community.

[0065] The refrigerator management system can also include a food donation section. This section allows users to suggest donating surplus food items. For example, it could suggest donating food items nearing their expiration date. Furthermore, it can provide a platform for donating food to local food banks or charities. Additionally, it can manage donated food and record donation history. This allows the refrigerator management system to reduce food waste and promote social contribution.

[0066] The following briefly describes the processing flow for example form 1.

[0067] Step 1: The collection unit automatically scans and manages the food items inside the refrigerator. The collection unit uses cameras and sensors installed inside the refrigerator to acquire information such as the type and quantity of food items and their expiration dates. For example, the collection unit automatically scans information on vegetables, meat, condiments, etc., inside the refrigerator and registers it in the database. The collection unit can also constantly monitor the status of the food items inside the refrigerator and periodically scan and update the information. Step 2: The suggestion department proposes recipes that use ingredients in order of their expiration date, based on the ingredient information collected by the collection department. The suggestion department proposes recipes that prioritize the use of ingredients with the nearest expiration date, based on the acquired ingredient information. For example, the suggestion department proposes salad and soup recipes using vegetables that are nearing their expiration date. The suggestion department can also propose recipes that efficiently use ingredients that are nearing their expiration date. Step 3: The verification unit remotely checks the status of ingredients in the refrigerator based on the recipe suggested by the suggestion unit. The verification unit can check information such as the type and quantity of ingredients in the refrigerator and their expiration dates via a smartphone app. For example, it can check the status of ingredients in the refrigerator and purchase necessary ingredients even when away from home. The verification unit can also check the status of ingredients in the refrigerator in real time. Step 4: The recommendation team recommends the best place to buy and products based on the food information verified by the verification team. The recommendation team collects information on special offers at nearby supermarkets and prices at online stores to recommend the best place to buy and products. For example, it may recommend vegetables on sale at nearby supermarkets or seasonings that can be purchased cheaply at online stores. The recommendation team can also recommend the best products according to the user's purchasing intent. Step 5: The ordering department automatically places orders for products recommended by the recommendation department based on the user's purchase intent. When a user indicates their purchase intent through the smartphone app, the ordering department automatically places the order and delivers it to the specified address. For example, it can confirm the user's purchase intent and automatically order the necessary ingredients and seasonings. The ordering department can also adjust the order contents according to the user's purchase intent.

[0068] (Example of form 2) The refrigerator management system according to an embodiment of the present invention is a system that automatically scans and manages ingredients in a refrigerator and suggests recipes that use ingredients in order of their expiration date. This refrigerator management system automatically scans and manages ingredients in a refrigerator and suggests recipes that use ingredients in order of their expiration date. In addition, the refrigerator management system can remotely check the status of ingredients in the refrigerator by linking with a smartphone app. Furthermore, when searching for recipes, the refrigerator management system suggests recipes that use only ingredients and seasonings that are already in the home. Moreover, the refrigerator management system not only manages inventory but also compares prices from nearby supermarkets and online stores to recommend the best place to buy and products, and provides a mechanism that automatically places orders according to the user's purchase intention. For example, the refrigerator management system uses cameras and sensors installed inside the refrigerator to acquire information such as the type and quantity of ingredients and their expiration dates. For example, it automatically scans information on vegetables, meat, seasonings, etc. inside the refrigerator and registers it in a database. This allows the system to constantly monitor the status of ingredients inside the refrigerator. Next, based on the acquired ingredient information, the refrigerator management system suggests recipes that prioritize the use of ingredients that are nearing their expiration date. For example, it suggests salad and soup recipes that use vegetables that are nearing their expiration date. This reduces food waste and allows for efficient use. Furthermore, the refrigerator management system can be linked with a smartphone app to remotely check the status of food inside the refrigerator. Users can check information such as the type and quantity of food in the refrigerator and its expiration date through the smartphone app. For example, they can check the status of food in the refrigerator and purchase necessary ingredients even when they are out. The refrigerator management system also suggests recipes that use only the ingredients and seasonings that are already in the home. For example, it suggests simple recipes using the vegetables, meat, and seasonings that are in the refrigerator. This allows users to cook without using ingredients or seasonings that they don't have at home. In addition to inventory management, the refrigerator management system also provides information on special offers at nearby supermarkets and compares prices at online stores to recommend the best place to buy and the best products. For example, it recommends vegetables that are on sale at nearby supermarkets or seasonings that can be purchased cheaply at online stores. This allows users to shop efficiently.Finally, the refrigerator management system provides a mechanism that automatically places orders based on the user's purchase intentions. For example, when a user indicates a purchase intention through a smartphone app, the refrigerator management system automatically places an order and delivers it to the specified address. This allows users to obtain necessary ingredients and seasonings without any hassle. In this way, the refrigerator management system efficiently manages the ingredients in the refrigerator, suggests recipes using ingredients that are nearing their expiration date, remotely checks the status of ingredients, recommends the best suppliers and products, and places orders automatically.

[0069] The refrigerator management system according to this embodiment comprises a collection unit, a suggestion unit, a confirmation unit, a recommendation unit, and an order unit. The collection unit automatically scans and manages the food items in the refrigerator. The collection unit acquires information such as the type and quantity of food items and their expiration dates using, for example, cameras and sensors installed inside the refrigerator. For example, the collection unit automatically scans information on vegetables, meat, seasonings, etc., inside the refrigerator and registers it in a database. The collection unit can also constantly monitor the status of the food items inside the refrigerator. For example, the collection unit can periodically scan and update information such as the type and quantity of food items and their expiration dates. The suggestion unit proposes recipes that use food items in order of their expiration date, based on the food item information collected by the collection unit. For example, the suggestion unit proposes recipes that prioritize the use of food items with approaching expiration dates, based on the acquired food item information. For example, the suggestion unit proposes salad and soup recipes using vegetables with approaching expiration dates. The suggestion unit can also propose recipes for efficiently using food items with approaching expiration dates. For example, the suggestion unit proposes recipes for dishes that combine food items with approaching expiration dates. The verification unit remotely checks the status of ingredients in the refrigerator based on the recipe proposed by the suggestion unit. The verification unit can, for example, check information such as the type, quantity, and expiration date of ingredients in the refrigerator via a smartphone app. For example, the verification unit can check the status of ingredients in the refrigerator and purchase necessary ingredients even when away from home. The verification unit can also check the status of ingredients in the refrigerator in real time. For example, the verification unit displays information such as the type, quantity, and expiration date of ingredients in the refrigerator in real time. The recommendation unit recommends the best place to buy and products based on the ingredient information confirmed by the verification unit. The recommendation unit collects information such as special offers from nearby supermarkets and price information from online stores to recommend the best place to buy and products. For example, the recommendation unit recommends vegetables on sale at nearby supermarkets or seasonings that can be purchased cheaply at online stores. The recommendation unit can also recommend the best products according to the user's purchase intention. For example, the recommendation unit confirms the user's purchase intention and recommends necessary ingredients and seasonings. The ordering unit automatically orders the products recommended by the recommendation unit according to the user's purchase intention.The ordering unit, for example, automatically places an order and delivers it to the specified address when a user indicates their intention to purchase via a smartphone app. For example, the ordering unit confirms the user's purchase intention and automatically orders the necessary ingredients and seasonings. The ordering unit can also adjust the order contents according to the user's purchase intention. For example, the ordering unit can change the order contents based on the user's purchase intention. As a result, the refrigerator management system according to this embodiment can efficiently manage the ingredients in the refrigerator, suggest recipes that use ingredients in order of their expiration date, remotely check the status of ingredients, recommend the optimal supplier and products, and place orders automatically.

