system

Abstract

The system according to this embodiment aims to appropriately manage the storage period and consumption order of food items in a refrigerator. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a reminder unit, and a VR display unit. The collection unit collects information on food in the refrigerator. The analysis unit analyzes the information collected by the collection unit and reminds the user of the storage period and consumption order of the food. The reminder unit provides the user with the information reminded by the analysis unit. The VR display unit displays the information provided by the reminder unit in a VR space.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, it is difficult to manage the storage period and consumption order of foods in a refrigerator, and there is a risk of food loss.

[0005] The system according to the embodiment aims to appropriately manage the storage period and consumption order of foods in a refrigerator.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, a reminder unit, and a VR display unit. The data collection unit collects information about the food in the refrigerator. The analysis unit analyzes the information collected by the data collection unit and reminds the user of the food's shelf life and consumption order. The reminder unit provides the user with the information reminded by the analysis unit. The VR display unit displays the information provided by the reminder unit in a VR space. [Effects of the Invention]

[0007] The system according to this embodiment can appropriately manage the storage period and consumption order of food items in a refrigerator. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The food loss management system according to an embodiment of the present invention is a system that aims to dramatically reduce household food waste. In this system, a generating AI understands food purchase information and reminds the user of storage periods and consumption order. In addition, the AI ​​identifies the type of food through a camera installed in the refrigerator, extracts information, and supports management. Users can visually manage the ingredients in the refrigerator in a VR space and check the refrigerator from anywhere, thus preventing overbuying. For example, the food loss management system uses a generating AI to manage the food in the refrigerator, understand the condition of the ingredients, and remind the user of expiration dates and recommended consumption order. Next, the user can check the inside of the refrigerator in a VR space, so they can check the refrigerator from anywhere and prevent overbuying. For example, a camera installed in the refrigerator identifies the type of food, and the generating AI analyzes that information and reminds the user of storage periods and consumption order. Users can visually check the inside of the refrigerator in a VR space and understand which foods are buried deep inside. This makes it easier to plan food consumption and reduces food waste. This means the food waste management system targets households that want to reduce food waste, households that want to address environmental issues, and households that want to make effective use of VR technology. Through generative AI and multimodal dialogue, it extracts food types and usage information from image data acquired from a camera inside the refrigerator, and provides a function that allows users to check the contents of their refrigerator in a VR space. This makes it easier to plan food consumption and reduces food waste.

[0029] The food loss management system according to this embodiment comprises a collection unit, an analysis unit, a reminder unit, and a VR display unit. The collection unit collects information about food in the refrigerator. The collection unit can collect information about food using, for example, a camera installed inside the refrigerator. The collection unit can also collect information about food using sensors or manual input. The analysis unit analyzes the information collected by the collection unit and reminds users about the storage period and consumption order of the food. The analysis unit can analyze the collected information using, for example, a generative AI to identify the type of food. The analysis unit can also analyze the information using image analysis, data mining, or machine learning algorithms. The reminder unit provides the user with the information reminded by the analysis unit. The reminder unit can, for example, set the notification method, timing, and frequency to remind the user. The reminder unit can also customize the reminder content based on the user's preferences and behavioral patterns. The VR display unit displays the information provided by the reminder unit in a VR space. The VR display unit allows, for example, the user to visually check the inside of the refrigerator using a VR device. Furthermore, the VR display unit can also display the arrangement and condition of food items inside the refrigerator as a 3D map. This allows the food loss management system according to this embodiment to efficiently collect, analyze, remind, and display information about food items inside the refrigerator.

[0030] The data collection unit collects information about food inside the refrigerator. For example, the unit can collect food information using a camera installed inside the refrigerator. Specifically, the camera periodically photographs the inside of the refrigerator and collects the image data. This allows the unit to understand the types, arrangement, and quantities of food inside the refrigerator. The data collection unit can also collect food information using sensors or manual input. For example, it can automatically register information when food is brought into the refrigerator using RFID tags or barcode readers. Furthermore, it can collect environmental data inside the refrigerator using temperature and humidity sensors to monitor the storage condition of the food. In the case of manual input, users can input information such as the name of the food, purchase date, and expiration date using a smartphone or tablet. This allows the data collection unit to collect food information in various ways and accurately understand the situation inside the refrigerator. The collected data is sent to a cloud server, making it accessible to the analysis unit and reminder unit. This allows the data collection unit to collect food information efficiently and accurately, improving the overall system performance.

[0031] The analysis unit analyzes the information collected by the collection unit and reminds users of the shelf life and consumption order of food items. For example, the analysis unit can use generative AI to analyze the collected information and identify the type of food item. The generative AI uses image recognition technology to identify the type of food item from camera images and compares it with a database to obtain detailed information. The analysis unit can also analyze information using image analysis, data mining, and machine learning algorithms. For example, it can use image analysis technology to analyze the appearance and labels of food items and automatically extract best-before and expiration dates. Using data mining technology, it can analyze past consumption data and purchase history to understand the user's consumption patterns. Using machine learning algorithms, it can predict the rate of food deterioration and storage conditions and suggest the optimal consumption order. As a result, the analysis unit can analyze the collected data from multiple angles and provide useful information to the user. Furthermore, the analysis unit can update the data in real time and provide analysis results based on the latest information. As a result, the analysis unit can accurately remind users of the shelf life and consumption order of food items, minimizing food waste.

[0032] The reminder unit provides users with information reminded by the analysis unit. The reminder unit can, for example, set the notification method, timing, and frequency to send reminders to users. Specifically, it can remind users of food expiration dates and storage periods using smartphone push notifications, email, SMS, etc. The reminder unit can also customize the content of reminders based on the user's preferences and behavioral patterns. For example, if a user prefers to consume a particular food, the unit can prioritize reminders when the expiration date of that food is approaching. Furthermore, the reminder unit can send reminders at the optimal time according to the user's schedule and lifestyle. For example, sending a reminder at the time the user returns home from work can encourage food consumption. The reminder unit can also collect feedback from users and continuously improve the accuracy and effectiveness of the reminder content. As a result, the reminder unit can provide users with useful information at the right time and effectively prevent food waste.

[0033] The VR display unit displays information provided by the reminder unit in a VR space. For example, the VR display unit allows users to visually check the inside of the refrigerator using a VR device. Specifically, by wearing VR goggles, users can see the arrangement and condition of food inside the refrigerator in a 3D map. This allows users to understand the condition of the food in detail without opening the refrigerator. The VR display unit can also visually display the expiration date and storage period of food, providing users with information that can be intuitively understood. For example, by displaying food nearing its expiration date in red and food with a long storage period in green, users can grasp important information at a glance. Furthermore, the VR display unit can also display related recipes and cooking methods when users select food. This allows users to efficiently utilize the food in the refrigerator and reduce food waste. The VR display unit can dynamically update the displayed content in response to user operations, providing the latest information. In this way, the VR display unit can provide users with visual and intuitive information and effectively support food waste management.

[0034] The collection unit can collect information from a camera installed inside the refrigerator. For example, the collection unit can collect food information using a high-resolution camera installed inside the refrigerator. The collection unit can also adjust the camera's field of view and installation position to cover the entire inside of the refrigerator. Furthermore, the collection unit can set the camera's resolution to obtain detailed food information. This allows the collection unit to accurately collect food information inside the refrigerator. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input image data acquired by the camera into a generating AI and have the generating AI extract food information from the image data.

