system

Abstract

The system according to this embodiment aims to efficiently manage ingredients in a refrigerator, suggest the optimal menu, and automatically procure the necessary ingredients. [Solution] The system according to the embodiment comprises a recognition unit, a suggestion unit, a procurement unit, and a confirmation unit. The recognition unit recognizes the food items in the refrigerator. The suggestion unit suggests a menu based on the food items recognized by the recognition unit. The procurement unit procures the necessary food items from an online supermarket based on the menu suggested by the suggestion unit. The confirmation unit checks the condition of the refrigerator and whether it has been opened or closed.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, it is troublesome to manage food ingredients in a refrigerator, propose recipes, and procure necessary food ingredients, and there are problems of food loss and duplicate purchases.

[0005] The system according to the embodiment aims to efficiently manage food ingredients in a refrigerator, propose an optimal recipe, and automatically procure necessary food ingredients.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a recognition unit, a suggestion unit, a procurement unit, and a confirmation unit. The recognition unit recognizes the food items inside the refrigerator. The suggestion unit suggests a menu based on the food items recognized by the recognition unit. The procurement unit procures the necessary food items from an online supermarket based on the menu suggested by the suggestion unit. The confirmation unit checks the condition of the refrigerator and whether it has been opened or closed. [Effects of the Invention]

[0007] The system according to this embodiment can efficiently manage ingredients in a refrigerator, suggest the optimal menu, and automatically procure the necessary ingredients. [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, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The refrigerator management system according to an embodiment of the present invention is a system that uses an AI camera mounted on the refrigerator to recognize the food inside the refrigerator, has a registered dietitian AI propose an optimal menu for each individual, and automatically procures the necessary ingredients from an online supermarket. The refrigerator management system uses an AI camera mounted on the refrigerator to recognize the food inside the refrigerator, and the registered dietitian AI proposes an optimal menu for each user based on health checkup results and pre-existing conditions. At this time, it considers the types of vegetables in the refrigerator and the expiration dates calculated backward from the purchase date to propose a menu that contributes to reducing food waste. Furthermore, if necessary ingredients are lacking, the registered dietitian AI automatically procures the ingredients from an online supermarket. This allows users to eat healthy meals without hassle. In addition, the status of the refrigerator and its opening and closing status can be checked using a smartphone app and can be shared with family members. This prevents duplicate purchases within the family and can also be used as a monitoring function for family members living far away. For example, it can save working couples and elderly people living alone the trouble of shopping and provide them with healthy meals. In this way, the refrigerator management system can propose an optimal menu according to the user's health condition, automatically procures the necessary ingredients, and checks the status of the refrigerator.

[0029] The refrigerator management system according to this embodiment comprises a recognition unit, a suggestion unit, a procurement unit, and a verification unit. The recognition unit recognizes food items inside the refrigerator. The recognition unit recognizes food items using, for example, an AI camera mounted on the refrigerator. The recognition unit can also recognize food items using barcode scanning or RFID tags. For example, the recognition unit recognizes food items inside the refrigerator using image recognition technology. When using barcode scanning, the recognition unit scans the barcode of the food item and recognizes it by comparing it with a database. When using RFID tags, the recognition unit reads the RFID tag attached to the food item and recognizes it. The suggestion unit suggests a menu based on the food items recognized by the recognition unit. The suggestion unit suggests a menu using, for example, a registered dietitian AI. The suggestion unit can suggest an optimal menu for each individual user based on health checkup results and pre-existing conditions. For example, the suggestion unit suggests a menu considering the user's blood pressure, blood sugar level, and allergy information. The suggestion unit can also suggest a menu considering the types of vegetables inside the refrigerator and their expiration dates calculated backward from the purchase date. For example, the proposal department considers the expiration dates of vegetables and proposes menus that contribute to reducing food waste. The procurement department procures the necessary ingredients from an online supermarket based on the menu proposed by the proposal department. The procurement department procures ingredients from a partner online supermarket, for example. The procurement department can automatically determine the timing and quantity of orders and arrange delivery. For example, the procurement department automatically orders the necessary ingredients from an online supermarket and arranges delivery. The verification department checks the status of the refrigerator and whether it has been opened or closed. The verification department can check the status of the refrigerator and whether it has been opened or closed, for example, using a smartphone app. The verification department can share the status of the refrigerator and whether it has been opened or closed with family members to prevent duplicate purchases. For example, the verification department shares the status of the ingredients in the refrigerator with family members via a smartphone app. The verification department can also be used as a monitoring function for family members living far away. For example, the verification department monitors whether the refrigerator has been opened or closed to check on the well-being of family members living far away. As a result, the refrigerator management system according to this embodiment can recognize the ingredients in the refrigerator, propose menus, automatically procure the necessary ingredients, and check the status of the refrigerator.

[0030] The recognition unit recognizes the food items inside the refrigerator. For example, the recognition unit uses an AI camera installed in the refrigerator to recognize the food items. The AI ​​camera is installed on the shelves or door inside the refrigerator and periodically takes images to determine the type and quantity of food items. By using image recognition technology, the AI ​​camera can analyze the shape, color, and labels of the food items to identify them. Furthermore, the AI ​​camera can improve the accuracy of food item recognition using deep learning algorithms. For example, the AI ​​camera can learn from past data and accurately recognize new food items or images from different angles. The recognition unit can also recognize food items using barcode scanning or RFID tags. When using barcode scanning, a barcode reader is installed on the refrigerator door or shelf, and by scanning the barcode when food items are placed in the refrigerator, the food item information is registered in the database. When using RFID tags, a reader inside the refrigerator reads the RFID tag attached to the food item and automatically recognizes the food item information. Since RFID tags can be read without contact, they can be accurately recognized regardless of where the food item is placed inside the refrigerator. As a result, the recognition unit can efficiently and accurately recognize the food items inside the refrigerator and register them in the database. Furthermore, the recognition unit can simultaneously register information such as the expiration date and purchase date of the ingredients, making it easier to manage the ingredients.

[0031] The suggestion unit proposes menus based on ingredients recognized by the recognition unit. For example, the suggestion unit can use a registered dietitian AI to propose menus. The registered dietitian AI can generate optimal menus considering the user's health condition and nutritional balance. Specifically, it takes the user's health checkup results, chronic illnesses, and allergy information as input and proposes appropriate ingredients and cooking methods based on that. For example, if the user has high blood pressure, it will propose a menu with reduced salt, and if their blood sugar is high, it will propose a low-carbohydrate menu. The suggestion unit can also propose menus considering the types of ingredients in the refrigerator and their expiration dates calculated backward from the purchase date. This reduces food waste and allows for the efficient use of ingredients. For example, if vegetables in the refrigerator are nearing their expiration date, it will prioritize suggesting dishes using those vegetables. Furthermore, the suggestion unit can learn the user's preferences and past eating history to propose menus tailored to each individual user. For example, it can propose new menus based on dishes and ingredients that the user has enjoyed eating in the past. In addition, the suggestion unit can propose special menus tailored to the season and events. For example, it can propose menus tailored to special days such as Christmas or birthdays, enriching the user's dining experience. This allows the suggestion department to comprehensively consider the user's health condition, preferences, and the contents of their refrigerator to propose the most suitable menu.

[0032] The Procurement Department procures the necessary ingredients from online supermarkets based on the menus proposed by the Proposal Department. For example, the Procurement Department procures ingredients from partner online supermarkets. These partner online supermarkets offer a wide selection of products and fast delivery services, allowing for the rapid procurement of ingredients that meet the user's needs. The Procurement Department automatically generates a list of necessary ingredients based on the proposed menus and places orders with the online supermarkets. The timing and quantity of orders, as well as delivery arrangements, are all handled automatically by the Procurement Department. For example, the Procurement Department considers the user's meal schedule and the inventory status in the refrigerator to order the necessary ingredients at the optimal time. The Procurement Department can also select appropriate ingredients considering the user's preferences and allergy information. For example, if a user is allergic to a particular ingredient, it will exclude that ingredient and suggest alternative ingredients. Furthermore, the Procurement Department automatically arranges delivery and can adjust the delivery date and time to meet the user's desired date and time. This allows users to procure the necessary ingredients without hassle and smoothly implement the proposed menus. In addition, the Procurement Department can utilize online supermarket sales information and coupons to procure ingredients at a reduced cost. This allows the procurement department to efficiently and economically source ingredients and support users' dietary needs.

[0033] The monitoring unit checks the status inside the refrigerator and its opening and closing status. For example, the monitoring unit can be used via a smartphone app to check the status inside the refrigerator and its opening and closing status. The smartphone app displays information acquired from the refrigerator's camera and sensors in real time, allowing the user to check the status of the food inside. For example, it displays information such as the type and quantity of food inside the refrigerator and its expiration date in a list, making it easy for the user to grasp the necessary information. It can also check the opening and closing status of the refrigerator, and can issue an alert if the refrigerator is left open or if abnormal opening and closing is detected. This prevents unnecessary energy consumption and helps maintain the freshness of food. Furthermore, the monitoring unit allows families to share the status inside the refrigerator and its opening and closing status, preventing duplicate purchases. For example, all family members can use the smartphone app to check the status of food inside the refrigerator in real time and know what food they need. This prevents unnecessary shopping and enables efficient food management. The monitoring unit can also be used as a monitoring function for family members living far away. For example, it can monitor the opening and closing status of the refrigerator to check on the well-being of family members living far away. If the refrigerator door remains untouched for an extended period or if abnormal opening and closing is detected, an alert can be issued to notify family members. This allows for monitoring the safety of family members living far away. The monitoring unit can then understand the refrigerator's contents in real time, supporting efficient food management and family safety.