[0070] The collection unit automatically scans and manages the food items inside the refrigerator. For example, it uses cameras and sensors installed inside the refrigerator to acquire information such as the type, quantity, and expiration date of the food items. Specifically, high-resolution cameras placed on each shelf and drawer inside the refrigerator capture images of the food items, and image recognition technology is used to identify the type of food. Weight sensors and RFID tags are used to measure the quantity and weight of the food items, and expiration dates are obtained by scanning barcodes or 2D codes (e.g., QR codes). This data is transmitted in real time to a central database, ensuring that the information is always up-to-date. Furthermore, the collection unit also monitors the temperature and humidity inside the refrigerator, providing information to maintain optimal food storage conditions. For example, a temperature sensor monitors the temperature inside the refrigerator and issues an alert if an abnormality is detected. A humidity sensor monitors the humidity level and makes adjustments to maintain appropriate humidity. This allows the collection unit to comprehensively manage not only the type, quantity, and expiration date of food items inside the refrigerator, but also their storage conditions. Additionally, the collection unit automatically scans and updates the database when the user adds new food items. For example, when a user puts new food items into the refrigerator, a camera recognizes the items, sensors measure their quantity and weight, and scans their expiration dates to register them in the database. This allows the collection unit to always maintain up-to-date food information, enabling efficient management.

[0071] The suggestion department proposes recipes that use ingredients in order of their expiration date, based on the ingredient information collected by the collection department. For example, the suggestion department proposes recipes that prioritize the use of ingredients nearing their expiration date, based on the acquired ingredient information. Specifically, the suggestion department uses AI to analyze ingredient information and generate recipes that efficiently use ingredients nearing their expiration date. For example, the AI ​​proposes recipes for salads, soups, and main dishes based on information about vegetables, meat, and seasonings in the refrigerator. The suggestion department can also propose customized recipes considering the user's preferences and past cooking history. For example, it proposes the optimal recipe based on dishes the user has previously enjoyed making and allergy information. Furthermore, the suggestion department also provides information on ingredient combinations and cooking methods to support users in cooking efficiently. For example, the suggestion department proposes recipes that combine ingredients nearing their expiration date and also provides information on cooking procedures and necessary cooking utensils. In this way, the suggestion department helps users use the ingredients in their refrigerator without waste and cook efficiently.

[0072] The verification unit remotely checks the status of ingredients in the refrigerator based on recipes proposed by the suggestion unit. The verification unit can, for example, check information such as the type, quantity, and expiration date of ingredients in the refrigerator via a smartphone app. Specifically, the verification unit provides a smartphone app so that users can check the status of ingredients in the refrigerator even when they are away from home. The app displays the latest ingredient information transmitted from the collection unit, which users can use as a reference when purchasing necessary ingredients. The verification unit can also check the status of ingredients in the refrigerator in real time. For example, when a user opens the refrigerator, the camera and sensors automatically scan and display the latest ingredient information in the app. This allows users to always know information such as the type, quantity, and expiration date of ingredients in the refrigerator. Furthermore, the verification unit records the consumption status and purchase history of ingredients, supporting users in efficiently managing their ingredients. For example, the verification unit records which ingredients the user consumed and when, which can be used as a reference for future purchases. In this way, the verification unit helps users efficiently manage the ingredients in their refrigerator and reduce waste.

[0073] The recommendation department recommends the best place to buy and products based on ingredient information verified by the verification department. For example, the recommendation department collects information on sales at nearby supermarkets and prices at online stores to recommend the best place to buy and products. Specifically, the recommendation department uses AI to analyze price information from nearby supermarkets and online stores to identify the most economical place to buy for the user. For example, the AI ​​collects information on sales at nearby supermarkets and recommends the information if the ingredients the user needs are on sale. It also analyzes price information from online stores and recommends products that the user can buy cheaply. Furthermore, the recommendation department can recommend the best products according to the user's purchasing intentions. For example, if the user prioritizes a particular brand or quality, it will recommend products that meet those conditions. The recommendation department can also provide customized recommendations by considering the user's past purchase history and preferences. For example, it will recommend the best products based on products the user has purchased in the past and brands they prefer. In this way, the recommendation department helps users purchase ingredients efficiently and make economical choices.

[0074] The ordering department automatically places orders for products recommended by the recommendation department based on the user's purchase intent. For example, when a user indicates their intention to purchase through a smartphone app, the ordering department automatically places the order and delivers it to the specified address. Specifically, when a user presses the purchase button on the app, the ordering department automatically handles the ordering process for the recommended products. For example, based on the user's delivery address and payment information, the ordering department sends the order to the online store and handles the product delivery process. The ordering department can also adjust the order details according to the user's purchase intent. For example, if the user adds a specific product or changes the quantity, the ordering department reflects these changes in the order. Furthermore, the ordering department can track the progress of the order in real time and notify the user. For example, it sends a notification when the order is accepted and provides updates on the shipping and delivery status of the products. In this way, the ordering department helps users efficiently purchase groceries and receive them smoothly.

[0075] The collection unit can acquire information such as the type and quantity of food items and their expiration dates using at least one of the cameras and sensors installed inside the refrigerator. For example, the collection unit can acquire information such as the type and quantity of food items and their expiration dates using a camera installed inside the refrigerator. For example, the collection unit can acquire information such as the type and quantity of food items and their expiration dates using a sensor installed inside the refrigerator. The collection unit can also acquire information about food items by combining cameras and sensors. For example, the collection unit can acquire images of food items with a camera and measure the weight of food items with a sensor. This allows the collection unit to accurately acquire information about food items inside the refrigerator. Cameras and sensors include, but are not limited to, CCD cameras and infrared sensors. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or without AI. For example, the collection unit can input image data acquired by the camera into a generating AI and have the generating AI process the image data to generate information such as the type and quantity of food items and their expiration dates.