[0035] The analysis unit can analyze the collected information and identify the type of food. For example, the analysis unit can analyze the collected image data using an image recognition algorithm to identify the type of food. The analysis unit can also identify the type of food using a database lookup. Furthermore, the analysis unit can identify the type of food using a machine learning algorithm. This allows the analysis unit to accurately identify the type of food. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the collected image data into a generative AI and have the generative AI perform the identification of the type of food.

[0036] The reminder unit can remind users about the shelf life and consumption order of food items based on the analyzed information. For example, the reminder unit can set the notification method, timing, and frequency to send reminders to the user. Furthermore, the reminder unit can customize the reminder content based on the user's preferences and behavioral patterns. In addition, the reminder unit can provide information for planning food consumption based on the shelf life and consumption order. This allows the reminder unit to appropriately remind users about the shelf life and consumption order of food items. Some or all of the above-described processes in the reminder unit may be performed using, for example, a generative AI, or without a generative AI. For example, the reminder unit can input the analyzed information into a generative AI and have the generative AI execute reminders about the shelf life and consumption order.

[0037] The VR display unit can enable users to visually inspect the inside of the refrigerator in a VR space. For example, the VR display unit allows users to visually inspect the inside of the refrigerator using a VR device. The VR display unit can also display the arrangement and condition of food items inside the refrigerator in a 3D map. Furthermore, the VR display unit can display the expiration dates and storage periods of the food items inside the refrigerator. This allows the VR display unit to enable users to visually inspect the inside of the refrigerator in a VR space. Some or all of the above-described processes in the VR display unit may be performed using, for example, a generative AI, or without a generative AI. For example, the VR display unit can input information about the food items inside the refrigerator into a generative AI and have the generative AI perform the display in the VR space.

[0038] The collection unit can monitor the temperature and humidity inside the refrigerator and adjust the collection frequency according to the degree of food deterioration. For example, if the temperature inside the refrigerator is high, the collection unit can increase the collection frequency to detect food deterioration early. The collection unit can also increase the collection frequency if the humidity inside the refrigerator is high to prevent mold growth. Furthermore, if the temperature and humidity inside the refrigerator are stable, the collection unit can reduce the collection frequency to conserve energy. In this way, the collection unit can detect food deterioration early by adjusting the collection frequency according to the environment inside the refrigerator. Some or all of the above processing in the collection unit may be performed using, for example, a generating AI, or without a generating AI. For example, the collection unit can input temperature and humidity data from inside the refrigerator into a generating AI and have the generating AI perform an analysis of the deterioration status.

[0039] The collection unit can collect detailed food information by scanning QR codes (registered trademarks) or barcodes attached to food packaging. For example, the collection unit can scan QR codes to obtain the manufacturing date and expiration date of the food. It can also scan barcodes to obtain nutritional information and allergy information about the food. Furthermore, the collection unit can scan QR codes or barcodes to obtain storage and cooking instructions for the food. In this way, the collection unit can collect detailed food information by scanning QR codes or barcodes. Some or all of the above processing in the collection unit may be performed using, for example, a generating AI, or without a generating AI. For example, the collection unit can input QR code or barcode data into a generating AI and have the generating AI extract detailed food information.

[0040] The data collection unit can install different cameras on each shelf in the refrigerator and collect food information for each shelf individually. For example, the data collection unit can collect vegetable information with the top camera and meat information with the middle camera. The data collection unit can also integrate the information collected by the cameras on each shelf to understand the food information for the entire refrigerator. Furthermore, the data collection unit can manage the food on each shelf based on the information collected by the cameras on each shelf. In this way, the data collection unit can accurately understand the food information for the entire refrigerator by collecting food information for each shelf individually. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or it may be performed without a generative AI. For example, the data collection unit can input image data acquired by the cameras on each shelf into a generative AI and have the generative AI extract food information.

[0041] The data collection unit can collect shopping lists and receipt information from outside the refrigerator and integrate it with the food information inside the refrigerator. For example, the data collection unit can collect shopping lists and compare them with the food information inside the refrigerator to identify missing items. The data collection unit can also collect receipt information and add information about purchased food items to the food information inside the refrigerator. Furthermore, the data collection unit can update the food information inside the refrigerator based on the shopping lists and receipt information. Thus, the data collection unit can update the food information inside the refrigerator by collecting shopping lists and receipt information. Some or all of the above processing in the data collection unit may be performed using, for example, a generating AI, or not using a generating AI. For example, the data collection unit can input shopping lists and receipt information into a generating AI and have the generating AI perform the integration of food information.

[0042] The analysis unit can analyze the nutritional value and allergy information of foods and suggest a consumption order tailored to the user's health condition. For example, if the user has allergies, the analysis unit can suggest a consumption order that avoids allergenic foods. Furthermore, if the user is on a diet, the analysis unit can suggest a consumption order that prioritizes low-calorie foods. Additionally, if the user's goal is health maintenance, the analysis unit can suggest a consumption order that prioritizes foods with balanced nutritional value. In this way, the analysis unit can support a healthy diet by suggesting a consumption order tailored to the user's health condition. Some or all of the above processing in the analysis unit may be performed using, for example, a generating AI, or without a generating AI. For example, the analysis unit can input the nutritional value and allergy information of foods into a generating AI and have the generating AI suggest a consumption order.

[0043] The analysis unit can analyze the user's food purchase history and predict the optimal order of consumption based on past consumption patterns. For example, the analysis unit can prioritize suggesting foods that the user has frequently consumed in the past. The analysis unit can also predict the optimal order of consumption based on the user's past consumption patterns. Furthermore, the analysis unit can prioritize suggesting foods that are nearing their expiration date based on the user's purchase history. In this way, the analysis unit can reduce food waste by predicting the optimal order of consumption based on past consumption patterns. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the user's purchase history data into a generative AI and have the generative AI perform the prediction of the order of consumption.

[0044] The analysis unit can analyze the origin and producer information of food products and propose a consumption order that suits the user's preferences. For example, if the user prefers food products from a particular origin, the analysis unit will prioritize suggesting products from that origin. Furthermore, if the user prefers food products from a particular producer, the analysis unit can prioritize suggesting products from that producer. In addition, the analysis unit can propose an optimal consumption order based on the user's preferences. This allows the analysis unit to improve user satisfaction by proposing a consumption order that suits the user's preferences. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the origin and producer information of food products into a generative AI and have the generative AI propose a consumption order.

[0045] The analysis unit can switch analysis algorithms according to the food storage method. For example, in the case of frozen foods, the analysis unit considers the expiration date after thawing. In the case of refrigerated foods, the analysis unit can also consider the temperature and humidity inside the refrigerator. Furthermore, in the case of room-temperature stored foods, the analysis unit can also consider the storage environment. As a result, the analysis unit can provide more accurate analysis results by performing analysis according to the food storage method. Some or all of the above processing in the analysis unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the analysis unit can input food storage method data into a generating AI and have the generating AI execute the switching of analysis algorithms.