[0034] The suggestion unit can optimize menus based on health checkup results and pre-existing medical conditions. For example, the suggestion unit optimizes menus by considering the user's blood pressure, blood sugar levels, and allergy information. The suggestion unit can propose nutritionally balanced menus according to the user's health condition. For example, if the user has high blood pressure, the suggestion unit will propose a menu with reduced salt. Also, if the user has high blood sugar levels, the suggestion unit can propose a low-carbohydrate menu. Furthermore, the suggestion unit can consider the user's allergy information and propose menus that do not contain allergens. For example, the suggestion unit will propose a menu that avoids ingredients the user is allergic to. In this way, the suggestion unit can propose the optimal menu according to the user's health condition. 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 health checkup results and pre-existing medical condition data into a generating AI and have the generating AI propose the optimal menu.

[0035] The suggestion unit can propose menus considering the types of vegetables in the refrigerator and their expiration dates calculated backward from the purchase date. For example, the suggestion unit can recognize the types of vegetables in the refrigerator and calculate their expiration dates backward from the purchase date. The suggestion unit can propose menus that prioritize the use of vegetables with approaching expiration dates. For example, the suggestion unit can propose dishes using vegetables with approaching expiration dates. The suggestion unit can also propose methods for preserving vegetables with further expiration dates. For example, the suggestion unit can propose methods for freezing vegetables with further expiration dates. In this way, the suggestion unit can propose menus that contribute to reducing food waste. 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 data on the types of vegetables in the refrigerator and the purchase date into a generating AI and have the generating AI propose menus that take expiration dates into consideration.

[0036] The procurement department can automatically procure necessary ingredients from online supermarkets. For example, the procurement department can automatically order any missing ingredients from online supermarkets based on menus proposed by the suggestion department. The procurement department can automatically determine the timing and quantity of orders, and arrange delivery. For example, the procurement department can automatically order the necessary ingredients from online supermarkets and arrange delivery. The procurement department can also select the most suitable ingredients according to the user's budget and preferences. For example, the procurement department can select high-quality ingredients within the user's budget. This allows the procurement department to save the user time and automatically procure the necessary ingredients. Some or all of the above processes in the procurement department may be performed using AI, for example, or not. For example, the procurement department can input menu data proposed by the suggestion department into a generating AI and have the generating AI perform the automatic procurement of necessary ingredients.

[0037] The confirmation unit can check the status inside the refrigerator and its opening and closing status using a smartphone app. For example, the confirmation unit can check the status of food items inside the refrigerator via the smartphone app. The confirmation unit can monitor the opening and closing status of the refrigerator and notify the smartphone app. For example, the confirmation unit can record the number of times and duration the refrigerator has been opened and display this information on the smartphone app. The confirmation unit can also check the expiration dates and inventory status of food items inside the refrigerator via the smartphone app. For example, the confirmation unit can notify the smartphone app of food items nearing their expiration date. This allows the user to check the status inside the refrigerator and its opening and closing status via the smartphone app. Some or all of the above-described processes in the confirmation unit may be performed using AI, for example, or without AI. For example, the confirmation unit can input refrigerator status data into a generating AI and have the generating AI execute the display on the smartphone app.

[0038] The confirmation unit can share the status of the refrigerator and its opening / closing status with family members, preventing duplicate purchases. For example, the confirmation unit can share the status of food items in the refrigerator with family members via a smartphone app. The confirmation unit can monitor the opening / closing status of the refrigerator and notify all family members. For example, the confirmation unit can record the number of times and duration the refrigerator is opened and share this information with all family members. The confirmation unit can also share the expiration dates and inventory status of food items in the refrigerator with family members. For example, the confirmation unit can notify all family members of food items that are nearing their expiration date. In this way, the confirmation unit can share the status of the refrigerator and its opening / closing status with family members, preventing duplicate purchases. Some or all of the above processes in the confirmation unit may be performed using AI, for example, or without AI. For example, the confirmation unit can input refrigerator status data into a generating AI and have the generating AI generate a display to share with all family members.

[0039] The recognition unit can evaluate the freshness and quality of ingredients upon recognition and propose the optimal storage method. For example, the recognition unit can evaluate the freshness of ingredients and propose freezing if the freshness has deteriorated. The recognition unit can evaluate the quality of ingredients and propose refrigeration if the quality is high. For example, the recognition unit can propose the optimal storage method according to the type of ingredient to prevent deterioration. In addition, the recognition unit can analyze the appearance, smell, and storage condition of ingredients to evaluate their freshness and quality. For example, the recognition unit can photograph the appearance of ingredients with a camera and evaluate their freshness. This allows the recognition unit to propose the optimal storage method according to the freshness and quality of the ingredients. Some or all of the above processing in the recognition unit may be performed using AI, for example, or without AI. For example, the recognition unit can input freshness and quality data of ingredients into a generating AI and have the generating AI propose the optimal storage method.

[0040] The recognition unit can calculate the nutritional value of ingredients when recognizing them and select ingredients according to the user's health condition. For example, the recognition unit can calculate the nutritional value of ingredients and prioritize selecting high-nutrient ingredients according to the user's health condition. The recognition unit can calculate the nutritional value of ingredients and select low-calorie ingredients according to the user's health condition. For example, the recognition unit can calculate the nutritional value of ingredients and select ingredients containing specific nutrients according to the user's health condition. In addition, the recognition unit can analyze food composition tables, calorie calculations, and nutrient content in order to calculate the nutritional value of ingredients. For example, the recognition unit calculates the nutritional value of ingredients and selects ingredients according to the user's health condition. In this way, the recognition unit can select ingredients according to the user's health condition. Some or all of the above processing in the recognition unit may be performed using AI, for example, or without AI. For example, the recognition unit can input the nutritional value data of ingredients into a generating AI and have the generating AI perform the selection of ingredients according to the user's health condition.

[0041] The recognition unit can improve its recognition accuracy by referring to the user's past purchase history when recognizing food items. For example, the recognition unit can refer to the user's past purchase history to improve the recognition accuracy of frequently purchased food items. The recognition unit can refer to the user's past purchase history to improve the recognition accuracy of food items of a specific brand. For example, the recognition unit can refer to the user's past purchase history to improve the recognition accuracy of seasonal food items. In addition, the recognition unit can analyze the purchase date and time, purchase frequency, and purchase quantity in order to refer to the user's past purchase history. For example, the recognition unit can refer to the user's past purchase history to improve its recognition accuracy. In this way, the recognition unit can improve its recognition accuracy by referring to the user's past purchase history. Some or all of the above processing in the recognition unit may be performed using AI, for example, or without AI. For example, the recognition unit can input the user's past purchase history data into a generating AI and have the generating AI perform the improvement of recognition accuracy.

[0042] The recognition unit can display recognition results while considering the user's frequency of using ingredients. For example, the recognition unit can prioritize displaying frequently used ingredients, taking into account the user's frequency of use. The recognition unit can also highlight less frequently used ingredients, taking into account the user's frequency of use. For example, the recognition unit can color-code ingredients according to their usage frequency, taking into account the user's frequency of use. Furthermore, the recognition unit can analyze usage frequency, interval, and quantity to consider the user's frequency of use. For example, the recognition unit can display recognition results while considering the user's frequency of use. As a result, the recognition unit can display recognition results that are appropriate to the user's frequency of use. Some or all of the above-described processes in the recognition unit may be performed using AI, for example, or without AI. For example, the recognition unit can input the user's ingredient usage frequency data into a generating AI and have the generating AI perform the display of the recognition results.

[0043] The suggestion unit can suggest the optimal menu by referring to the user's past meal history when suggesting a menu. For example, the suggestion unit can refer to the user's past meal history and suggest a balanced menu. The suggestion unit can refer to the user's past meal history and suggest a menu using the user's preferred ingredients. For example, the suggestion unit can refer to the user's past meal history and suggest a highly nutritious menu. In addition, the suggestion unit can analyze the date and time of meals, the content of meals, and the amount of food eaten in order to refer to the user's past meal history. For example, the suggestion unit can refer to the user's past meal history and suggest the optimal menu. In this way, the suggestion unit can refer to the user's past meal history and suggest the optimal menu. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not using AI. For example, the suggestion unit can input the user's past meal history data into a generating AI and have the generating AI perform the task of suggesting the optimal menu.

[0044] The suggestion unit can propose safe menus by taking into account the user's allergy information when suggesting menus. For example, the suggestion unit can propose menus that do not contain allergens, taking into account the user's allergy information. The suggestion unit can propose menus that use alternative ingredients, taking into account the user's allergy information. For example, the suggestion unit can propose menus that use ingredients with fewer allergens, taking into account the user's allergy information. In addition, the suggestion unit can analyze the type of allergen, the severity of the allergy, and past allergic reactions in order to take into account the user's allergy information. For example, the suggestion unit can propose safe menus, taking into account the user's allergy information. In this way, the suggestion unit can propose safe menus by taking into account the user's allergy information. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without using AI. For example, the suggestion unit can input the user's allergy information data into a generating AI and have the generating AI execute safe menu suggestions.