[0076] The suggestion unit can propose recipes that prioritize the use of ingredients nearing their expiration date, based on the acquired ingredient information. For example, the suggestion unit can propose recipes for salads and soups using vegetables nearing their expiration date. The suggestion unit can also propose recipes for efficiently using ingredients nearing their expiration date. For example, the suggestion unit can propose recipes for dishes that combine ingredients nearing their expiration date. This allows the suggestion unit to reduce food waste by prioritizing the use of ingredients nearing their expiration date. Specific criteria for ingredients nearing their expiration date include, but are not limited to, the number of days remaining until the expiration date. Some or all of the above processing in the suggestion unit may be performed using, for example, AI, or not using AI. For example, the suggestion unit can input acquired ingredient information into a generation AI and have the generation AI propose recipes that prioritize the use of ingredients nearing their expiration date.

[0077] The verification unit can remotely check the status of food items in the refrigerator via a smartphone app. For example, the verification unit can check information such as the type, quantity, and expiration date of food items in the refrigerator via a smartphone app. For example, the verification unit can check the status of food items in the refrigerator and purchase necessary items even when away from home. Furthermore, the verification unit can check the status of food items in the refrigerator in real time. For example, the verification unit displays information such as the type, quantity, and expiration date of food items in the refrigerator in real time. This allows the verification unit to remotely check the status of food items in the refrigerator. Specific methods and technologies for remote verification include, but are not limited to, the devices and applications used. Some or all of the above-described processes in the verification unit may be performed using AI, for example, or without AI. For example, the verification unit can input food information acquired via a smartphone app into a generating AI and have the generating AI perform the remote verification.

[0078] The recommendation unit can collect sale information from nearby supermarkets and price information from online stores to recommend places to buy and products. For example, the recommendation unit can collect sale information from nearby supermarkets and recommend the best place to buy and products. For example, the recommendation unit can collect price information from online stores and recommend the best place to buy and products. The recommendation unit can also combine sale information from nearby supermarkets and price information from online stores to recommend the best place to buy and products. For example, the recommendation unit can recommend vegetables on sale at nearby supermarkets or seasonings that can be purchased cheaply at online stores. In this way, the recommendation unit can help users shop efficiently by recommending the best places to buy and products. Specific methods and criteria for collecting sale information and price information include, but are not limited to, data sources and update frequency. Some or all of the above processing in the recommendation unit may be performed using, for example, AI, or not using AI. For example, the recommendation unit can input sale information and price information into a generating AI and have the generating AI perform recommendations for the best places to buy and products.

[0079] The ordering system can confirm the user's purchase intent and automatically order the necessary ingredients and seasonings. For example, when a user indicates their purchase intent through a smartphone app, the ordering system automatically places an order and delivers it to the specified address. For example, the ordering system confirms the user's purchase intent and automatically orders the necessary ingredients and seasonings. The ordering system can also adjust the order contents according to the user's purchase intent. For example, the ordering system can change the order contents based on the user's purchase intent. This allows the ordering system to automatically order according to the user's purchase intent, enabling users to obtain the necessary ingredients and seasonings without any effort. Methods for confirming purchase intent include, but are not limited to, a user interface and a confirmation process. Some or all of the above-described processes in the ordering system may be performed using, for example, AI, or not using AI. For example, the ordering system can input the user's purchase intent into a generating AI and have the generating AI perform the confirmation of purchase intent and the execution of the order.

[0080] The data collection unit can estimate the user's emotions and adjust the frequency of scanning ingredients based on the estimated emotions. For example, if the user is stressed, the data collection unit can reduce the scanning frequency and provide less frequent notifications. For example, if the user is relaxed, the data collection unit can increase the scanning frequency and provide more detailed information. If the user is in a hurry, the data collection unit can minimize the scanning frequency and quickly provide only the necessary information. In this way, the data collection unit can reduce user stress by adjusting the frequency of scanning ingredients 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 data collection unit may be performed using AI or not using AI. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation and adjustment of scanning frequency.

[0081] The collection unit can evaluate the storage condition of food items based on the temperature and humidity inside the refrigerator when collecting them. For example, if the temperature inside the refrigerator is high, the collection unit can detect food deterioration early and notify the user to use the food promptly. For example, if the humidity inside the refrigerator is high, the collection unit will pay particular attention to evaluating the storage condition of moisture-sensitive food items. Furthermore, if there are large fluctuations in temperature and humidity, the collection unit can frequently check the storage condition of food items and suggest appropriate storage methods. In this way, the collection unit can detect food deterioration early by evaluating the storage condition of food items while considering the temperature and humidity inside the refrigerator. Specific measurement methods and criteria for temperature and humidity include, but are not limited to, the sensors used and the measurement frequency. Some or all of the above processing in the collection unit may be performed using, for example, AI, or not using AI. For example, the collection unit can input temperature and humidity data into a generating AI and have the generating AI perform the evaluation of the storage condition.

[0082] The collection unit can analyze the shape and color of ingredients to determine their freshness when they are collected. For example, if the color of an ingredient has changed, the collection unit can determine that its freshness has decreased and notify the user to use it as soon as possible. For example, if the shape of an ingredient has changed, the collection unit can determine that it has deteriorated and encourage its use. The collection unit can also periodically scan for changes in the color and shape of ingredients and evaluate changes in freshness in real time. This allows the collection unit to detect deterioration of ingredients early by analyzing their shape and color to determine their freshness. Specific methods and criteria for analyzing shape and color include, but are not limited to, image analysis algorithms and color criteria. Some or all of the above-described processes in the collection unit may be performed using, for example, AI, or not using AI. For example, the collection unit can input image data of ingredients into a generating AI and have the generating AI perform shape and color analysis.

[0083] The collection unit can estimate the user's emotions and determine the priority of ingredients to collect based on the estimated emotions. For example, if the user is stressed, the collection unit will prioritize collecting ingredients that are easy to cook. For example, if the user is relaxed, the collection unit will also collect ingredients that take time to cook. Furthermore, if the user is in a hurry, the collection unit can prioritize collecting ingredients that can be used immediately. In this way, the collection unit can reduce the user's stress by determining the priority of ingredients to collect 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 collection unit may be performed using AI, for example, or not using AI. For example, the collection unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation and determine the priority of ingredients to collect.