[0046] The reminder unit can suggest the optimal timing for consumption, taking into account the user's schedule, when sending a reminder. For example, the reminder unit can suggest the optimal timing for consumption according to the user's schedule. Furthermore, if the user is busy, the reminder unit can suggest foods that can be easily consumed. Additionally, if the user has more time, the reminder unit can suggest foods that require more preparation time. In this way, the reminder unit can reduce food waste by suggesting consumption timings that align with the user's schedule. Some or all of the above processing in the reminder unit may be performed using, for example, a generative AI, or without one. For example, the reminder unit can input the user's schedule data into a generative AI and have the generative AI suggest consumption timings.

[0047] The reminder unit can select the optimal reminder method by referring to the user's past reminder history when sending a reminder. For example, the reminder unit may prioritize suggesting reminder methods that the user has preferred to use in the past. The reminder unit can also select the optimal reminder method from the user's past reminder history. Furthermore, the reminder unit can adjust the timing of reminders based on the user's reminder history. In this way, the reminder unit can provide the optimal reminder method by referring to the user's past reminder history. Some or all of the above processing in the reminder unit may be performed using, for example, a generation AI, or without a generation AI. For example, the reminder unit can input the user's reminder history data into a generation AI and have the generation AI select the reminder method.

[0048] The reminder unit can adjust the notification method according to the user's device when a reminder is given. For example, if the user is using a smartphone, the reminder unit will send a push notification. The reminder unit can also provide a reminder optimized for a larger screen if the user is using a tablet. Furthermore, if the user is using a smartwatch, the reminder unit can send a vibration notification. This allows the reminder unit to enhance the effectiveness of reminders by providing notification methods tailored to the user's device. Some or all of the above processing in the reminder unit may be performed using, for example, a generative AI, or without one. For example, the reminder unit can input the user's device information into the generative AI and have the generative AI adjust the notification method.

[0049] The reminder function can send notifications to the user's family and roommates when reminders are given, enabling collaborative food management. For example, the reminder function can send reminder notifications to all members of the user's family for collaborative food management. It can also send reminder notifications to roommates to share food consumption plans. Furthermore, the reminder function can suggest the optimal reminder timing, taking into account the schedules of family members and roommates. This allows the reminder function to reduce food waste by enabling collaborative food management with family members and roommates. Some or all of the above processing in the reminder function may be performed using, for example, a generative AI, or without a generative AI. For example, the reminder function can input schedule data of family members and roommates into a generative AI and have the generative AI suggest reminder timings.

[0050] The VR display unit can optimize the arrangement of food items inside the refrigerator during VR display, making it visually easy to understand. For example, the VR display unit can organize the food items inside the refrigerator by category and display them visually. The VR display unit can also optimize the arrangement of food items, displaying frequently used items at the front. Furthermore, the VR display unit can efficiently utilize the space inside the refrigerator and optimize the arrangement of food items. As a result, the VR display unit can optimize the arrangement of food items inside the refrigerator, making it visually easy to understand. Some or all of the above processing in the VR display unit may be performed using, for example, a generation AI, or without a generation AI. For example, the VR display unit can input food arrangement data inside the refrigerator into a generation AI and have the generation AI perform the arrangement optimization.

[0051] The VR display unit can highlight food items nearing their expiration date during VR display to alert the user. For example, the VR display unit can highlight food items nearing their expiration date in red to alert the user. The VR display unit can also display food items nearing their expiration date in a pop-up to alert the user. Furthermore, the VR display unit can list food items nearing their expiration date to alert the user. In this way, the VR display unit can alert the user by highlighting food items nearing their expiration date. Some or all of the above processing in the VR display unit may be performed using, for example, a generation AI, or without a generation AI. For example, the VR display unit can input expiration date data into a generation AI and have the generation AI perform the highlighting process.

[0052] The VR display unit can support the user's diet by displaying nutritional information and recipe suggestions for the food in the refrigerator during VR display. For example, the VR display unit can support the user's diet by displaying nutritional information for the food in the refrigerator. The VR display unit can also support the user's diet by suggesting recipes using the food in the refrigerator. Furthermore, the VR display unit can support the user's diet by suggesting recipes that take into account the nutritional balance of the food in the refrigerator. In this way, the VR display unit can support the user's diet by displaying nutritional information and recipe suggestions for the food in the refrigerator. Some or all of the above processing in the VR display unit may be performed using, for example, a generation AI, or without a generation AI. For example, the VR display unit can input nutritional information and recipe data for the food into a generation AI and have the generation AI execute the display processing.

[0053] The VR display unit can display the location of food items in the refrigerator on a 3D map during VR display, allowing users to intuitively find food items. For example, the VR display unit can display the location of food items in the refrigerator on a 3D map, allowing users to intuitively find food items. The VR display unit can also use the 3D map to visually confirm the arrangement of food items in the refrigerator. Furthermore, the VR display unit can use the 3D map to efficiently manage the location of food items in the refrigerator. Thus, by displaying the location of food items in the refrigerator on a 3D map, the VR display unit can enable users to intuitively find food items. Some or all of the above processing in the VR display unit may be performed using, for example, a generation AI, or without a generation AI. For example, the VR display unit can input food location data from the refrigerator into a generation AI and have the generation AI generate a 3D map.

[0054] The VR display unit can optimize the arrangement of food items inside the refrigerator during VR display, making it visually easy to understand. For example, the VR display unit can organize the food items inside the refrigerator by category and display them visually. The VR display unit can also optimize the arrangement of food items, displaying frequently used items at the front. Furthermore, the VR display unit can efficiently utilize the space inside the refrigerator and optimize the arrangement of food items. As a result, the VR display unit can optimize the arrangement of food items inside the refrigerator, making it visually easy to understand. Some or all of the above processing in the VR display unit may be performed using, for example, a generation AI, or without a generation AI. For example, the VR display unit can input food arrangement data inside the refrigerator into a generation AI and have the generation AI perform the arrangement optimization.

[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 food waste management system can analyze the nutritional value of food in a refrigerator and suggest a consumption plan tailored to the user's health condition. For example, if a user is on a diet, it can prioritize suggesting low-calorie foods. It can also suggest foods rich in specific nutrients if the user needs them. Furthermore, if a user has allergies, it can suggest a consumption plan that avoids allergenic foods. In this way, the food waste management system can support a healthy diet by suggesting a consumption plan that suits the user's health needs.

[0057] A food waste management system can propose consumption plans based on how food is stored in the refrigerator. For example, for frozen foods, it proposes a consumption plan considering the expiration date after thawing. For refrigerated foods, it can also propose a consumption plan considering the temperature and humidity inside the refrigerator. Furthermore, for foods stored at room temperature, it can propose a consumption plan considering the storage environment. In this way, a food waste management system can reduce food waste by proposing consumption plans that are appropriate to how food is stored.

[0058] A food waste management system can optimize the placement of food in a refrigerator and display it in a visually easy-to-understand manner. For example, it can organize food in the refrigerator by category and display it in an easy-to-understand way. It can also place frequently used foods at the front so that they can be easily retrieved. Furthermore, it can efficiently utilize the space inside the refrigerator and optimize the placement of food. In this way, a food waste management system can optimize the placement of food in a refrigerator and display it in a visually easy-to-understand manner.

[0059] A food waste management system can analyze the nutritional information of food in the refrigerator and suggest recipes tailored to the user's health condition. For example, if a user is on a diet, it can suggest low-calorie recipes. It can also suggest recipes rich in specific nutrients if the user needs them. Furthermore, if a user has allergies, it can suggest recipes that avoid allergenic foods. In this way, the food waste management system can support a healthy diet by suggesting recipes that suit the user's health needs.