[0045] The suggestion unit can customize menu suggestions by taking into account the user's food preferences. For example, the suggestion unit can suggest menus that prioritize the user's favorite ingredients. The suggestion unit can suggest menus that avoid ingredients the user dislikes. For example, the suggestion unit can suggest menus that take into account the user's preferred cooking methods. Furthermore, the suggestion unit can analyze past preference data, survey results, and types of ingredients to consider the user's food preferences. For example, the suggestion unit can customize the suggestions by taking the user's food preferences into account. This allows the suggestion unit to suggest menus that match the user's food preferences. Some or all of the above processes in the suggestion unit may be performed using AI, for example, or not using AI. For example, the suggestion unit can input the user's food preference data into a generating AI and have the generating AI customize the suggestions.

[0046] The suggestion unit can propose the optimal cooking time when suggesting menus, taking into account the user's meal times. For example, the suggestion unit can suggest menus that can be prepared in a short time to match the user's meal times. The suggestion unit can also suggest menus that can be prepared over a longer period of time to match the user's meal times. For example, the suggestion unit can suggest menus with adjusted cooking times to match the user's meal times. Furthermore, the suggestion unit can analyze meal timing, cooking time, and meal frequency to take the user's meal times into consideration. For example, the suggestion unit can propose the optimal cooking time considering the user's meal times. In this way, the suggestion unit can propose the optimal cooking time according to the user's meal times. 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 meal time data into a generating AI and have the generating AI propose the optimal cooking time.

[0047] The procurement department can select the most suitable ingredients when procuring ingredients, taking into account the user's budget. For example, the procurement department can select high-quality ingredients within the user's budget. The procurement department can select cost-effective ingredients within the user's budget. For example, the procurement department can select ingredients to provide all the necessary ingredients within the user's budget. In addition, the procurement department can analyze monthly budgets, purchase unit prices, and expenditure history to take the user's budget into consideration. For example, the procurement department can select the most suitable ingredients considering the user's budget. This allows the procurement department to select the most suitable ingredients according to the user's budget. Some or all of the above processes in the procurement department may be performed using AI, for example, or not using AI. For example, the procurement department can input the user's budget data into a generating AI and have the generating AI select the most suitable ingredients.

[0048] The procurement department can prevent duplicate purchases by referring to the user's purchase history when procuring ingredients. For example, the procurement department can refer to the user's purchase history and remove ingredients that have already been purchased from the list. The procurement department can refer to the user's purchase history and warn about ingredients that may be duplicated. For example, the procurement department can refer to the user's purchase history and select only the necessary ingredients. In addition, the procurement department can analyze the purchase date and time, purchase frequency, and purchase quantity in order to refer to the user's purchase history. For example, the procurement department can refer to the user's purchase history and prevent duplicate purchases. In this way, the procurement department can prevent duplicate purchases by referring to the user's purchase history. Some or all of the above processes in the procurement department may be performed using AI, for example, or not using AI. For example, the procurement department can input the user's purchase history data into a generating AI and have the generating AI perform the task of preventing duplicate purchases.

[0049] The procurement department can select the most suitable online supermarket when procuring ingredients, taking into account the user's geographical location. For example, the procurement department can select the closest online supermarket based on the user's geographical location. The procurement department can also select an online supermarket with a short delivery time based on the user's geographical location. For example, the procurement department can select an online supermarket with low shipping costs based on the user's geographical location. Furthermore, the procurement department can analyze the user's address, delivery area, and the location of online supermarkets in order to take the user's geographical location into consideration. For example, the procurement department can select the most suitable online supermarket by taking the user's geographical location into consideration. This allows the procurement department to select the most suitable online supermarket based on the user's geographical location. Some or all of the above processes in the procurement department may be performed using AI, for example, or without AI. For example, the procurement department can input the user's geographical location data into a generating AI and have the generating AI select the most suitable online supermarket.

[0050] The procurement department can adjust the amount of ingredients procured when procuring ingredients, taking into account the user's frequency of ingredient use. For example, the procurement department can increase the amount of ingredients that are frequently used, taking into account the user's frequency of ingredient use. The procurement department can also decrease the amount of ingredients that are used less frequently, taking into account the user's frequency of ingredient use. For example, the procurement department can set the amount of ingredients procured according to the frequency of use, taking into account the user's frequency of ingredient use. In addition, the procurement department can analyze the number of uses, intervals between uses, and amounts used in order to take into account the user's frequency of ingredient use. For example, the procurement department can adjust the amount of ingredients procured, taking into account the user's frequency of ingredient use. In this way, the procurement department can adjust the amount of ingredients procured according to the user's frequency of ingredient use. Some or all of the above processes in the procurement department may be performed using AI, for example, or without using AI. For example, the procurement department can input user ingredient usage frequency data into a generating AI and have the generating AI perform the adjustment of the amount of ingredients procured.

[0051] The confirmation unit can highlight the expiration dates of food items when checking the contents of the refrigerator. For example, the confirmation unit can highlight food items nearing their expiration date in red. The confirmation unit can also display a warning for food items that have expired. For example, the confirmation unit can display food items far from their expiration date in green. Furthermore, the confirmation unit can change the color, font size, and display alerts to highlight the expiration dates. For example, the confirmation unit can display food items nearing their expiration date in red to alert the user. This makes it easier to manage expiration dates by highlighting the expiration dates of food items. 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 food expiration date data into a generating AI and have the generating AI execute the highlighting method.

[0052] The confirmation unit can display the frequency of use of ingredients when checking the contents of the refrigerator, thereby encouraging efficient use. For example, the confirmation unit can display frequently used ingredients in blue. The confirmation unit can display infrequently used ingredients in yellow. For example, the confirmation unit can use color coding according to the frequency of use to encourage efficient use. The confirmation unit can also analyze the number of uses, intervals between uses, and amounts used in order to display the frequency of use. For example, the confirmation unit can display frequently used ingredients in blue to encourage efficient use by the user. In this way, the confirmation unit can encourage efficient use of ingredients by displaying the frequency of use. 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 ingredient usage frequency data into a generating AI and have the generating AI determine the display method.

[0053] The confirmation unit can select the optimal display method by referring to the user's past usage history when checking the status inside the refrigerator. For example, the confirmation unit can refer to the user's past usage history and prioritize the display of frequently used ingredients. The confirmation unit can refer to the user's past usage history and highlight ingredients that are used infrequently. For example, the confirmation unit can refer to the user's past usage history and display ingredients in different colors according to their usage frequency. In addition, the confirmation unit can analyze the date and time of use, frequency of use, and quantity of use in order to refer to the user's past usage history. For example, the confirmation unit can refer to the user's past usage history and select the optimal display method. In this way, the confirmation unit can refer to the user's past usage history and select the optimal display method. 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 past usage history data into a generating AI and have the generating AI select the optimal display method.

[0054] The verification unit can select the optimal display method when checking the status inside the refrigerator, taking into account the user's device information. For example, if the user is using a smartphone, the verification unit can provide a display method that matches the screen size. If the user is using a tablet, the verification unit can provide a display method optimized for a larger screen. For example, if the user is using a smartwatch, the verification unit can provide a concise and highly visible display method. In addition, the verification unit can analyze the type of device, screen size, and operating method in order to take the user's device information into consideration. For example, the verification unit can select the optimal display method by taking the user's device information into consideration. This allows the verification unit to select the optimal display method based on the user's device information. Some or all of the above processing in the verification unit may be performed using AI, for example, or without using AI. For example, the verification unit can input the user's device information data into a generating AI and have the generating AI select the optimal display method.

[0055] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0056] A refrigerator management system can optimize the placement of food items based on the user's frequency of use. For example, frequently used items can be placed in easily accessible locations, while less frequently used items can be placed at the back. Furthermore, the placement of items can be color-coded according to their frequency of use. This allows the refrigerator management system to provide an optimal arrangement of food items based on the user's usage frequency.

[0057] The refrigerator management system can optimize the placement of food items by referring to the user's past food usage history. For example, it can place frequently used items in easily accessible locations and less frequently used items at the back. It can also color-code the placement of food items based on past usage history. In this way, the refrigerator management system can provide an optimal arrangement tailored to the user's past usage history.

[0058] The refrigerator management system can optimize the placement of food items, taking into account the expiration dates of the user's food. For example, it can place items with approaching expiration dates in easily accessible locations and items with later expiration dates at the back. It can also color-code the placement of items according to their expiration dates. This allows the refrigerator management system to provide the optimal placement of food items according to the user's food expiration dates.

[0059] The refrigerator management system can optimize the placement of food items based on the nutritional value of the user's food. For example, it can place high-nutrient items in easily accessible locations and low-nutrient items at the back. It can also color-code the placement of items according to their nutritional value. This allows the refrigerator management system to provide the optimal placement of food items based on the user's nutritional value.

[0060] The refrigerator management system can optimize the placement of food items, taking into account the user's food storage methods. For example, it can place items that need to be frozen in the freezer compartment and items that need to be refrigerated in the refrigerator compartment. It can also color-code the placement of items according to their storage method. This allows the refrigerator management system to provide the optimal placement of food items according to the user's storage methods.

[0061] The following briefly describes the processing flow for example form 1.