[0084] The collection unit can efficiently scan for food items based on their placement within the refrigerator. For example, the collection unit can optimize the scanning order based on the placement information. For example, the collection unit can limit the scanning range considering the placement information to collect items efficiently. The collection unit can also adjust the scanning frequency based on the placement information to collect items efficiently. In this way, the collection unit can improve the efficiency of food collection by scanning efficiently while considering the placement information within the refrigerator. Specific methods and criteria for collecting placement information include, but are not limited to, camera position and object recognition technology. Some or all of the above-described processes in the collection unit may be performed using, for example, AI, or without AI. For example, the collection unit can input the placement information within the refrigerator into a generating AI and have the generating AI optimize the scanning order and range.

[0085] The collection unit can identify newly added ingredients based on the user's purchase history when collecting ingredients. For example, the collection unit can identify newly purchased ingredients based on the user's purchase history and register them in the database. For example, the collection unit can refer to the purchase history and prioritize scanning for newly added ingredients in the refrigerator. The collection unit can also automatically set the expiration date of newly added ingredients based on the purchase history. This allows the collection unit to improve the accuracy of ingredient management by identifying newly added ingredients by referring to the user's purchase history. Specific methods for collecting and using purchase history include, but are not limited to, the database structure and privacy protection. Some or all of the above processing in the collection unit may be performed using, for example, AI, or not using AI. For example, the collection unit can input user purchase history data into a generating AI and have the generating AI identify newly added ingredients.

[0086] The suggestion unit can estimate the user's emotions and adjust the recipe suggestion method based on the estimated emotions. For example, if the user is stressed, the suggestion unit can suggest a simple and easy recipe. For example, if the user is relaxed, the suggestion unit can suggest a recipe that can be enjoyed at a leisurely pace. Also, if the user is in a hurry, the suggestion unit can suggest a recipe that can be cooked in a short time. In this way, the suggestion unit can reduce the user's stress by adjusting the recipe suggestion method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the 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 and adjustment of the recipe suggestion method.

[0087] The suggestion unit can suggest recipes that suit the user's preferences based on their past cooking history. For example, the suggestion unit can suggest recipes that suit the user's preferences based on dishes the user has made in the past. For example, the suggestion unit can suggest recipes using ingredients that the user likes based on their past cooking history. The suggestion unit can also analyze the user's past cooking history and suggest similar recipes. In this way, the suggestion unit can improve user satisfaction by suggesting recipes that suit the user's preferences by referring to their past cooking history. Specific methods for collecting and using past cooking history include, but are not limited to, database structure and privacy protection. 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 the user's past cooking history data into a generating AI and have the generating AI suggest recipes that suit the user's preferences.

[0088] The suggestion unit can suggest healthy recipes based on the nutritional value of ingredients when suggesting recipes. For example, the suggestion unit can suggest balanced recipes based on the nutritional value of ingredients. For example, the suggestion unit can suggest healthy recipes using highly nutritious ingredients. Furthermore, the suggestion unit can suggest recipes with appropriate nutritional value, taking into account the user's health condition. In this way, the suggestion unit can support the user's health by suggesting healthy recipes that take into account the nutritional value of ingredients. Specific evaluation methods and criteria for nutritional value include, but are not limited to, nutritional information tables and calorie calculations. 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 value data of ingredients into a generating AI and have the generating AI suggest healthy recipes.

[0089] The suggestion unit can estimate the user's emotions and adjust the difficulty of recipes based on those emotions. For example, if the user is stressed, the suggestion unit can suggest an easy recipe. For example, if the user is relaxed, the suggestion unit can suggest a more difficult recipe. Also, if the user is in a hurry, the suggestion unit can suggest a recipe that can be made in a short time. In this way, the suggestion unit can reduce the user's stress by adjusting the difficulty of recipes 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 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 and recipe difficulty adjustment.

[0090] The suggestion unit can suggest safe recipes based on the user's allergy information when suggesting recipes. For example, the suggestion unit can suggest recipes that do not contain allergens based on the user's allergy information. For example, the suggestion unit can suggest recipes that use safe ingredients, taking allergy information into consideration. The suggestion unit can also suggest recipes that avoid allergens by referring to the user's allergy information. In this way, the suggestion unit can protect the user's health by suggesting safe recipes that take the user's allergy information into consideration. Specific methods for collecting and using allergy information include, but are not limited to, user input and database structure. Some or all of the above processing in the suggestion unit may be performed using, for example, AI, or not using AI. For example, the suggestion unit can input the user's allergy information into a generating AI and have the generating AI suggest safe recipes.

[0091] The suggestion unit can suggest appropriate recipes based on the user's dietary restrictions when suggesting recipes. For example, the suggestion unit can suggest appropriate recipes based on the user's dietary restrictions. For example, the suggestion unit can suggest recipes using appropriate ingredients, taking into account dietary restrictions such as vegetarianism or gluten-free diets. The suggestion unit can also refer to the user's dietary restrictions and suggest recipes that meet those restrictions. In this way, the suggestion unit can support the user's health by suggesting appropriate recipes that take the user's dietary restrictions into account. Specific content and criteria of dietary restrictions include, but are not limited to, calorie restrictions or restrictions on specific nutrients. Some or all of the above processing in the suggestion unit may be performed using, for example, AI, or not using AI. For example, the suggestion unit can input the user's dietary restriction data into a generating AI and have the generating AI suggest appropriate recipes.

[0092] The confirmation unit can estimate the user's emotions and adjust the display method of the food status based on the estimated user emotions. For example, if the user is stressed, the confirmation unit provides a simple and highly visible display method. For example, if the user is relaxed, the confirmation unit provides a display method that includes detailed information. Also, if the user is in a hurry, the confirmation unit can provide a concise display method. In this way, the confirmation unit can reduce user stress by adjusting the display method of the food status according to the user's emotions. 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 confirmation unit may be performed using AI, for example, or without AI. For example, the confirmation unit can input the user's emotion data into the generative AI and have the generative AI perform emotion estimation and adjustment of the display method.

[0093] The verification unit can explicitly display the expiration dates of food items in the refrigerator during verification. For example, the verification unit can highlight food items nearing their expiration date and encourage them to be used sooner. For example, the verification unit can display a warning for food items that have expired and encourage them to be discarded. The verification unit can also color-code food items nearing their expiration date to make them visually easier to understand. In this way, the verification unit can reduce food waste by highlighting the expiration dates of food items in the refrigerator. Specific methods and criteria for explicitly displaying expiration dates include, but are not limited to, display format and highlighting method. Some or all of the above processing in the verification unit may be performed using, for example, AI, or not using AI. For example, the verification unit can input expiration date data into a generating AI and have the generating AI execute the explicit display of expiration dates.