[0060] A food waste management system can highlight the expiration dates of food in the refrigerator to alert users. For example, it can highlight food nearing its expiration date in red to draw user attention. It can also display food nearing its expiration date in a pop-up to alert users. Furthermore, it can list food nearing its expiration date to alert users. In this way, a food waste management system can alert users by highlighting food nearing its expiration date.

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

[0062] Step 1: The collection unit collects information about the food inside the refrigerator. The collection unit can collect information about the food using, for example, a camera installed inside the refrigerator. Alternatively, the collection unit can collect information about the food using sensors or manual input. Step 2: The analysis unit analyzes the information collected by the collection unit and reminds users of the shelf life and consumption order of food items. The analysis unit can, for example, use generative AI to analyze the collected information and identify the type of food item. The analysis unit can also analyze the information using image analysis, data mining, and machine learning algorithms. Step 3: The reminder unit provides the user with the information reminded by the analysis unit. The reminder unit can, for example, set the notification method, timing, and frequency to send reminders to the user. The reminder unit can also customize the reminder content based on the user's preferences and behavioral patterns. Step 4: The VR display unit displays the information provided by the reminder unit in the VR space. For example, the VR display unit allows the user to visually check the inside of the refrigerator using a VR device. The VR display unit can also display the arrangement and condition of food inside the refrigerator in a 3D map.

[0063] (Example of form 2) The food loss management system according to an embodiment of the present invention is a system that aims to dramatically reduce household food waste. In this system, a generating AI understands food purchase information and reminds the user of storage periods and consumption order. In addition, the AI ​​identifies the type of food through a camera installed in the refrigerator, extracts information, and supports management. Users can visually manage the ingredients in the refrigerator in a VR space and check the refrigerator from anywhere, thus preventing overbuying. For example, the food loss management system uses a generating AI to manage the food in the refrigerator, understand the condition of the ingredients, and remind the user of expiration dates and recommended consumption order. Next, the user can check the inside of the refrigerator in a VR space, so they can check the refrigerator from anywhere and prevent overbuying. For example, a camera installed in the refrigerator identifies the type of food, and the generating AI analyzes that information and reminds the user of storage periods and consumption order. Users can visually check the inside of the refrigerator in a VR space and understand which foods are buried deep inside. This makes it easier to plan food consumption and reduces food waste. This means the food waste management system targets households that want to reduce food waste, households that want to address environmental issues, and households that want to make effective use of VR technology. Through generative AI and multimodal dialogue, it extracts food types and usage information from image data acquired from a camera inside the refrigerator, and provides a function that allows users to check the contents of their refrigerator in a VR space. This makes it easier to plan food consumption and reduces food waste.

[0064] The food loss management system according to this embodiment comprises a collection unit, an analysis unit, a reminder unit, and a VR display unit. The collection unit collects information about food in the refrigerator. The collection unit can collect information about food using, for example, a camera installed inside the refrigerator. The collection unit can also collect information about food using sensors or manual input. The analysis unit analyzes the information collected by the collection unit and reminds users about the storage period and consumption order of the food. The analysis unit can analyze the collected information using, for example, a generative AI to identify the type of food. The analysis unit can also analyze the information using image analysis, data mining, or machine learning algorithms. The reminder unit provides the user with the information reminded by the analysis unit. The reminder unit can, for example, set the notification method, timing, and frequency to remind the user. The reminder unit can also customize the reminder content based on the user's preferences and behavioral patterns. The VR display unit displays the information provided by the reminder unit in a VR space. The VR display unit allows, for example, the user to visually check the inside of the refrigerator using a VR device. Furthermore, the VR display unit can also display the arrangement and condition of food items inside the refrigerator as a 3D map. This allows the food loss management system according to this embodiment to efficiently collect, analyze, remind, and display information about food items inside the refrigerator.

[0065] The data collection unit collects information about food inside the refrigerator. For example, the unit can collect food information using a camera installed inside the refrigerator. Specifically, the camera periodically photographs the inside of the refrigerator and collects the image data. This allows the unit to understand the types, arrangement, and quantities of food inside the refrigerator. The data collection unit can also collect food information using sensors or manual input. For example, it can automatically register information when food is brought into the refrigerator using RFID tags or barcode readers. Furthermore, it can collect environmental data inside the refrigerator using temperature and humidity sensors to monitor the storage condition of the food. In the case of manual input, users can input information such as the name of the food, purchase date, and expiration date using a smartphone or tablet. This allows the data collection unit to collect food information in various ways and accurately understand the situation inside the refrigerator. The collected data is sent to a cloud server, making it accessible to the analysis unit and reminder unit. This allows the data collection unit to collect food information efficiently and accurately, improving the overall system performance.

[0066] The analysis unit analyzes the information collected by the collection unit and reminds users of the shelf life and consumption order of food items. For example, the analysis unit can use generative AI to analyze the collected information and identify the type of food item. The generative AI uses image recognition technology to identify the type of food item from camera images and compares it with a database to obtain detailed information. The analysis unit can also analyze information using image analysis, data mining, and machine learning algorithms. For example, it can use image analysis technology to analyze the appearance and labels of food items and automatically extract best-before and expiration dates. Using data mining technology, it can analyze past consumption data and purchase history to understand the user's consumption patterns. Using machine learning algorithms, it can predict the rate of food deterioration and storage conditions and suggest the optimal consumption order. As a result, the analysis unit can analyze the collected data from multiple angles and provide useful information to the user. Furthermore, the analysis unit can update the data in real time and provide analysis results based on the latest information. As a result, the analysis unit can accurately remind users of the shelf life and consumption order of food items, minimizing food waste.

[0067] The reminder unit provides users with information reminded by the analysis unit. The reminder unit can, for example, set the notification method, timing, and frequency to send reminders to users. Specifically, it can remind users of food expiration dates and storage periods using smartphone push notifications, email, SMS, etc. The reminder unit can also customize the content of reminders based on the user's preferences and behavioral patterns. For example, if a user prefers to consume a particular food, the unit can prioritize reminders when the expiration date of that food is approaching. Furthermore, the reminder unit can send reminders at the optimal time according to the user's schedule and lifestyle. For example, sending a reminder at the time the user returns home from work can encourage food consumption. The reminder unit can also collect feedback from users and continuously improve the accuracy and effectiveness of the reminder content. As a result, the reminder unit can provide users with useful information at the right time and effectively prevent food waste.

[0068] The VR display unit displays information provided by the reminder unit in a VR space. For example, the VR display unit allows users to visually check the inside of the refrigerator using a VR device. Specifically, by wearing VR goggles, users can see the arrangement and condition of food inside the refrigerator in a 3D map. This allows users to understand the condition of the food in detail without opening the refrigerator. The VR display unit can also visually display the expiration date and storage period of food, providing users with information that can be intuitively understood. For example, by displaying food nearing its expiration date in red and food with a long storage period in green, users can grasp important information at a glance. Furthermore, the VR display unit can also display related recipes and cooking methods when users select food. This allows users to efficiently utilize the food in the refrigerator and reduce food waste. The VR display unit can dynamically update the displayed content in response to user operations, providing the latest information. In this way, the VR display unit can provide users with visual and intuitive information and effectively support food waste management.