[0062] Step 1: The recognition unit recognizes the food items inside the refrigerator. The recognition unit can recognize food items using, for example, an AI camera installed in the refrigerator. Alternatively, the recognition unit can recognize food items using barcode scanning or RFID tags. For example, the recognition unit can recognize food items inside the refrigerator using image recognition technology. When using barcode scanning, the recognition unit scans the barcode of the food item and matches it with a database to recognize the food item. When using RFID tags, the recognition unit reads the RFID tag attached to the food item to recognize the food item. Step 2: The suggestion unit proposes a menu based on the ingredients recognized by the recognition unit. The suggestion unit can propose a menu using, for example, a registered dietitian AI. The suggestion unit can propose a menu that is optimal for each individual user based on health checkup results and pre-existing conditions. For example, the suggestion unit can propose a menu that takes into account the user's blood pressure, blood sugar levels, and allergy information. The suggestion unit can also propose a menu that takes into account the types of vegetables in the refrigerator and their expiration dates calculated backward from the purchase date. For example, the suggestion unit can propose a menu that takes into account the expiration dates of vegetables and contributes to reducing food waste. Step 3: The Procurement Department procures the necessary ingredients from an online supermarket based on the menu proposed by the Proposal Department. For example, the Procurement Department procures ingredients from a partner online supermarket. The Procurement Department can automatically determine the timing and quantity of orders, and arrange delivery. For example, the Procurement Department automatically orders the necessary ingredients from an online supermarket and arranges delivery. Step 4: The monitoring unit checks the status inside the refrigerator and whether it has been opened or closed. The monitoring unit can check the status inside the refrigerator and whether it has been opened or closed, for example, using a smartphone app. The monitoring unit can share the status inside the refrigerator and whether it has been opened or closed with family members, preventing duplicate purchases. For example, the monitoring unit can share the status of food in the refrigerator with family members via a smartphone app. The monitoring unit can also be used as a monitoring function for family members living far away. For example, the monitoring unit can monitor whether the refrigerator has been opened or closed to check on the well-being of family members living far away.

[0063] (Example of form 2) The refrigerator management system according to an embodiment of the present invention is a system that uses an AI camera mounted on the refrigerator to recognize the food inside the refrigerator, has a registered dietitian AI propose an optimal menu for each individual, and automatically procures the necessary ingredients from an online supermarket. The refrigerator management system uses an AI camera mounted on the refrigerator to recognize the food inside the refrigerator, and the registered dietitian AI proposes an optimal menu for each user based on health checkup results and pre-existing conditions. At this time, it considers the types of vegetables in the refrigerator and the expiration dates calculated backward from the purchase date to propose a menu that contributes to reducing food waste. Furthermore, if necessary ingredients are lacking, the registered dietitian AI automatically procures the ingredients from an online supermarket. This allows users to eat healthy meals without hassle. In addition, the status of the refrigerator and its opening and closing status can be checked using a smartphone app and can be shared with family members. This prevents duplicate purchases within the family and can also be used as a monitoring function for family members living far away. For example, it can save working couples and elderly people living alone the trouble of shopping and provide them with healthy meals. In this way, the refrigerator management system can propose an optimal menu according to the user's health condition, automatically procures the necessary ingredients, and checks the status of the refrigerator.

[0064] The refrigerator management system according to this embodiment comprises a recognition unit, a suggestion unit, a procurement unit, and a verification unit. The recognition unit recognizes food items inside the refrigerator. The recognition unit recognizes food items using, for example, an AI camera mounted on the refrigerator. The recognition unit can also recognize food items using barcode scanning or RFID tags. For example, the recognition unit recognizes food items inside the refrigerator using image recognition technology. When using barcode scanning, the recognition unit scans the barcode of the food item and recognizes it by comparing it with a database. When using RFID tags, the recognition unit reads the RFID tag attached to the food item and recognizes it. The suggestion unit suggests a menu based on the food items recognized by the recognition unit. The suggestion unit suggests a menu using, for example, a registered dietitian AI. The suggestion unit can suggest an optimal menu for each individual user based on health checkup results and pre-existing conditions. For example, the suggestion unit suggests a menu considering the user's blood pressure, blood sugar level, and allergy information. The suggestion unit can also suggest a menu considering the types of vegetables inside the refrigerator and their expiration dates calculated backward from the purchase date. For example, the proposal department considers the expiration dates of vegetables and proposes menus that contribute to reducing food waste. The procurement department procures the necessary ingredients from an online supermarket based on the menu proposed by the proposal department. The procurement department procures ingredients from a partner online supermarket, for example. The procurement department can automatically determine the timing and quantity of orders and arrange delivery. For example, the procurement department automatically orders the necessary ingredients from an online supermarket and arranges delivery. The verification department checks the status of the refrigerator and whether it has been opened or closed. The verification department can check the status of the refrigerator and whether it has been opened or closed, for example, using a smartphone app. The verification department can share the status of the refrigerator and whether it has been opened or closed with family members to prevent duplicate purchases. For example, the verification department shares the status of the ingredients in the refrigerator with family members via a smartphone app. The verification department can also be used as a monitoring function for family members living far away. For example, the verification department monitors whether the refrigerator has been opened or closed to check on the well-being of family members living far away. As a result, the refrigerator management system according to this embodiment can recognize the ingredients in the refrigerator, propose menus, automatically procure the necessary ingredients, and check the status of the refrigerator.

[0065] The recognition unit recognizes the food items inside the refrigerator. For example, the recognition unit uses an AI camera installed in the refrigerator to recognize the food items. The AI ​​camera is installed on the shelves or door inside the refrigerator and periodically takes images to determine the type and quantity of food items. By using image recognition technology, the AI ​​camera can analyze the shape, color, and labels of the food items to identify them. Furthermore, the AI ​​camera can improve the accuracy of food item recognition using deep learning algorithms. For example, the AI ​​camera can learn from past data and accurately recognize new food items or images from different angles. The recognition unit can also recognize food items using barcode scanning or RFID tags. When using barcode scanning, a barcode reader is installed on the refrigerator door or shelf, and by scanning the barcode when food items are placed in the refrigerator, the food item information is registered in the database. When using RFID tags, a reader inside the refrigerator reads the RFID tag attached to the food item and automatically recognizes the food item information. Since RFID tags can be read without contact, they can be accurately recognized regardless of where the food item is placed inside the refrigerator. As a result, the recognition unit can efficiently and accurately recognize the food items inside the refrigerator and register them in the database. Furthermore, the recognition unit can simultaneously register information such as the expiration date and purchase date of the ingredients, making it easier to manage the ingredients.

[0066] The suggestion unit proposes menus based on ingredients recognized by the recognition unit. For example, the suggestion unit can use a registered dietitian AI to propose menus. The registered dietitian AI can generate optimal menus considering the user's health condition and nutritional balance. Specifically, it takes the user's health checkup results, chronic illnesses, and allergy information as input and proposes appropriate ingredients and cooking methods based on that. For example, if the user has high blood pressure, it will propose a menu with reduced salt, and if their blood sugar is high, it will propose a low-carbohydrate menu. The suggestion unit can also propose menus considering the types of ingredients in the refrigerator and their expiration dates calculated backward from the purchase date. This reduces food waste and allows for the efficient use of ingredients. For example, if vegetables in the refrigerator are nearing their expiration date, it will prioritize suggesting dishes using those vegetables. Furthermore, the suggestion unit can learn the user's preferences and past eating history to propose menus tailored to each individual user. For example, it can propose new menus based on dishes and ingredients that the user has enjoyed eating in the past. In addition, the suggestion unit can propose special menus tailored to the season and events. For example, it can propose menus tailored to special days such as Christmas or birthdays, enriching the user's dining experience. This allows the suggestion department to comprehensively consider the user's health condition, preferences, and the contents of their refrigerator to propose the most suitable menu.

[0067] The Procurement Department procures the necessary ingredients from online supermarkets based on the menus proposed by the Proposal Department. For example, the Procurement Department procures ingredients from partner online supermarkets. These partner online supermarkets offer a wide selection of products and fast delivery services, allowing for the rapid procurement of ingredients that meet the user's needs. The Procurement Department automatically generates a list of necessary ingredients based on the proposed menus and places orders with the online supermarkets. The timing and quantity of orders, as well as delivery arrangements, are all handled automatically by the Procurement Department. For example, the Procurement Department considers the user's meal schedule and the inventory status in the refrigerator to order the necessary ingredients at the optimal time. The Procurement Department can also select appropriate ingredients considering the user's preferences and allergy information. For example, if a user is allergic to a particular ingredient, it will exclude that ingredient and suggest alternative ingredients. Furthermore, the Procurement Department automatically arranges delivery and can adjust the delivery date and time to meet the user's desired date and time. This allows users to procure the necessary ingredients without hassle and smoothly implement the proposed menus. In addition, the Procurement Department can utilize online supermarket sales information and coupons to procure ingredients at a reduced cost. This allows the procurement department to efficiently and economically source ingredients and support users' dietary needs.