[0094] The verification unit can evaluate the frequency of use of ingredients during verification and display them preferentially. For example, the verification unit can prioritize the display of frequently used ingredients to manage them efficiently. For example, the verification unit can make less frequently used ingredients stand out to encourage their use. The verification unit can also analyze usage frequency and provide the optimal display method. In this way, the verification unit can efficiently manage ingredients by analyzing their usage frequency and displaying them preferentially. Specific methods and criteria for evaluating usage frequency include, but are not limited to, the number of uses and the duration of use. Some or all of the above processing in the verification unit may be performed using, for example, AI, or not using AI. For example, the verification unit can input ingredient usage frequency data into a generating AI and have the generating AI perform the evaluation and display of usage frequency.

[0095] The confirmation unit can estimate the user's emotions and adjust the frequency of notifications about the food status based on the estimated emotions. For example, if the user is stressed, the confirmation unit can reduce the notification frequency and provide only the necessary information. For example, if the user is relaxed, the confirmation unit can increase the notification frequency and provide detailed information. Also, if the user is in a hurry, the confirmation unit can minimize the notification frequency and provide concise information. In this way, the confirmation unit can reduce user stress by adjusting the frequency of notifications about the food status according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or 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 confirmation unit may be performed using AI, for example, or without AI. For example, the confirmation unit can input the user's emotion data into the generative AI and have the generative AI perform emotion estimation and adjustment of notification frequency.

[0096] The verification unit can send notifications at the optimal time based on the user's smartphone location information during verification. For example, if the user is at home, the verification unit can notify the user of the status of food in the refrigerator in real time. For example, if the user is out, the verification unit can notify only the necessary information, allowing the user to check the details later. The verification unit can also send notifications at the optimal time based on the user's location information. In this way, the verification unit can improve user convenience by sending notifications at the optimal time considering the user's smartphone location information. Specific methods for collecting and using location information include, but are not limited to, GPS data and privacy protection. Some or all of the above processing in the verification unit may be performed using, for example, AI, or not using AI. For example, the verification unit can input the user's location information data into a generating AI and have the generating AI execute a notification at the optimal time.

[0097] The verification unit can predict food consumption based on the user's family structure during verification. For example, the verification unit predicts food consumption based on family structure and displays an appropriate amount. For example, the verification unit predicts food consumption considering family structure and reduces waste. The verification unit can also predict food consumption based on family structure and provide an optimal management method. In this way, the verification unit can reduce waste by predicting food consumption considering the user's family structure. Specific methods for collecting and using family structure include, but are not limited to, user input and database structure. Some or all of the above processing in the verification unit may be performed using, for example, AI, or without AI. For example, the verification unit can input the user's family structure data into a generating AI and have the generating AI perform consumption prediction.

[0098] The recommendation system can estimate the user's emotions and adjust how recommended products are displayed based on those emotions. For example, if the user is stressed, the recommendation system can provide a simple and highly visible display. For example, if the user is relaxed, the recommendation system can provide a display that includes detailed information. Furthermore, if the user is in a hurry, the recommendation system can provide a concise display. In this way, the recommendation system can reduce user stress by adjusting how recommended products are displayed 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 recommendation system may be performed using AI, or not using AI. For example, the recommendation system can input user emotion data into a generative AI and have the generative AI perform emotion estimation and adjustment of the display method.

[0099] The recommendation system can recommend products that match the user's preferences based on their past purchase history. For example, the recommendation system can recommend products that match the user's preferences based on products the user has purchased in the past. For example, the recommendation system can recommend products from brands the user likes based on their past purchase history. The recommendation system can also analyze the user's past purchase history and recommend similar products. In this way, the recommendation system can improve user satisfaction by recommending products that match the user's preferences by referring to their past purchase history. Specific methods for collecting and using past purchase history include, but are not limited to, database structure and privacy protection. Some or all of the above processing in the recommendation system may be performed using, for example, AI, or not using AI. For example, the recommendation system can input the user's past purchase history data into a generating AI and have the generating AI recommend products that match the user's preferences.

[0100] The recommendation department can recommend highly reliable products based on product reviews and ratings. For example, the recommendation department can recommend highly-rated products based on product reviews. For example, the recommendation department can recommend highly-rated products that match the user's preferences. The recommendation department can also analyze product ratings and recommend highly reliable products. In this way, the recommendation department can improve user satisfaction by recommending highly reliable products that take product reviews and ratings into consideration. Specific methods and criteria for collecting reviews and ratings include, but are not limited to, review sites and rating criteria. Some or all of the above processing in the recommendation department may be performed using AI, for example, or not using AI. For example, the recommendation department can input product review and rating data into a generating AI and have the generating AI perform recommendations for highly reliable products.

[0101] The recommendation system can estimate the user's emotions and prioritize recommended products based on those emotions. For example, if the user is stressed, the recommendation system will prioritize recommending products that are easy to purchase. For example, if the user is relaxed, the recommendation system will prioritize recommending products that contain detailed information. Also, if the user is in a hurry, the recommendation system can prioritize recommending products that can be purchased quickly. In this way, the recommendation system can reduce user stress by prioritizing recommended products 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 recommendation system may be performed using AI or not. For example, the recommendation system can input user emotion data into a generative AI and have the generative AI perform emotion estimation and priority determination.

[0102] The recommendation system can recommend the most suitable products based on the user's budget. For example, the recommendation system can recommend products that fit within a price range based on the user's budget. For example, the recommendation system can recommend the most cost-effective product within the budget. The recommendation system can also recommend the most suitable products while considering the user's budget. In this way, the recommendation system can improve user satisfaction by recommending the most suitable products while considering the user's budget. Specific methods for collecting and using budgets include, but are not limited to, user input and database structure. Some or all of the above processes in the recommendation system may be performed using, for example, AI, or not using AI. For example, the recommendation system can input user budget data into a generating AI and have the generating AI perform the optimal product recommendation.

[0103] The recommendation system can evaluate the user's purchasing patterns and suggest efficient shopping routes during the recommendation process. For example, the recommendation system can suggest efficient shopping routes based on the user's purchasing patterns. For example, the recommendation system can analyze purchasing patterns and suggest the shortest route to shop. The recommendation system can also suggest efficient shopping routes considering the user's purchasing patterns. In this way, the recommendation system can improve shopping efficiency by analyzing the user's purchasing patterns and suggesting efficient shopping routes. Specific methods and criteria for analyzing purchasing patterns include, but are not limited to, purchase frequency and purchase timing. Some or all of the above processing in the recommendation system may be performed using, for example, AI, or not using AI. For example, the recommendation system can input user purchasing pattern data into a generating AI and have the generating AI suggest efficient shopping routes.