[0069] The collection unit can collect information from a camera installed inside the refrigerator. For example, the collection unit can collect food information using a high-resolution camera installed inside the refrigerator. The collection unit can also adjust the camera's field of view and installation position to cover the entire inside of the refrigerator. Furthermore, the collection unit can set the camera's resolution to obtain detailed food information. This allows the collection unit to accurately collect food information inside the refrigerator. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input image data acquired by the camera into a generating AI and have the generating AI extract food information from the image data.

[0070] The analysis unit can analyze the collected information and identify the type of food. For example, the analysis unit can analyze the collected image data using an image recognition algorithm to identify the type of food. The analysis unit can also identify the type of food using a database lookup. Furthermore, the analysis unit can identify the type of food using a machine learning algorithm. This allows the analysis unit to accurately identify the type of food. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the collected image data into a generative AI and have the generative AI perform the identification of the type of food.

[0071] The reminder unit can remind users about the shelf life and consumption order of food items based on the analyzed information. For example, the reminder unit can set the notification method, timing, and frequency to send reminders to the user. Furthermore, the reminder unit can customize the reminder content based on the user's preferences and behavioral patterns. In addition, the reminder unit can provide information for planning food consumption based on the shelf life and consumption order. This allows the reminder unit to appropriately remind users about the shelf life and consumption order of food items. Some or all of the above-described processes in the reminder unit may be performed using, for example, a generative AI, or without a generative AI. For example, the reminder unit can input the analyzed information into a generative AI and have the generative AI execute reminders about the shelf life and consumption order.

[0072] The VR display unit can enable users to visually inspect the inside of the refrigerator in a VR space. For example, the VR display unit allows users to visually inspect the inside of the refrigerator using a VR device. The VR display unit can also display the arrangement and condition of food items inside the refrigerator in a 3D map. Furthermore, the VR display unit can display the expiration dates and storage periods of the food items inside the refrigerator. This allows the VR display unit to enable users to visually inspect the inside of the refrigerator in a VR space. Some or all of the above-described processes in the VR display unit may be performed using, for example, a generative AI, or without a generative AI. For example, the VR display unit can input information about the food items inside the refrigerator into a generative AI and have the generative AI perform the display in the VR space.

[0073] The data collection unit can estimate the user's emotions and adjust the timing of food information collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can reduce the collection frequency to alleviate the user's burden. Conversely, if the user is relaxed, the data collection unit can increase the collection frequency and provide more detailed food information. Furthermore, if the user is in a hurry, the data collection unit can shorten the collection timing to provide information quickly. In this way, the data collection unit can reduce the user's burden by adjusting the collection timing according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using a generative AI, or not. For example, the data collection unit can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0074] The collection unit can monitor the temperature and humidity inside the refrigerator and adjust the collection frequency according to the degree of food deterioration. For example, if the temperature inside the refrigerator is high, the collection unit can increase the collection frequency to detect food deterioration early. The collection unit can also increase the collection frequency if the humidity inside the refrigerator is high to prevent mold growth. Furthermore, if the temperature and humidity inside the refrigerator are stable, the collection unit can reduce the collection frequency to conserve energy. In this way, the collection unit can detect food deterioration early by adjusting the collection frequency according to the environment inside the refrigerator. Some or all of the above processing in the collection unit may be performed using, for example, a generating AI, or without a generating AI. For example, the collection unit can input temperature and humidity data from inside the refrigerator into a generating AI and have the generating AI perform an analysis of the deterioration status.

[0075] The collection unit can collect detailed food information by scanning QR codes or barcodes attached to food packaging. For example, the collection unit can scan QR codes to obtain the food's manufacturing date and expiration date. It can also scan barcodes to obtain nutritional information and allergy information. Furthermore, the collection unit can scan QR codes or barcodes to obtain information on how to store and prepare the food. In this way, the collection unit can collect detailed food information by scanning QR codes or barcodes. Some or all of the above processing in the collection unit may be performed using, for example, a generating AI, or without a generating AI. For example, the collection unit can input QR code or barcode data into a generating AI and have the generating AI extract detailed food information.

[0076] The data collection unit can estimate the user's emotions and determine the priority of food information to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit will prioritize collecting only important food information. If the user is relaxed, the data collection unit can also prioritize collecting detailed food information. Furthermore, if the user is in a hurry, the data collection unit can prioritize collecting food information that can be collected quickly. In this way, the data collection unit can prioritize the collection of important information by determining the priority of food information to collect according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using a generative AI, or not using a generative AI. For example, the data collection unit can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0077] The data collection unit can install different cameras on each shelf in the refrigerator and collect food information for each shelf individually. For example, the data collection unit can collect vegetable information with the top camera and meat information with the middle camera. The data collection unit can also integrate the information collected by the cameras on each shelf to understand the food information for the entire refrigerator. Furthermore, the data collection unit can manage the food on each shelf based on the information collected by the cameras on each shelf. In this way, the data collection unit can accurately understand the food information for the entire refrigerator by collecting food information for each shelf individually. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or it may be performed without a generative AI. For example, the data collection unit can input image data acquired by the cameras on each shelf into a generative AI and have the generative AI extract food information.

[0078] The data collection unit can collect shopping lists and receipt information from outside the refrigerator and integrate it with the food information inside the refrigerator. For example, the data collection unit can collect shopping lists and compare them with the food information inside the refrigerator to identify missing items. The data collection unit can also collect receipt information and add information about purchased food items to the food information inside the refrigerator. Furthermore, the data collection unit can update the food information inside the refrigerator based on the shopping lists and receipt information. Thus, the data collection unit can update the food information inside the refrigerator by collecting shopping lists and receipt information. Some or all of the above processing in the data collection unit may be performed using, for example, a generating AI, or not using a generating AI. For example, the data collection unit can input shopping lists and receipt information into a generating AI and have the generating AI perform the integration of food information.

[0079] The analysis unit can estimate the user's emotions and adjust the way the analysis results are presented based on the estimated emotions. For example, if the user is relaxed, the analysis unit can provide detailed analysis results. If the user is stressed, the analysis unit can also provide concise analysis results. Furthermore, if the user is in a hurry, the analysis unit can provide analysis results that can be quickly understood. In this way, the analysis unit can provide analysis results that are easy for the user to understand by adjusting the way the analysis results are presented according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using a generative AI, or not using a generative AI. For example, the analysis unit can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0080] The analysis unit can analyze the nutritional value and allergy information of foods and suggest a consumption order tailored to the user's health condition. For example, if the user has allergies, the analysis unit can suggest a consumption order that avoids allergenic foods. Furthermore, if the user is on a diet, the analysis unit can suggest a consumption order that prioritizes low-calorie foods. Additionally, if the user's goal is health maintenance, the analysis unit can suggest a consumption order that prioritizes foods with balanced nutritional value. In this way, the analysis unit can support a healthy diet by suggesting a consumption order tailored to the user's health condition. Some or all of the above processing in the analysis unit may be performed using, for example, a generating AI, or without a generating AI. For example, the analysis unit can input the nutritional value and allergy information of foods into a generating AI and have the generating AI suggest a consumption order.