[0068] The monitoring unit checks the status inside the refrigerator and its opening and closing status. For example, the monitoring unit can be used via a smartphone app to check the status inside the refrigerator and its opening and closing status. The smartphone app displays information acquired from the refrigerator's camera and sensors in real time, allowing the user to check the status of the food inside. For example, it displays information such as the type and quantity of food inside the refrigerator and its expiration date in a list, making it easy for the user to grasp the necessary information. It can also check the opening and closing status of the refrigerator, and can issue an alert if the refrigerator is left open or if abnormal opening and closing is detected. This prevents unnecessary energy consumption and helps maintain the freshness of food. Furthermore, the monitoring unit allows families to share the status inside the refrigerator and its opening and closing status, preventing duplicate purchases. For example, all family members can use the smartphone app to check the status of food inside the refrigerator in real time and know what food they need. This prevents unnecessary shopping and enables efficient food management. The monitoring unit can also be used as a monitoring function for family members living far away. For example, it can monitor the opening and closing status of the refrigerator to check on the well-being of family members living far away. If the refrigerator door remains untouched for an extended period or if abnormal opening and closing is detected, an alert can be issued to notify family members. This allows for monitoring the safety of family members living far away. The monitoring unit can then understand the refrigerator's contents in real time, supporting efficient food management and family safety.

[0069] The suggestion unit can optimize menus based on health checkup results and pre-existing medical conditions. For example, the suggestion unit optimizes menus by considering the user's blood pressure, blood sugar levels, and allergy information. The suggestion unit can propose nutritionally balanced menus according to the user's health condition. For example, if the user has high blood pressure, the suggestion unit will propose a menu with reduced salt. Also, if the user has high blood sugar levels, the suggestion unit can propose a low-carbohydrate menu. Furthermore, the suggestion unit can consider the user's allergy information and propose menus that do not contain allergens. For example, the suggestion unit will propose a menu that avoids ingredients the user is allergic to. In this way, the suggestion unit can propose the optimal menu according to the user's health condition. 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 health checkup results and pre-existing medical condition data into a generating AI and have the generating AI propose the optimal menu.

[0070] The suggestion unit can propose menus considering the types of vegetables in the refrigerator and their expiration dates calculated backward from the purchase date. For example, the suggestion unit can recognize the types of vegetables in the refrigerator and calculate their expiration dates backward from the purchase date. The suggestion unit can propose menus that prioritize the use of vegetables with approaching expiration dates. For example, the suggestion unit can propose dishes using vegetables with approaching expiration dates. The suggestion unit can also propose methods for preserving vegetables with further expiration dates. For example, the suggestion unit can propose methods for freezing vegetables with further expiration dates. In this way, the suggestion unit can propose menus that contribute to reducing food waste. 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 data on the types of vegetables in the refrigerator and the purchase date into a generating AI and have the generating AI propose menus that take expiration dates into consideration.

[0071] The procurement department can automatically procure necessary ingredients from online supermarkets. For example, the procurement department can automatically order any missing ingredients from online supermarkets based on menus proposed by the suggestion department. The procurement department can automatically determine the timing and quantity of orders, and arrange delivery. For example, the procurement department can automatically order the necessary ingredients from online supermarkets and arrange delivery. The procurement department can also select the most suitable ingredients according to the user's budget and preferences. For example, the procurement department can select high-quality ingredients within the user's budget. This allows the procurement department to save the user time and automatically procure the necessary ingredients. Some or all of the above processes in the procurement department may be performed using AI, for example, or not. For example, the procurement department can input menu data proposed by the suggestion department into a generating AI and have the generating AI perform the automatic procurement of necessary ingredients.

[0072] The confirmation unit can check the status inside the refrigerator and its opening and closing status using a smartphone app. For example, the confirmation unit can check the status of food items inside the refrigerator via the smartphone app. The confirmation unit can monitor the opening and closing status of the refrigerator and notify the smartphone app. For example, the confirmation unit can record the number of times and duration the refrigerator has been opened and display this information on the smartphone app. The confirmation unit can also check the expiration dates and inventory status of food items inside the refrigerator via the smartphone app. For example, the confirmation unit can notify the smartphone app of food items nearing their expiration date. This allows the user to check the status inside the refrigerator and its opening and closing status via the smartphone app. Some or all of the above-described processes in the confirmation unit may be performed using AI, for example, or without AI. For example, the confirmation unit can input refrigerator status data into a generating AI and have the generating AI execute the display on the smartphone app.

[0073] The confirmation unit can share the status of the refrigerator and its opening / closing status with family members, preventing duplicate purchases. For example, the confirmation unit can share the status of food items in the refrigerator with family members via a smartphone app. The confirmation unit can monitor the opening / closing status of the refrigerator and notify all family members. For example, the confirmation unit can record the number of times and duration the refrigerator is opened and share this information with all family members. The confirmation unit can also share the expiration dates and inventory status of food items in the refrigerator with family members. For example, the confirmation unit can notify all family members of food items that are nearing their expiration date. In this way, the confirmation unit can share the status of the refrigerator and its opening / closing status with family members, preventing duplicate purchases. Some or all of the above processes in the confirmation unit may be performed using AI, for example, or without AI. For example, the confirmation unit can input refrigerator status data into a generating AI and have the generating AI generate a display to share with all family members.

[0074] The monitoring unit can be used as a monitoring function for family members living far away. For example, the monitoring unit can monitor the opening and closing status of the refrigerator to check on the well-being of family members living far away. The monitoring unit can record the number of times and duration the refrigerator is opened and notify family members living far away. For example, if the refrigerator is not opened for a certain period of time, the monitoring unit can send a warning to family members living far away. The monitoring unit can also share the expiration dates and inventory status of food items in the refrigerator with family members living far away. For example, the monitoring unit can notify family members living far away of food items that are nearing their expiration date. Thus, the monitoring unit can be used as a monitoring function for family members living far away. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the monitoring unit may be performed using AI, for example, or without AI. For example, the confirmation unit can input data on the refrigerator's opening and closing status into a generating AI, which can then execute a notification to family members living far away.

[0075] The recognition unit can estimate the user's emotions and adjust the accuracy of food recognition based on the estimated emotions. For example, if the user is stressed, the recognition unit can increase the accuracy of food recognition and reduce misrecognition. If the user is relaxed, the recognition unit can maintain normal accuracy and prioritize processing speed. For example, if the user is in a hurry, the recognition unit can increase the accuracy of food recognition and provide recognition results quickly. The recognition unit can also perform facial recognition, voice analysis, and behavioral pattern analysis to estimate the user's emotions. For example, the recognition unit can capture the user's facial expressions with a camera and estimate emotions using an emotion estimation algorithm. This allows the recognition unit to adjust the accuracy of food recognition according to the user's emotions. Some or all of the above processing in the recognition unit may be performed using AI, for example, or without AI. For example, the recognition unit can input user emotion data into a generating AI and have the generating AI perform the adjustment of food recognition accuracy.

[0076] The recognition unit can evaluate the freshness and quality of ingredients upon recognition and propose the optimal storage method. For example, the recognition unit can evaluate the freshness of ingredients and propose freezing if the freshness has deteriorated. The recognition unit can evaluate the quality of ingredients and propose refrigeration if the quality is high. For example, the recognition unit can propose the optimal storage method according to the type of ingredient to prevent deterioration. In addition, the recognition unit can analyze the appearance, smell, and storage condition of ingredients to evaluate their freshness and quality. For example, the recognition unit can photograph the appearance of ingredients with a camera and evaluate their freshness. This allows the recognition unit to propose the optimal storage method according to the freshness and quality of the ingredients. Some or all of the above processing in the recognition unit may be performed using AI, for example, or without AI. For example, the recognition unit can input freshness and quality data of ingredients into a generating AI and have the generating AI propose the optimal storage method.

[0077] The recognition unit can calculate the nutritional value of ingredients when recognizing them and select ingredients according to the user's health condition. For example, the recognition unit can calculate the nutritional value of ingredients and prioritize selecting high-nutrient ingredients according to the user's health condition. The recognition unit can calculate the nutritional value of ingredients and select low-calorie ingredients according to the user's health condition. For example, the recognition unit can calculate the nutritional value of ingredients and select ingredients containing specific nutrients according to the user's health condition. In addition, the recognition unit can analyze food composition tables, calorie calculations, and nutrient content in order to calculate the nutritional value of ingredients. For example, the recognition unit calculates the nutritional value of ingredients and selects ingredients according to the user's health condition. In this way, the recognition unit can select ingredients according to the user's health condition. Some or all of the above processing in the recognition unit may be performed using AI, for example, or without AI. For example, the recognition unit can input the nutritional value data of ingredients into a generating AI and have the generating AI perform the selection of ingredients according to the user's health condition.

[0078] The recognition unit can estimate the user's emotions and determine the priority of food recognition based on the estimated emotions. For example, if the user is stressed, the recognition unit will prioritize recognizing important foods. If the user is relaxed, the recognition unit can recognize all foods equally. For example, if the user is in a hurry, the recognition unit will prioritize recognizing foods that are nearing their expiration date. The recognition unit can also perform facial recognition, voice analysis, and behavioral pattern analysis to estimate the user's emotions. For example, the recognition unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. This allows the recognition unit to determine the priority of food recognition according to the user's emotions. Some or all of the above processing in the recognition unit may be performed using AI, for example, or without AI. For example, the recognition unit can input the user's emotion data into a generating AI and have the generating AI determine the priority of food recognition.