[0104] The ordering system can estimate the user's emotions and adjust the timing of the order based on those emotions. For example, if the user is stressed, the ordering system can delay the order to allow them to place the order in a relaxed state. For example, if the user is relaxed, the ordering system can speed up the order to complete it quickly. Also, if the user is in a hurry, the ordering system can optimize the timing of the order to allow for quick placement. In this way, the ordering system can reduce user stress by adjusting the timing of the order according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the ordering system may be performed using AI, or not using AI. For example, the ordering system can input user emotion data into a generative AI and have the generative AI perform emotion estimation and adjustment of order timing.

[0105] The ordering department can select the optimal ordering method by referring to the user's past order history when an order is placed. For example, the ordering department can select the optimal ordering method based on the user's past order history. For example, the ordering department can prioritize suggesting ordering methods preferred by the user based on past order history. The ordering department can also analyze the user's past order history and select the optimal ordering method. In this way, the ordering department can improve user satisfaction by selecting the optimal ordering method by referring to the user's past order history. Specific methods for collecting and using past order history include, but are not limited to, database structure and privacy protection. Some or all of the above processing in the ordering department may be performed using AI, for example, or not using AI. For example, the ordering department can input the user's past order history data into a generating AI and have the generating AI select the optimal ordering method.

[0106] The order department can select the most suitable delivery company based on delivery time at the time of order. For example, the order department can select the most suitable delivery company based on delivery time. For example, the order department can select the most suitable delivery company to match the user's desired delivery time. The order department can also select the most suitable delivery company considering delivery time. In this way, the order department can improve user satisfaction by selecting the most suitable delivery company considering delivery time. Specific evaluation methods and criteria for delivery time include, but are not limited to, the delivery company's schedule and delivery distance. Some or all of the above processing in the order department may be performed using AI, for example, or not using AI. For example, the order department can input delivery time data into a generating AI and have the generating AI perform the selection of the most suitable delivery company.

[0107] The order processing unit can estimate the user's emotions and adjust the order confirmation method based on the estimated emotions. For example, if the user is stressed, the order processing unit can provide a simple and highly visible confirmation method. For example, if the user is relaxed, the order processing unit can provide a confirmation method that includes detailed information. Also, if the user is in a hurry, the order processing unit can provide a concise confirmation method. In this way, the order processing unit can reduce user stress by adjusting the order confirmation method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the order processing unit may be performed using AI, for example, or not using AI. For example, the order processing unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation and adjustment of the confirmation method.

[0108] The order processing unit can suggest the most suitable payment option based on the user's payment method at the time of ordering. For example, the order processing unit can suggest the most suitable payment option based on the user's payment method. For example, the order processing unit can suggest a payment option preferred by the user, taking the payment method into consideration. The order processing unit can also suggest the most suitable payment option by referring to the user's payment method. In this way, the order processing unit can improve user satisfaction by suggesting the most suitable payment option considering the user's payment method. Specific methods for collecting and using payment methods include, but are not limited to, credit cards and electronic money. Some or all of the above processing in the order processing unit may be performed using, for example, AI, or not using AI. For example, the order processing unit can input the user's payment method data into a generating AI and have the generating AI suggest the most suitable payment option.

[0109] The order processing unit can select the optimal delivery route based on the user's address information at the time of order. For example, the order processing unit can select the optimal delivery route based on the user's address information. For example, the order processing unit can take the address information into consideration to ensure delivery via the shortest route. The order processing unit can also refer to the user's address information to select the optimal delivery route. This allows the order processing unit to improve delivery efficiency by selecting the optimal delivery route while considering the user's address information. Specific methods for collecting and using address information include, but are not limited to, user input and database structure. Some or all of the above-described processes in the order processing unit may be performed using, for example, AI, or not. For example, the order processing unit can input user address information data into a generating AI and have the generating AI select the optimal delivery route.

[0110] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0111] The refrigerator management system can also be equipped with a voice recognition unit. The voice recognition unit can recognize the user's voice commands and provide information about the ingredients in the refrigerator by voice. For example, if the user says, "Tell me what vegetables are in the refrigerator," the voice recognition unit will answer by voice the types and quantities of vegetables in the refrigerator. The voice recognition unit can also suggest recipes based on the user's voice commands. For example, if the user says, "Tell me a recipe for tonight's dinner," the voice recognition unit will suggest an appropriate recipe by voice based on the information about the ingredients in the refrigerator. Furthermore, the voice recognition unit can suggest purchasing ingredients based on the user's voice commands. For example, if the user says, "We're running low on milk, please buy some," the voice recognition unit will suggest purchasing milk and can automatically place an order if necessary. In this way, the refrigerator management system can improve user convenience by using voice recognition functionality.

[0112] The refrigerator management system can also include a health management unit. This unit can suggest ingredients and recipes based on the user's health status. For example, if a user is on a diet, the health management unit can suggest low-calorie ingredients and recipes. If a user needs to consume a specific nutrient, the health management unit can suggest ingredients and recipes rich in that nutrient. Furthermore, the health management unit can manage the expiration dates of ingredients based on the user's health status. For example, if a user needs to consume a particular ingredient, the unit can notify them when its expiration date is approaching. In this way, the refrigerator management system can support the user's health.

[0113] The refrigerator management system can also include an energy management unit. This unit can make suggestions for optimizing the refrigerator's energy consumption. For example, it can suggest improving cooling efficiency by optimizing the placement of food items inside the refrigerator. It can also monitor the frequency of refrigerator opening and closing and suggest ways to reduce unnecessary energy consumption. Furthermore, it can suggest reducing energy consumption by optimizing the refrigerator's temperature settings. In this way, the refrigerator management system can optimize energy consumption and support environmentally conscious operation.

[0114] The refrigerator management system can also include a food exchange function. This function can propose food exchanges between users. For example, it can suggest that users exchange surplus food with other users. Furthermore, it can provide a platform to facilitate food exchanges between nearby users. Additionally, it can manage exchanged food and record the exchange history. This allows the refrigerator management system to reduce food waste and promote the efficient use of food within the community.

[0115] The refrigerator management system can also include a food donation section. This section allows users to suggest donating surplus food items. For example, it could suggest donating food items nearing their expiration date. Furthermore, it can provide a platform for donating food to local food banks or charities. Additionally, it can manage donated food and record donation history. This allows the refrigerator management system to reduce food waste and promote social contribution.