[0081] The analysis unit can analyze the user's food purchase history and predict the optimal order of consumption based on past consumption patterns. For example, the analysis unit can prioritize suggesting foods that the user has frequently consumed in the past. The analysis unit can also predict the optimal order of consumption based on the user's past consumption patterns. Furthermore, the analysis unit can prioritize suggesting foods that are nearing their expiration date based on the user's purchase history. In this way, the analysis unit can reduce food waste by predicting the optimal order of consumption based on past consumption patterns. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the user's purchase history data into a generative AI and have the generative AI perform the prediction of the order of consumption.

[0082] The analysis unit can estimate the user's emotions and adjust the level of detail in the analysis results based on the estimated emotions. For example, if the user is relaxed, the analysis unit can provide detailed analysis results. If the user is stressed, the analysis unit can also provide concise analysis results. Furthermore, if the user is in a hurry, the analysis unit can provide analysis results that can be quickly understood. In this way, the analysis unit can provide the user with appropriate information by adjusting the level of detail in the analysis results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using a generative AI, or not using a generative AI. For example, the analysis unit can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0083] The analysis unit can analyze the origin and producer information of food products and propose a consumption order that suits the user's preferences. For example, if the user prefers food products from a particular origin, the analysis unit will prioritize suggesting products from that origin. Furthermore, if the user prefers food products from a particular producer, the analysis unit can prioritize suggesting products from that producer. In addition, the analysis unit can propose an optimal consumption order based on the user's preferences. This allows the analysis unit to improve user satisfaction by proposing a consumption order that suits the user's preferences. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the origin and producer information of food products into a generative AI and have the generative AI propose a consumption order.

[0084] The analysis unit can switch analysis algorithms according to the food storage method. For example, in the case of frozen foods, the analysis unit considers the expiration date after thawing. In the case of refrigerated foods, the analysis unit can also consider the temperature and humidity inside the refrigerator. Furthermore, in the case of room-temperature stored foods, the analysis unit can also consider the storage environment. As a result, the analysis unit can provide more accurate analysis results by performing analysis according to the food storage method. Some or all of the above processing in the analysis unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the analysis unit can input food storage method data into a generating AI and have the generating AI execute the switching of analysis algorithms.

[0085] The reminder unit can estimate the user's emotions and adjust the timing of reminders based on the estimated emotions. For example, if the user is relaxed, the reminder unit can increase the frequency of reminders. Conversely, if the user is stressed, the reminder unit can decrease the frequency of reminders. Furthermore, if the user is in a hurry, the reminder unit can shorten the timing of reminders. In this way, the reminder unit can reduce the burden on the user by adjusting the timing of reminders according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reminder unit may be performed using a generative AI, or not. For example, the reminder unit can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0086] The reminder unit can suggest the optimal timing for consumption, taking into account the user's schedule, when sending a reminder. For example, the reminder unit can suggest the optimal timing for consumption according to the user's schedule. Furthermore, if the user is busy, the reminder unit can suggest foods that can be easily consumed. Additionally, if the user has more time, the reminder unit can suggest foods that require more preparation time. In this way, the reminder unit can reduce food waste by suggesting consumption timings that align with the user's schedule. Some or all of the above processing in the reminder unit may be performed using, for example, a generative AI, or without one. For example, the reminder unit can input the user's schedule data into a generative AI and have the generative AI suggest consumption timings.

[0087] The reminder unit can select the optimal reminder method by referring to the user's past reminder history when sending a reminder. For example, the reminder unit may prioritize suggesting reminder methods that the user has preferred to use in the past. The reminder unit can also select the optimal reminder method from the user's past reminder history. Furthermore, the reminder unit can adjust the timing of reminders based on the user's reminder history. In this way, the reminder unit can provide the optimal reminder method by referring to the user's past reminder history. Some or all of the above processing in the reminder unit may be performed using, for example, a generation AI, or without a generation AI. For example, the reminder unit can input the user's reminder history data into a generation AI and have the generation AI select the reminder method.

[0088] The reminder unit can estimate the user's emotions and determine the priority of reminders based on the estimated emotions. For example, if the user is stressed, the reminder unit will prioritize only important reminders. It can also prioritize detailed reminders if the user is relaxed. Furthermore, if the user is in a hurry, it can prioritize reminders that can be quickly understood. In this way, the reminder unit can prioritize important reminders by determining the priority of reminders 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 may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reminder unit may be performed using a generative AI, or not. For example, the reminder unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0089] The reminder unit can adjust the notification method according to the user's device when a reminder is given. For example, if the user is using a smartphone, the reminder unit will send a push notification. The reminder unit can also provide a reminder optimized for a larger screen if the user is using a tablet. Furthermore, if the user is using a smartwatch, the reminder unit can send a vibration notification. This allows the reminder unit to enhance the effectiveness of reminders by providing notification methods tailored to the user's device. Some or all of the above processing in the reminder unit may be performed using, for example, a generative AI, or without one. For example, the reminder unit can input the user's device information into the generative AI and have the generative AI adjust the notification method.

[0090] The reminder function can send notifications to the user's family and roommates when reminders are given, enabling collaborative food management. For example, the reminder function can send reminder notifications to all members of the user's family for collaborative food management. It can also send reminder notifications to roommates to share food consumption plans. Furthermore, the reminder function can suggest the optimal reminder timing, taking into account the schedules of family members and roommates. This allows the reminder function to reduce food waste by enabling collaborative food management with family members and roommates. Some or all of the above processing in the reminder function may be performed using, for example, a generative AI, or without a generative AI. For example, the reminder function can input schedule data of family members and roommates into a generative AI and have the generative AI suggest reminder timings.

[0091] The VR display unit can estimate the user's emotions and adjust the VR display interface based on the estimated emotions. For example, if the user is relaxed, the VR display unit can provide an interface that displays detailed information. If the user is stressed, the VR display unit can also provide a simple and highly visible interface. Furthermore, if the user is in a hurry, the VR display unit can provide an interface that allows for quick information retrieval. In this way, the VR display unit makes visual confirmation easier by providing an interface that responds to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the VR display unit may be performed using a generative AI, or not using a generative AI. For example, the VR display unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0092] The VR display unit can optimize the arrangement of food items inside the refrigerator during VR display, making it visually easy to understand. For example, the VR display unit can organize the food items inside the refrigerator by category and display them visually. The VR display unit can also optimize the arrangement of food items, displaying frequently used items at the front. Furthermore, the VR display unit can efficiently utilize the space inside the refrigerator and optimize the arrangement of food items. As a result, the VR display unit can optimize the arrangement of food items inside the refrigerator, making it visually easy to understand. Some or all of the above processing in the VR display unit may be performed using, for example, a generation AI, or without a generation AI. For example, the VR display unit can input food arrangement data inside the refrigerator into a generation AI and have the generation AI perform the arrangement optimization.

[0093] The VR display unit can highlight food items nearing their expiration date during VR display to alert the user. For example, the VR display unit can highlight food items nearing their expiration date in red to alert the user. The VR display unit can also display food items nearing their expiration date in a pop-up to alert the user. Furthermore, the VR display unit can list food items nearing their expiration date to alert the user. In this way, the VR display unit can alert the user by highlighting food items nearing their expiration date. Some or all of the above processing in the VR display unit may be performed using, for example, a generation AI, or without a generation AI. For example, the VR display unit can input expiration date data into a generation AI and have the generation AI perform the highlighting process.