[0079] The recognition unit can improve its recognition accuracy by referring to the user's past purchase history when recognizing food items. For example, the recognition unit can refer to the user's past purchase history to improve the recognition accuracy of frequently purchased food items. The recognition unit can refer to the user's past purchase history to improve the recognition accuracy of food items of a specific brand. For example, the recognition unit can refer to the user's past purchase history to improve the recognition accuracy of seasonal food items. In addition, the recognition unit can analyze the purchase date and time, purchase frequency, and purchase quantity in order to refer to the user's past purchase history. For example, the recognition unit can refer to the user's past purchase history to improve its recognition accuracy. In this way, the recognition unit can improve its recognition accuracy by referring to the user's past purchase history. Some or all of the above processing in the recognition unit may be performed using AI, for example, or without AI. For example, the recognition unit can input the user's past purchase history data into a generating AI and have the generating AI perform the improvement of recognition accuracy.

[0080] The recognition unit can display recognition results while considering the user's frequency of using ingredients. For example, the recognition unit can prioritize displaying frequently used ingredients, taking into account the user's frequency of use. The recognition unit can also highlight less frequently used ingredients, taking into account the user's frequency of use. For example, the recognition unit can color-code ingredients according to their usage frequency, taking into account the user's frequency of use. Furthermore, the recognition unit can analyze usage frequency, interval, and quantity to consider the user's frequency of use. For example, the recognition unit can display recognition results while considering the user's frequency of use. As a result, the recognition unit can display recognition results that are appropriate to the user's frequency of use. Some or all of the above-described processes in the recognition unit may be performed using AI, for example, or without AI. For example, the recognition unit can input the user's ingredient usage frequency data into a generating AI and have the generating AI perform the display of the recognition results.

[0081] The suggestion unit can estimate the user's emotions and adjust its menu suggestion method based on the estimated emotions. For example, if the user is stressed, the suggestion unit can suggest a simple and easy-to-prepare menu. If the user is relaxed, the suggestion unit can suggest a menu that can be enjoyed over time. For example, if the user is in a hurry, the suggestion unit can suggest a menu that can be prepared in a short time. The suggestion unit can also perform facial recognition, voice analysis, and behavioral pattern analysis to estimate the user's emotions. For example, the suggestion unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. This allows the suggestion unit to adjust its menu suggestion method according to the user's emotions. 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 emotion data into a generating AI and have the generating AI adjust the menu suggestion method.

[0082] The suggestion unit can suggest the optimal menu by referring to the user's past meal history when suggesting a menu. For example, the suggestion unit can refer to the user's past meal history and suggest a balanced menu. The suggestion unit can refer to the user's past meal history and suggest a menu using the user's preferred ingredients. For example, the suggestion unit can refer to the user's past meal history and suggest a highly nutritious menu. In addition, the suggestion unit can analyze the date and time of meals, the content of meals, and the amount of food eaten in order to refer to the user's past meal history. For example, the suggestion unit can refer to the user's past meal history and suggest the optimal menu. In this way, the suggestion unit can refer to the user's past meal history and suggest the optimal menu. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not using AI. For example, the suggestion unit can input the user's past meal history data into a generating AI and have the generating AI perform the task of suggesting the optimal menu.

[0083] The suggestion unit can propose safe menus by taking into account the user's allergy information when suggesting menus. For example, the suggestion unit can propose menus that do not contain allergens, taking into account the user's allergy information. The suggestion unit can propose menus that use alternative ingredients, taking into account the user's allergy information. For example, the suggestion unit can propose menus that use ingredients with fewer allergens, taking into account the user's allergy information. In addition, the suggestion unit can analyze the type of allergen, the severity of the allergy, and past allergic reactions in order to take into account the user's allergy information. For example, the suggestion unit can propose safe menus, taking into account the user's allergy information. In this way, the suggestion unit can propose safe menus by taking into account the user's allergy information. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without using AI. For example, the suggestion unit can input the user's allergy information data into a generating AI and have the generating AI execute safe menu suggestions.

[0084] The suggestion unit can estimate the user's emotions and determine the priority of menus based on the estimated emotions. For example, if the user is stressed, the suggestion unit will prioritize suggesting easy and quick menus. If the user is relaxed, the suggestion unit can prioritize suggesting menus that can be enjoyed over time. For example, if the user is in a hurry, the suggestion unit will prioritize suggesting menus that can be prepared quickly. The suggestion unit can also perform facial recognition, voice analysis, and behavioral pattern analysis to estimate the user's emotions. For example, the suggestion unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. This allows the suggestion unit to determine the priority of menus according to the user's emotions. 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 emotion data into a generating AI and have the generating AI determine the menu priorities.

[0085] The suggestion unit can customize menu suggestions by taking into account the user's food preferences. For example, the suggestion unit can suggest menus that prioritize the user's favorite ingredients. The suggestion unit can suggest menus that avoid ingredients the user dislikes. For example, the suggestion unit can suggest menus that take into account the user's preferred cooking methods. Furthermore, the suggestion unit can analyze past preference data, survey results, and types of ingredients to consider the user's food preferences. For example, the suggestion unit can customize the suggestions by taking the user's food preferences into account. This allows the suggestion unit to suggest menus that match the user's food preferences. Some or all of the above processes in the suggestion unit may be performed using AI, for example, or not using AI. For example, the suggestion unit can input the user's food preference data into a generating AI and have the generating AI customize the suggestions.

[0086] The suggestion unit can propose the optimal cooking time when suggesting menus, taking into account the user's meal times. For example, the suggestion unit can suggest menus that can be prepared in a short time to match the user's meal times. The suggestion unit can also suggest menus that can be prepared over a longer period of time to match the user's meal times. For example, the suggestion unit can suggest menus with adjusted cooking times to match the user's meal times. Furthermore, the suggestion unit can analyze meal timing, cooking time, and meal frequency to take the user's meal times into consideration. For example, the suggestion unit can propose the optimal cooking time considering the user's meal times. In this way, the suggestion unit can propose the optimal cooking time according to the user's meal times. 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 meal time data into a generating AI and have the generating AI propose the optimal cooking time.

[0087] The procurement unit can estimate the user's emotions and adjust the timing of ingredient procurement based on the estimated emotions. For example, if the user is stressed, the procurement unit can expedite ingredient procurement. If the user is relaxed, the procurement unit can maintain a normal timing for ingredient procurement. For example, if the user is in a hurry, the procurement unit can expedite ingredient procurement. The procurement unit can also perform facial recognition, voice analysis, and behavioral pattern analysis to estimate the user's emotions. For example, the procurement unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. This allows the procurement unit to adjust the timing of ingredient procurement according to the user's emotions. Some or all of the above processes in the procurement unit may be performed using AI, for example, or without AI. For example, the procurement unit can input user emotion data into a generating AI and have the generating AI adjust the timing of ingredient procurement.

[0088] The procurement department can select the most suitable ingredients when procuring ingredients, taking into account the user's budget. For example, the procurement department can select high-quality ingredients within the user's budget. The procurement department can select cost-effective ingredients within the user's budget. For example, the procurement department can select ingredients to provide all the necessary ingredients within the user's budget. In addition, the procurement department can analyze monthly budgets, purchase unit prices, and expenditure history to take the user's budget into consideration. For example, the procurement department can select the most suitable ingredients considering the user's budget. This allows the procurement department to select the most suitable ingredients according to the user's budget. Some or all of the above processes in the procurement department may be performed using AI, for example, or not using AI. For example, the procurement department can input the user's budget data into a generating AI and have the generating AI select the most suitable ingredients.

[0089] The procurement department can prevent duplicate purchases by referring to the user's purchase history when procuring ingredients. For example, the procurement department can refer to the user's purchase history and remove ingredients that have already been purchased from the list. The procurement department can refer to the user's purchase history and warn about ingredients that may be duplicated. For example, the procurement department can refer to the user's purchase history and select only the necessary ingredients. In addition, the procurement department can analyze the purchase date and time, purchase frequency, and purchase quantity in order to refer to the user's purchase history. For example, the procurement department can refer to the user's purchase history and prevent duplicate purchases. In this way, the procurement department can prevent duplicate purchases by referring to the user's purchase history. Some or all of the above processes in the procurement department may be performed using AI, for example, or not using AI. For example, the procurement department can input the user's purchase history data into a generating AI and have the generating AI perform the task of preventing duplicate purchases.

[0090] The procurement department can estimate the user's emotions and determine the priority of ingredient procurement based on the estimated emotions. For example, if the user is stressed, the procurement department will prioritize procuring important ingredients. If the user is relaxed, the procurement department can procure all ingredients equally. For example, if the user is in a hurry, the procurement department will prioritize procuring ingredients that are nearing their expiration date. The procurement department can also perform facial recognition, voice analysis, and behavioral pattern analysis to estimate the user's emotions. For example, the procurement department can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. This allows the procurement department to determine the priority of ingredient procurement according to the user's emotions. Some or all of the above processes in the procurement department may be performed using AI, for example, or not using AI. For example, the procurement department can input user emotion data into a generating AI and have the generating AI determine the priority of ingredient procurement.

[0091] The procurement department can select the most suitable online supermarket when procuring ingredients, taking into account the user's geographical location. For example, the procurement department can select the closest online supermarket based on the user's geographical location. The procurement department can also select an online supermarket with a short delivery time based on the user's geographical location. For example, the procurement department can select an online supermarket with low shipping costs based on the user's geographical location. Furthermore, the procurement department can analyze the user's address, delivery area, and the location of online supermarkets in order to take the user's geographical location into consideration. For example, the procurement department can select the most suitable online supermarket by taking the user's geographical location into consideration. This allows the procurement department to select the most suitable online supermarket based on the user's geographical location. Some or all of the above processes in the procurement department may be performed using AI, for example, or without AI. For example, the procurement department can input the user's geographical location data into a generating AI and have the generating AI select the most suitable online supermarket.