[0116] The refrigerator management system can estimate the user's emotions and adjust how it suggests ingredients based on those emotions. For example, if the user is stressed, it can suggest easy-to-prepare, low-effort ingredients. If the user is relaxed, it can suggest ingredients that take time to cook. If the user is in a hurry, it can suggest ingredients that can be used immediately. In this way, the refrigerator management system can reduce user stress by adjusting how it suggests ingredients according to the user's emotions.

[0117] The refrigerator management system can estimate the user's emotions and adjust its recipe suggestions based on those emotions. For example, if the user is stressed, it can suggest easy and quick recipes. If the user is relaxed, it can suggest recipes that can be enjoyed at a leisurely pace. If the user is in a hurry, it can suggest recipes that can be prepared in a short amount of time. In this way, the refrigerator management system can reduce user stress by adjusting its recipe suggestions according to the user's emotions.

[0118] The refrigerator management system can estimate the user's emotions and suggest food storage methods based on those emotions. For example, if the user is stressed, it can suggest a simple and easy storage method. If the user is relaxed, it can suggest a method that takes time to store the food. If the user is in a hurry, it can suggest a method that allows for quick storage. In this way, the refrigerator management system can reduce user stress by suggesting food storage methods according to the user's emotions.

[0119] The refrigerator management system can estimate the user's emotions and suggest grocery purchasing methods based on those emotions. For example, if the user is stressed, it can suggest an easy and hassle-free purchasing method. If the user is relaxed, it can suggest a method that allows for more time to shop. If the user is in a hurry, it can suggest a method that allows for quick purchase. In this way, the refrigerator management system can reduce user stress by suggesting grocery purchasing methods according to the user's emotions.

[0120] The refrigerator management system can estimate the user's emotions and suggest ways to consume food based on those emotions. For example, if the user is stressed, it can suggest easy and hassle-free consumption methods. If the user is relaxed, it can suggest methods that allow for more time to consume the food. If the user is in a hurry, it can suggest methods that allow for quick consumption. In this way, the refrigerator management system can reduce user stress by suggesting ways to consume food according to the user's emotions.

[0121] The following briefly describes the processing flow for example form 2.

[0122] Step 1: The collection unit automatically scans and manages the food items inside the refrigerator. The collection unit uses cameras and sensors installed inside the refrigerator to acquire information such as the type and quantity of food items and their expiration dates. For example, the collection unit automatically scans information on vegetables, meat, condiments, etc., inside the refrigerator and registers it in the database. The collection unit can also constantly monitor the status of the food items inside the refrigerator and periodically scan and update the information. Step 2: The suggestion department proposes recipes that use ingredients in order of their expiration date, based on the ingredient information collected by the collection department. The suggestion department proposes recipes that prioritize the use of ingredients with the nearest expiration date, based on the acquired ingredient information. For example, the suggestion department proposes salad and soup recipes using vegetables that are nearing their expiration date. The suggestion department can also propose recipes that efficiently use ingredients that are nearing their expiration date. Step 3: The verification unit remotely checks the status of ingredients in the refrigerator based on the recipe suggested by the suggestion unit. The verification unit can check information such as the type and quantity of ingredients in the refrigerator and their expiration dates via a smartphone app. For example, it can check the status of ingredients in the refrigerator and purchase necessary ingredients even when away from home. The verification unit can also check the status of ingredients in the refrigerator in real time. Step 4: The recommendation team recommends the best place to buy and products based on the food information verified by the verification team. The recommendation team collects information on special offers at nearby supermarkets and prices at online stores to recommend the best place to buy and products. For example, it may recommend vegetables on sale at nearby supermarkets or seasonings that can be purchased cheaply at online stores. The recommendation team can also recommend the best products according to the user's purchasing intent. Step 5: The ordering department automatically places orders for products recommended by the recommendation department based on the user's purchase intent. When a user indicates their purchase intent through the smartphone app, the ordering department automatically places the order and delivers it to the specified address. For example, it can confirm the user's purchase intent and automatically order the necessary ingredients and seasonings. The ordering department can also adjust the order contents according to the user's purchase intent.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] Each of the multiple elements described above, including the collection unit, proposal unit, confirmation unit, recommendation unit, and ordering unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit acquires information about the food in the refrigerator using the camera 42 and sensors of the smart device 14, and this information is managed by the identification unit 290 of the data processing unit 12. The proposal unit is implemented by the identification unit 290 of the data processing unit 12 and proposes recipes based on the collected food information. The confirmation unit is implemented by the control unit 46A of the smart device 14 and checks the status of the food in the refrigerator via a smartphone app. The recommendation unit is implemented by the identification unit 290 of the data processing unit 12 and recommends the best place to buy and the best products. The ordering unit is implemented by the control unit 46A of the smart device 14 and automatically places an order according to the user's purchase intention. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0127] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0132] 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).

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] Each of the multiple elements described above, including the collection unit, proposal unit, confirmation unit, recommendation unit, and ordering unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit acquires information about the food in the refrigerator using the camera 42 and sensors of the smart glasses 214 and manages it with the identification unit 290 of the data processing unit 12. The proposal unit is implemented by the identification unit 290 of the data processing unit 12 and proposes recipes based on the collected food information. The confirmation unit is implemented by the control unit 46A of the smart glasses 214 and checks the status of the food in the refrigerator via a smartphone app. The recommendation unit is implemented by the identification unit 290 of the data processing unit 12 and recommends the best place to buy and the best products. The ordering unit is implemented by the control unit 46A of the smart glasses 214 and automatically places an order according to the user's purchase intention. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0143] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0144] 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.

[0145] 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.

[0146] 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.

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

[0148] 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).

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.).

[0155] 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.

[0156] 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.

[0157] 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.

[0158] Each of the multiple elements described above, including the collection unit, proposal unit, confirmation unit, recommendation unit, and ordering unit, is implemented by at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit acquires information about the food in the refrigerator using the camera 42 and sensors of the headset terminal 314 and manages it with the identification processing unit 290 of the data processing unit 12. The proposal unit is implemented by the identification processing unit 290 of the data processing unit 12 and proposes recipes based on the collected food information. The confirmation unit is implemented by the control unit 46A of the headset terminal 314 and checks the status of the food in the refrigerator via a smartphone app. The recommendation unit is implemented by the identification processing unit 290 of the data processing unit 12 and recommends the best place to buy and the best products. The ordering unit is implemented by the control unit 46A of the headset terminal 314 and automatically places an order according to the user's purchase intention. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0159] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0164] 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).