[0094] The VR display unit can estimate the user's emotions and determine the priority of the VR display based on the estimated emotions. For example, if the user is relaxed, the VR display unit can prioritize displaying detailed information. If the user is stressed, the VR display unit can also prioritize displaying only important information. Furthermore, if the user is in a hurry, the VR display unit can prioritize displaying information that can be quickly accessed. In this way, the VR display unit can prioritize the display of important information by determining the priority of the VR display according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the VR display unit may be performed using, for example, a generative AI, or without a generative AI. For example, the VR display unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0095] The VR display unit can support the user's diet by displaying nutritional information and recipe suggestions for the food in the refrigerator during VR display. For example, the VR display unit can support the user's diet by displaying nutritional information for the food in the refrigerator. The VR display unit can also support the user's diet by suggesting recipes using the food in the refrigerator. Furthermore, the VR display unit can support the user's diet by suggesting recipes that take into account the nutritional balance of the food in the refrigerator. In this way, the VR display unit can support the user's diet by displaying nutritional information and recipe suggestions for the food in the refrigerator. Some or all of the above processing in the VR display unit may be performed using, for example, a generation AI, or without a generation AI. For example, the VR display unit can input nutritional information and recipe data for the food into a generation AI and have the generation AI execute the display processing.

[0096] The VR display unit can display the location of food items in the refrigerator on a 3D map during VR display, allowing users to intuitively find food items. For example, the VR display unit can display the location of food items in the refrigerator on a 3D map, allowing users to intuitively find food items. The VR display unit can also use the 3D map to visually confirm the arrangement of food items in the refrigerator. Furthermore, the VR display unit can use the 3D map to efficiently manage the location of food items in the refrigerator. Thus, by displaying the location of food items in the refrigerator on a 3D map, the VR display unit can enable users to intuitively find food items. Some or all of the above processing in the VR display unit may be performed using, for example, a generation AI, or without a generation AI. For example, the VR display unit can input food location data from the refrigerator into a generation AI and have the generation AI generate a 3D map.

[0097] The VR display unit can optimize the arrangement of food items inside the refrigerator during VR display, making it visually easy to understand. For example, the VR display unit can organize the food items inside the refrigerator by category and display them visually. The VR display unit can also optimize the arrangement of food items, displaying frequently used items at the front. Furthermore, the VR display unit can efficiently utilize the space inside the refrigerator and optimize the arrangement of food items. As a result, the VR display unit can optimize the arrangement of food items inside the refrigerator, making it visually easy to understand. Some or all of the above processing in the VR display unit may be performed using, for example, a generation AI, or without a generation AI. For example, the VR display unit can input food arrangement data inside the refrigerator into a generation AI and have the generation AI perform the arrangement optimization.

[0098] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0099] A food waste management system can estimate a user's emotions and suggest a food consumption plan based on those emotions. For example, if a user is stressed, it can prioritize suggesting easy-to-prepare foods. If the user is relaxed, it can suggest recipes that require more time to prepare. Furthermore, if the user is in a hurry, it can suggest foods that can be eaten immediately. In this way, the food waste management system can reduce food waste by suggesting a consumption plan that is tailored to the user's emotions.

[0100] A food waste management system can analyze the nutritional value of food in a refrigerator and suggest a consumption plan tailored to the user's health condition. For example, if a user is on a diet, it can prioritize suggesting low-calorie foods. It can also suggest foods rich in specific nutrients if the user needs them. Furthermore, if a user has allergies, it can suggest a consumption plan that avoids allergenic foods. In this way, the food waste management system can support a healthy diet by suggesting a consumption plan that suits the user's health needs.

[0101] A food waste management system can propose consumption plans based on how food is stored in the refrigerator. For example, for frozen foods, it proposes a consumption plan considering the expiration date after thawing. For refrigerated foods, it can also propose a consumption plan considering the temperature and humidity inside the refrigerator. Furthermore, for foods stored at room temperature, it can propose a consumption plan considering the storage environment. In this way, a food waste management system can reduce food waste by proposing consumption plans that are appropriate to how food is stored.

[0102] The food waste management system can estimate the user's emotions and adjust the content of reminders based on those emotions. For example, if the user is stressed, a concise reminder will be given. If the user is relaxed, a more detailed reminder can be given. Furthermore, if the user is in a hurry, a quickly understandable reminder can be given. In this way, the food waste management system can reduce the burden on the user by providing reminders tailored to their emotions.

[0103] A food waste management system can optimize the placement of food in a refrigerator and display it in a visually easy-to-understand manner. For example, it can organize food in the refrigerator by category and display it in an easy-to-understand way. It can also place frequently used foods at the front so that they can be easily retrieved. Furthermore, it can efficiently utilize the space inside the refrigerator and optimize the placement of food. In this way, a food waste management system can optimize the placement of food in a refrigerator and display it in a visually easy-to-understand manner.

[0104] A food waste management system can estimate a user's emotions and suggest a food consumption order based on those emotions. For example, if a user is stressed, it can prioritize suggesting foods that are easy to prepare. If a user is relaxed, it can suggest recipes that require more time to cook. Furthermore, if a user is in a hurry, it can suggest foods that can be eaten immediately. In this way, the food waste management system can reduce food waste by suggesting a consumption order that suits the user's emotions.

[0105] A food waste management system can analyze the nutritional information of food in the refrigerator and suggest recipes tailored to the user's health condition. For example, if a user is on a diet, it can suggest low-calorie recipes. It can also suggest recipes rich in specific nutrients if the user needs them. Furthermore, if a user has allergies, it can suggest recipes that avoid allergenic foods. In this way, the food waste management system can support a healthy diet by suggesting recipes that suit the user's health needs.

[0106] The food waste management system can estimate the user's emotions and adjust the timing of reminders based on those emotions. For example, if the user is relaxed, the frequency of reminders can be increased. Conversely, if the user is stressed, the frequency of reminders can be decreased. Furthermore, if the user is in a hurry, the reminder interval can be shortened. In this way, the food waste management system can reduce the burden on the user by adjusting the timing of reminders according to the user's emotions.

[0107] A food waste management system can highlight the expiration dates of food in the refrigerator to alert users. For example, it can highlight food nearing its expiration date in red to draw user attention. It can also display food nearing its expiration date in a pop-up to alert users. Furthermore, it can list food nearing its expiration date to alert users. In this way, a food waste management system can alert users by highlighting food nearing its expiration date.

[0108] A food waste management system can estimate a user's emotions and prioritize reminders based on those emotions. For example, if a user is stressed, only important reminders will be prioritized. If a user is relaxed, detailed reminders may be prioritized. Furthermore, if a user is in a hurry, reminders that can be quickly understood may be prioritized. In this way, the food waste management system can prioritize important reminders by determining reminder priorities according to the user's emotions.

[0109] The following briefly describes the processing flow for example form 2.