[0092] The procurement department can adjust the amount of ingredients procured when procuring ingredients, taking into account the user's frequency of ingredient use. For example, the procurement department can increase the amount of ingredients that are frequently used, taking into account the user's frequency of ingredient use. The procurement department can also decrease the amount of ingredients that are used less frequently, taking into account the user's frequency of ingredient use. For example, the procurement department can set the amount of ingredients procured according to the frequency of use, taking into account the user's frequency of ingredient use. In addition, the procurement department can analyze the number of uses, intervals between uses, and amounts used in order to take into account the user's frequency of ingredient use. For example, the procurement department can adjust the amount of ingredients procured, taking into account the user's frequency of ingredient use. In this way, the procurement department can adjust the amount of ingredients procured according to the user's frequency of ingredient use. Some or all of the above processes in the procurement department may be performed using AI, for example, or without using AI. For example, the procurement department can input user ingredient usage frequency data into a generating AI and have the generating AI perform the adjustment of the amount of ingredients procured.

[0093] The confirmation unit can estimate the user's emotions and adjust the display method for the refrigerator's status based on the estimated emotions. For example, if the user is stressed, the confirmation unit can provide a simple and highly visible display method. If the user is relaxed, the confirmation unit can provide a display method that includes detailed information. For example, if the user is in a hurry, the confirmation unit can provide a concise display method. The confirmation unit can also perform facial recognition, voice analysis, and behavioral pattern analysis to estimate the user's emotions. For example, the confirmation unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. This allows the confirmation unit to adjust the display method for the refrigerator's status according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the confirmation unit may be performed using AI, for example, or without AI. For example, the verification unit can input user emotion data into the generating AI and have the generating AI adjust the way the status inside the refrigerator is displayed.

[0094] The confirmation unit can highlight the expiration dates of food items when checking the contents of the refrigerator. For example, the confirmation unit can highlight food items nearing their expiration date in red. The confirmation unit can also display a warning for food items that have expired. For example, the confirmation unit can display food items far from their expiration date in green. Furthermore, the confirmation unit can change the color, font size, and display alerts to highlight the expiration dates. For example, the confirmation unit can display food items nearing their expiration date in red to alert the user. This makes it easier to manage expiration dates by highlighting the expiration dates of food items. 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 food expiration date data into a generating AI and have the generating AI execute the highlighting method.

[0095] The confirmation unit can display the frequency of use of ingredients when checking the contents of the refrigerator, thereby encouraging efficient use. For example, the confirmation unit can display frequently used ingredients in blue. The confirmation unit can display infrequently used ingredients in yellow. For example, the confirmation unit can use color coding according to the frequency of use to encourage efficient use. The confirmation unit can also analyze the number of uses, intervals between uses, and amounts used in order to display the frequency of use. For example, the confirmation unit can display frequently used ingredients in blue to encourage efficient use by the user. In this way, the confirmation unit can encourage efficient use of ingredients by displaying the frequency of use. 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 ingredient usage frequency data into a generating AI and have the generating AI determine the display method.

[0096] The confirmation unit can estimate the user's emotions and determine the priority of displaying the contents of the refrigerator based on the estimated emotions. For example, if the user is stressed, the confirmation unit will prioritize displaying important ingredients. If the user is relaxed, the confirmation unit can display all ingredients equally. For example, if the user is in a hurry, the confirmation unit will prioritize displaying ingredients that are nearing their expiration date. The confirmation unit can also perform facial recognition, voice analysis, and behavioral pattern analysis to estimate the user's emotions. For example, the confirmation unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. This allows the confirmation unit to determine the priority of displaying the contents of the refrigerator according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the confirmation unit may be performed using AI, for example, or without AI. For example, the verification unit can input user emotion data into the generating AI and have the generating AI determine the priority of displaying the status inside the refrigerator.

[0097] The confirmation unit can select the optimal display method by referring to the user's past usage history when checking the status inside the refrigerator. For example, the confirmation unit can refer to the user's past usage history and prioritize the display of frequently used ingredients. The confirmation unit can refer to the user's past usage history and highlight ingredients that are used infrequently. For example, the confirmation unit can refer to the user's past usage history and display ingredients in different colors according to their usage frequency. In addition, the confirmation unit can analyze the date and time of use, frequency of use, and quantity of use in order to refer to the user's past usage history. For example, the confirmation unit can refer to the user's past usage history and select the optimal display method. In this way, the confirmation unit can refer to the user's past usage history and select the optimal display method. 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 past usage history data into a generating AI and have the generating AI select the optimal display method.

[0098] The verification unit can select the optimal display method when checking the status inside the refrigerator, taking into account the user's device information. For example, if the user is using a smartphone, the verification unit can provide a display method that matches the screen size. If the user is using a tablet, the verification unit can provide a display method optimized for a larger screen. For example, if the user is using a smartwatch, the verification unit can provide a concise and highly visible display method. In addition, the verification unit can analyze the type of device, screen size, and operating method in order to take the user's device information into consideration. For example, the verification unit can select the optimal display method by taking the user's device information into consideration. This allows the verification unit to select the optimal display method based on the user's device information. Some or all of the above processing in the verification unit may be performed using AI, for example, or without using AI. For example, the verification unit can input the user's device information data into a generating AI and have the generating AI select the optimal display method.

[0099] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0100] The refrigerator management system can estimate the user's emotions and optimize the placement of food items in the refrigerator based on those emotions. For example, if the user is stressed, it can place frequently used items in easily accessible locations. If the user is relaxed, it can suggest an efficient storage arrangement. If the user is in a hurry, it can place items at the front for quick retrieval. In this way, the refrigerator management system can optimize the placement of food items according to the user's emotions.

[0101] A refrigerator management system can optimize the placement of food items based on the user's frequency of use. For example, frequently used items can be placed in easily accessible locations, while less frequently used items can be placed at the back. Furthermore, the placement of items can be color-coded according to their frequency of use. This allows the refrigerator management system to provide an optimal arrangement of food items based on the user's usage frequency.

[0102] The refrigerator management system can estimate the user's emotions and adjust the temperature setting inside the refrigerator based on those emotions. For example, if the user is stressed, the temperature inside the refrigerator can be set lower to preserve the freshness of the food. If the user is relaxed, the normal temperature setting can be maintained. Also, if the user is in a hurry, the temperature inside the refrigerator can be adjusted quickly. In this way, the refrigerator management system can provide temperature settings that are tailored to the user's emotions.

[0103] The refrigerator management system can optimize the placement of food items by referring to the user's past food usage history. For example, it can place frequently used items in easily accessible locations and less frequently used items at the back. It can also color-code the placement of food items based on past usage history. In this way, the refrigerator management system can provide an optimal arrangement tailored to the user's past usage history.

[0104] The refrigerator management system can estimate the user's emotions and adjust the lighting inside the refrigerator based on those emotions. For example, if the user is stressed, the lighting inside the refrigerator will be brighter to improve visibility. If the user is relaxed, the normal lighting setting can be maintained. Also, if the user is in a hurry, the lighting inside the refrigerator can be quickly adjusted. In this way, the refrigerator management system can provide lighting settings that are tailored to the user's emotions.

[0105] The refrigerator management system can optimize the placement of food items, taking into account the expiration dates of the user's food. For example, it can place items with approaching expiration dates in easily accessible locations and items with later expiration dates at the back. It can also color-code the placement of items according to their expiration dates. This allows the refrigerator management system to provide the optimal placement of food items according to the user's food expiration dates.

[0106] The refrigerator management system can estimate the user's emotions and adjust the responses of the voice assistant inside the refrigerator based on those emotions. For example, if the user is stressed, the voice assistant's responses will be quick and concise. If the user is relaxed, it can provide more detailed information. Also, if the user is in a hurry, the voice assistant's responses will be quickly adjusted. In this way, the refrigerator management system can provide voice assistant responses that are tailored to the user's emotions.

[0107] The refrigerator management system can optimize the placement of food items based on the nutritional value of the user's food. For example, it can place high-nutrient items in easily accessible locations and low-nutrient items at the back. It can also color-code the placement of items according to their nutritional value. This allows the refrigerator management system to provide the optimal placement of food items based on the user's nutritional value.

[0108] The refrigerator management system can estimate the user's emotions and adjust alert notifications within the refrigerator based on those emotions. For example, if the user is stressed, only important alerts will be sent. If the user is relaxed, all alerts can be sent. Furthermore, if the user is in a hurry, the alert notifications will be quickly adjusted. This allows the refrigerator management system to provide alert notifications tailored to the user's emotions.

[0109] The refrigerator management system can optimize the placement of food items, taking into account the user's food storage methods. For example, it can place items that need to be frozen in the freezer compartment and items that need to be refrigerated in the refrigerator compartment. It can also color-code the placement of items according to their storage method. This allows the refrigerator management system to provide the optimal placement of food items according to the user's storage methods.

[0110] The following briefly describes the processing flow for example form 2.