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.).

[0172] 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.

[0173] 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.

[0174] 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.

[0175] Each of the multiple elements described above, including the collection unit, proposal unit, confirmation unit, recommendation unit, and ordering unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the collection unit acquires information about the food in the refrigerator using the camera 42 and sensors of the robot 414 and manages it with the identification processing unit 290 of the data processing unit 12. The proposal unit is implemented by the identification processing unit 290 of the data processing unit 12 and proposes recipes based on the collected food information. The confirmation unit is implemented by the control unit 46A of the robot 414 and checks the status of the food in the refrigerator via a smartphone app. The recommendation unit is implemented by the identification processing unit 290 of the data processing unit 12 and recommends the best place to buy and the best products. The ordering unit is implemented by the control unit 46A of the robot 414 and automatically places an order according to the user's purchase intention. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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."

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] (Note 1) A collection unit that automatically scans and manages the food items inside the refrigerator, Based on the food information collected by the aforementioned collection unit, the proposal unit proposes recipes that can be used in order of the ingredients closest to their expiration date, and A confirmation unit remotely checks the status of ingredients in the refrigerator based on the recipe proposed by the aforementioned proposal unit, Based on the food ingredient information confirmed by the aforementioned verification unit, a recommendation unit recommends a place to buy and products, The system includes an ordering unit that automatically places orders for products recommended by the recommendation unit according to the user's purchase intent. A system characterized by the following features. (Note 2) The aforementioned collection unit is Information such as the type and quantity of food items and their expiration dates is obtained using at least one of the cameras and sensors installed inside the refrigerator. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned proposal section is, Based on the acquired ingredient information, we propose recipes that prioritize using ingredients with the nearest expiration date. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned verification unit is You can remotely check the status of food in your refrigerator via a smartphone app. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned recommendation department, We collect sale information from nearby supermarkets and price information from online stores to recommend where to buy and what products to purchase. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned ordering section is, The system confirms the user's purchase intent and automatically orders the necessary ingredients and seasonings. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is The system estimates the user's emotions and adjusts the frequency of scanning ingredients based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is When collecting ingredients, assess their preservation status based on the temperature and humidity inside the refrigerator. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When collecting ingredients, their shape and color are analyzed to determine their freshness. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is It estimates the user's emotions and determines the priority of ingredients to collect based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting ingredients, the system efficiently scans them based on their placement within the refrigerator. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is When collecting ingredients, identify newly added ingredients based on the user's purchase history. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned proposal section is, The system estimates the user's emotions and adjusts the recipe suggestion method based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned proposal section is, When suggesting recipes, the system will suggest recipes that match the user's preferences based on their past cooking history. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned proposal section is, When suggesting recipes, we propose healthy recipes based on the nutritional value of the ingredients. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned proposal section is, The system estimates the user's emotions and adjusts the difficulty level of recipes based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned proposal section is, When suggesting recipes, we will suggest safe recipes based on the user's allergy information. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned proposal section is, When suggesting recipes, the system will suggest appropriate recipes based on the user's dietary restrictions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned verification unit is The system estimates the user's emotions and adjusts how the ingredient status is displayed based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned verification unit is When checking, the expiration dates of the food items in the refrigerator should be clearly displayed. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned verification unit is During the review process, the frequency of use of ingredients is evaluated and displayed preferentially. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned verification unit is The system estimates the user's emotions and adjusts the frequency of notifications about the food availability based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned verification unit is During verification, notifications will be sent at the optimal time based on the user's smartphone location information. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned verification unit is During the verification process, the amount of food consumed is predicted based on the user's family structure. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned recommendation department, The system estimates the user's emotions and adjusts how recommended products are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned recommendation department, When making recommendations, the system suggests products that match the user's preferences based on their past purchase history. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned recommendation department, When making recommendations, we recommend reliable products based on product reviews and ratings. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned recommendation department, It estimates the user's emotions and prioritizes recommended products based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned recommendation department, When making recommendations, the system will suggest the most suitable products based on the user's budget. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned recommendation department, When making recommendations, the system evaluates the user's purchasing patterns and suggests an efficient shopping route. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned ordering section is, It estimates the user's emotions and adjusts the timing of orders based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned ordering section is, When an order is placed, the system selects the optimal ordering method by referring to the user's past order history. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned ordering section is, When you place an order, the most suitable shipping carrier will be selected based on the delivery time. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned ordering section is, The system estimates the user's emotions and adjusts the order confirmation process based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned ordering section is, When you place an order, we will suggest the best payment option based on your payment method. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned ordering section is, When an order is placed, the system selects the optimal delivery route based on the user's address information. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0195] 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 collection unit that automatically scans and manages the food items inside the refrigerator, Based on the food information collected by the aforementioned collection unit, the proposal unit proposes recipes that can be used in order of the ingredients closest to their expiration date, and A confirmation unit remotely checks the status of ingredients in the refrigerator based on the recipe proposed by the aforementioned proposal unit, Based on the food ingredient information confirmed by the aforementioned verification unit, a recommendation unit recommends a place to buy and products, The system includes an ordering unit that automatically places orders for products recommended by the recommendation unit according to the user's purchase intent. A system characterized by the following features.

2. The aforementioned collection unit is Information such as the type and quantity of food items and their expiration dates is obtained using at least one of the cameras and sensors installed inside the refrigerator. The system according to feature 1.

3. The aforementioned proposal section is, Based on the acquired ingredient information, we propose recipes that prioritize using ingredients with the nearest expiration date. The system according to feature 1.

4. The aforementioned verification unit is You can remotely check the status of food in your refrigerator via a smartphone app. The system according to feature 1.

5. The aforementioned recommendation department, We collect sale information from nearby supermarkets and price information from online stores to recommend where to buy and what products to purchase. The system according to feature 1.

6. The aforementioned ordering section is, The system confirms the user's purchase intent and automatically orders the necessary ingredients and seasonings. The system according to feature 1.

7. The aforementioned collection unit is The system estimates the user's emotions and adjusts the frequency of scanning ingredients based on those emotions. The system according to feature 1.

8. The aforementioned collection unit is When collecting ingredients, assess their preservation status based on the temperature and humidity inside the refrigerator. The system according to feature 1.

9. The aforementioned collection unit is When collecting ingredients, their shape and color are analyzed to determine their freshness. The system according to feature 1.

10. The aforementioned collection unit is It estimates the user's emotions and determines the priority of ingredients to collect based on the estimated user emotions. The system according to feature 1.

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