[0110] Step 1: The collection unit collects information about the food inside the refrigerator. The collection unit can collect information about the food using, for example, a camera installed inside the refrigerator. Alternatively, the collection unit can collect information about the food using sensors or manual input. Step 2: The analysis unit analyzes the information collected by the collection unit and reminds users of the shelf life and consumption order of food items. The analysis unit can, for example, use generative AI to analyze the collected information and identify the type of food item. The analysis unit can also analyze the information using image analysis, data mining, and machine learning algorithms. Step 3: The reminder unit provides the user with the information reminded by the analysis unit. The reminder unit can, for example, set the notification method, timing, and frequency to send reminders to the user. The reminder unit can also customize the reminder content based on the user's preferences and behavioral patterns. Step 4: The VR display unit displays the information provided by the reminder unit in the VR space. For example, the VR display unit allows the user to visually check the inside of the refrigerator using a VR device. The VR display unit can also display the arrangement and condition of food inside the refrigerator in a 3D map.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] Each of the multiple elements described above, including the data collection unit, analysis unit, reminder unit, and VR display unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit collects food information in the refrigerator using the camera 42 of the smart device 14. The analysis unit is implemented in the identification processing unit 290 of the data processing unit 12, for example, and analyzes the collected information to remind the user of the food's shelf life and consumption order. The reminder unit is implemented in the control unit 46A of the smart device 14, for example, and notifies the user. The VR display unit displays food information in the refrigerator in a VR space using the output device 40 of the smart device 14, for example. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0115] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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).

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] Each of the multiple elements described above, including the data collection unit, analysis unit, reminder unit, and VR display unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit collects food information in the refrigerator using the camera 42 of the smart glasses 214. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which analyzes the collected information and reminds the user of the food's shelf life and consumption order. The reminder unit is implemented, for example, by the control unit 46A of the smart glasses 214, which notifies the user. The VR display unit displays food information in the refrigerator in a VR space using, for example, the output device 40 of the smart glasses 214. The correspondence between each unit and the devices and control units is not limited to the example described above, and various modifications are possible.

[0131] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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).

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] Each of the multiple elements described above, including the data collection unit, analysis unit, reminder unit, and VR display unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit collects food information in the refrigerator using the camera 42 of the headset terminal 314. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which analyzes the collected information and reminds the user of the food's shelf life and consumption order. The reminder unit is implemented, for example, by the control unit 46A of the headset terminal 314, which notifies the user. The VR display unit displays food information in the refrigerator in a VR space using, for example, the output device 40 of the headset terminal 314. The correspondence between each unit and the devices and control units is not limited to the example described above, and various modifications are possible.

[0147] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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).

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.).

[0160] 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.

[0161] 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.

[0162] 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.

[0163] Each of the multiple elements described above, including the data collection unit, analysis unit, reminder unit, and VR display unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the data collection unit collects food information in the refrigerator using the camera 42 of the robot 414. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which analyzes the collected information and reminds the user of the food's shelf life and consumption order. The reminder unit is implemented, for example, by the control unit 46A of the robot 414, which notifies the user. The VR display unit displays food information in the refrigerator in a VR space using, for example, the output device 40 of the robot 414. The correspondence between each unit and the devices and control units is not limited to the example described above, and various modifications are possible.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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."

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] (Note 1) A collection unit that collects information on food inside the refrigerator, The information collected by the aforementioned collection unit is analyzed by an analysis unit that reminds users of the storage period and consumption order of food items. A reminder unit provides the user with the information reminded by the analysis unit, The system includes a VR display unit that displays the information provided by the reminder unit in a VR space. A system characterized by the following features. (Note 2) The aforementioned collection unit is Collect information from a camera installed inside the refrigerator. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, The collected information is analyzed to identify the type of food. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned reminder unit, Based on the analyzed information, reminders will be sent regarding food storage periods and consumption order. The system described in Appendix 1, characterized by the features described herein. (Note 5) The VR display unit is Allows users to visually inspect the inside of a refrigerator in a VR space. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reminder unit, Provides users with information to help them plan their food consumption. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is The system estimates the user's emotions and adjusts the timing of food information collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is The temperature and humidity inside the refrigerator are monitored, and the collection frequency is adjusted according to the degree of food spoilage. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is Scan the QR code or barcode on food packaging to collect detailed food information. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is The system estimates the user's emotions and prioritizes the food information to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is A different camera is installed on each shelf inside the refrigerator to collect individual information about the food items on each shelf. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is Collect shopping lists and receipt information from outside the refrigerator and integrate it with food information from inside the refrigerator. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, It estimates the user's emotions and adjusts the way the analysis results are presented based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, It analyzes the nutritional value and allergy information of foods and suggests a consumption order tailored to the user's health condition. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, By analyzing food purchase history, we predict the optimal order of consumption based on past consumption patterns. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, It estimates the user's emotions and adjusts the level of detail in the analysis results based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, We analyze information on the origin and producers of food products and suggest consumption orders tailored to the user's preferences. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, The analysis algorithm is switched depending on the food storage method. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned reminder unit, It estimates the user's emotions and adjusts the timing of reminders based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned reminder unit, When sending a reminder, the system will suggest the optimal timing for consumption, taking the user's schedule into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned reminder unit, When sending a reminder, the system will refer to the user's past reminder history to select the most suitable reminder method. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned reminder unit, It estimates the user's emotions and determines the priority of reminders based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned reminder unit, When sending reminders, adjust the notification method according to the user's device. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned reminder unit, When a reminder is sent, notifications are also sent to the user's family and roommates, allowing for collaborative food management. The system described in Appendix 1, characterized by the features described herein. (Note 25) The VR display unit is It estimates the user's emotions and adjusts the VR display interface based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The VR display unit is When displayed in VR, the arrangement of food items inside the refrigerator is optimized for a visually clearer display. The system described in Appendix 1, characterized by the features described herein. (Note 27) The VR display unit is When displayed in VR, food items nearing their expiration date are highlighted to alert the user. The system described in Appendix 1, characterized by the features described herein. (Note 28) The VR display unit is It estimates the user's emotions and determines the priority of VR displays based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The VR display unit is When viewed in VR, the system displays nutritional information and recipe suggestions for the food in the refrigerator, supporting the user's eating habits. The system described in Appendix 1, characterized by the features described herein. (Note 30) The VR display unit is When using VR, the location of food items inside the refrigerator will be displayed on a 3D map, allowing users to intuitively find what they're looking for. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0183] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A collection unit that collects information on food inside the refrigerator, The information collected by the aforementioned collection unit is analyzed by an analysis unit that reminds users of the storage period and consumption order of food items. A reminder unit provides the user with the information reminded by the analysis unit, The system includes a VR display unit that displays the information provided by the reminder unit in a VR space. A system characterized by the following features.

2. The aforementioned collection unit is Collect information from a camera installed inside the refrigerator. The system according to feature 1.

3. The aforementioned analysis unit, The collected information is analyzed to identify the type of food. The system according to feature 1.

4. The aforementioned reminder unit, Based on the analyzed information, reminders will be sent regarding food storage periods and consumption order. The system according to feature 1.

5. The VR display unit is Allows users to visually inspect the inside of a refrigerator in a VR space. The system according to feature 1.

6. The aforementioned reminder unit, Provides users with information to help them plan their food consumption. The system according to feature 1.

7. The aforementioned collection unit is The system estimates the user's emotions and adjusts the timing of food information collection based on those estimated emotions. The system according to feature 1.

8. The aforementioned collection unit is The temperature and humidity inside the refrigerator are monitored, and the collection frequency is adjusted according to the degree of food spoilage. The system according to feature 1.

9. The aforementioned collection unit is Scan the barcode on the food packaging to collect detailed food information. The system according to feature 1.

10. The aforementioned collection unit is The system estimates the user's emotions and prioritizes the food information to collect based on those estimated emotions. The system according to feature 1.

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