[0111] Step 1: The recognition unit recognizes the food items inside the refrigerator. The recognition unit can recognize food items using, for example, an AI camera installed in the refrigerator. Alternatively, the recognition unit can recognize food items using barcode scanning or RFID tags. For example, the recognition unit can recognize food items inside the refrigerator using image recognition technology. When using barcode scanning, the recognition unit scans the barcode of the food item and matches it with a database to recognize the food item. When using RFID tags, the recognition unit reads the RFID tag attached to the food item to recognize the food item. Step 2: The suggestion unit proposes a menu based on the ingredients recognized by the recognition unit. The suggestion unit can propose a menu using, for example, a registered dietitian AI. The suggestion unit can propose a menu that is optimal for each individual user based on health checkup results and pre-existing conditions. For example, the suggestion unit can propose a menu that takes into account the user's blood pressure, blood sugar levels, and allergy information. The suggestion unit can also propose a menu that takes into account the types of vegetables in the refrigerator and their expiration dates calculated backward from the purchase date. For example, the suggestion unit can propose a menu that takes into account the expiration dates of vegetables and contributes to reducing food waste. Step 3: The Procurement Department procures the necessary ingredients from an online supermarket based on the menu proposed by the Proposal Department. For example, the Procurement Department procures ingredients from a partner online supermarket. The Procurement Department can automatically determine the timing and quantity of orders, and arrange delivery. For example, the Procurement Department automatically orders the necessary ingredients from an online supermarket and arranges delivery. Step 4: The monitoring unit checks the status inside the refrigerator and whether it has been opened or closed. The monitoring unit can check the status inside the refrigerator and whether it has been opened or closed, for example, using a smartphone app. The monitoring unit can share the status inside the refrigerator and whether it has been opened or closed with family members, preventing duplicate purchases. For example, the monitoring unit can share the status of food in the refrigerator with family members via a smartphone app. The monitoring unit can also be used as a monitoring function for family members living far away. For example, the monitoring unit can monitor whether the refrigerator has been opened or closed to check on the well-being of family members living far away.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] Each of the multiple elements described above, including the recognition unit, suggestion unit, procurement unit, and confirmation unit, is implemented, for example, by at least one of the smart device 14 and the data processing unit 12. For example, the recognition unit recognizes food items in the refrigerator using the camera 42 and barcode scanning function of the smart device 14. The suggestion unit is implemented by the identification processing unit 290 of the data processing unit 12 and proposes the optimal menu to the user using a registered dietitian AI. The procurement unit is implemented by the identification processing unit 290 of the data processing unit 12 and automatically procures the necessary food items from an online supermarket. The confirmation unit uses the control unit 46A of the smart device 14 to check the status inside the refrigerator and whether it has been opened or closed, and this can be shared with family members. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0116] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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).

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.).

[0128] 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.

[0129] 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.

[0130] 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.

[0131] Each of the multiple elements described above, including the recognition unit, suggestion unit, procurement unit, and confirmation unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the recognition unit recognizes food items in the refrigerator using the camera 42 and barcode scanning function of the smart glasses 214. The suggestion unit is implemented by the identification processing unit 290 of the data processing unit 12 and proposes the optimal menu to the user using a registered dietitian AI. The procurement unit is implemented by the identification processing unit 290 of the data processing unit 12 and automatically procures the necessary food items from an online supermarket. The confirmation unit uses the control unit 46A of the smart glasses 214 to check the status of the refrigerator and whether it has been opened or closed, and this can be shared with family members. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0132] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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).

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.).

[0144] 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.

[0145] 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.

[0146] 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.

[0147] Each of the multiple elements described above, including the recognition unit, suggestion unit, procurement unit, and confirmation unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the recognition unit recognizes food items in the refrigerator using the camera 42 and barcode scanning function of the headset terminal 314. The suggestion unit is implemented by the identification processing unit 290 of the data processing unit 12 and proposes the optimal menu to the user using a registered dietitian AI. The procurement unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and automatically procures the necessary food items from an online supermarket. The confirmation unit uses the control unit 46A of the headset terminal 314 to check the status of the refrigerator and whether it has been opened or closed, and this can be shared with family members. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0148] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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).

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.).

[0161] 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.

[0162] 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.

[0163] 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.

[0164] Each of the multiple elements described above, including the recognition unit, suggestion unit, procurement unit, and confirmation unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the recognition unit recognizes food items in the refrigerator using the camera 42 and barcode scanning function of the robot 414. The suggestion unit is implemented by the identification processing unit 290 of the data processing unit 12 and proposes the optimal menu to the user using a registered dietitian AI. The procurement unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and automatically procures the necessary food items from an online supermarket. The confirmation unit uses the control unit 46A of the robot 414 to check the status inside the refrigerator and whether it has been opened or closed, and this can be shared with the family. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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."

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] (Note 1) A recognition unit that recognizes the food items inside the refrigerator, A suggestion unit that proposes a menu based on the ingredients recognized by the recognition unit, The Procurement Department procures the necessary ingredients from an online supermarket based on the menu proposed by the Proposal Department, It includes a confirmation unit that checks the condition inside the refrigerator and the opening and closing status. A system characterized by the following features. (Note 2) The aforementioned proposal section is, Optimize meal plans based on health checkup results and pre-existing conditions. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned proposal section is, We will suggest a menu that takes into account the types of vegetables in your refrigerator and their expiration dates, calculated backward from the date of purchase. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned procurement department, Automatically source necessary ingredients from an online supermarket. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned verification unit is Use a smartphone app to check the condition inside the refrigerator and whether it has been opened or closed. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned verification unit is Share information about the contents and opening / closing status of the refrigerator with family members to prevent duplicate purchases. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned verification unit is It can be used as a monitoring function for family members who live far away. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned recognition unit, The system estimates the user's emotions and adjusts the accuracy of food recognition based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned recognition unit, When identifying ingredients, the system evaluates their freshness and quality and suggests the optimal storage method. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned recognition unit, When recognizing ingredients, the nutritional value of the ingredients is calculated, and ingredients are selected according to the user's health condition. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned recognition unit, The system estimates the user's emotions and determines the priority of ingredient recognition based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned recognition unit, When recognizing food items, the system improves recognition accuracy by referencing the user's past purchase history. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned recognition unit, When recognizing ingredients, the recognition results are displayed taking into account the user's frequency of using those ingredients. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned proposal section is, The system estimates the user's emotions and adjusts the menu suggestion method based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned proposal section is, When suggesting menus, the system refers to the user's past meal history to suggest the most suitable menu. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned proposal section is, When suggesting menus, we take the user's allergy information into consideration to propose safe menus. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned proposal section is, It estimates the user's emotions and determines the priority of the menu based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned proposal section is, When suggesting menus, the suggestions are customized to take into account the user's food preferences. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned proposal section is, When suggesting menus, the system will propose the optimal cooking time, taking into account the user's mealtime. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned procurement department, The system estimates the user's emotions and adjusts the timing of ingredient procurement based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned procurement department, When procuring ingredients, we select the most suitable ingredients while taking the user's budget into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned procurement department, When procuring ingredients, the system prevents duplicate purchases by referring to the user's purchase history. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned procurement department, The system estimates the user's emotions and determines the priority of ingredient procurement based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned procurement department, When procuring ingredients, the system selects the most suitable online supermarket by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned procurement department, When procuring ingredients, adjust the quantity procured considering the user's frequency of ingredient use. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned verification unit is The system estimates the user's emotions and adjusts the display of the refrigerator's status based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned verification unit is When checking the contents of the refrigerator, highlight the expiration dates of the food items. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned verification unit is When checking the contents of the refrigerator, displaying the frequency of use of each ingredient encourages efficient use. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned verification unit is The system estimates the user's emotions and determines the priority of displaying the status inside the refrigerator based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned verification unit is When checking the status inside the refrigerator, the system selects the optimal display method by referring to the user's past usage history. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned verification unit is When checking the status inside the refrigerator, the system selects the optimal display method considering the user's device information. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0184] 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 recognition unit that recognizes the food items inside the refrigerator, A suggestion unit that proposes a menu based on the ingredients recognized by the recognition unit, The Procurement Department procures the necessary ingredients from an online supermarket based on the menu proposed by the Proposal Department, It includes a confirmation unit that checks the condition inside the refrigerator and the opening and closing status. A system characterized by the following features.

2. The aforementioned proposal section is, Optimize meal plans based on health checkup results and pre-existing conditions. The system according to feature 1.

3. The aforementioned proposal section is, We will suggest a menu that takes into account the types of vegetables in your refrigerator and their expiration dates, calculated backward from the date of purchase. The system according to feature 1.

4. The aforementioned procurement department, Automatically source necessary ingredients from an online supermarket. The system according to feature 1.

5. The aforementioned verification unit is Use a smartphone app to check the condition inside the refrigerator and whether it has been opened or closed. The system according to feature 1.

6. The aforementioned verification unit is Share information about the contents and opening / closing status of the refrigerator with family members to prevent duplicate purchases. The system according to feature 1.

7. The aforementioned verification unit is It can be used as a monitoring function for family members who live far away. The system according to feature 1.

8. The aforementioned recognition unit, The system estimates the user's emotions and adjusts the accuracy of food recognition based on those emotions. The system according to feature 1.

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