system

Abstract

The system according to this embodiment aims to efficiently perform tasks ranging from managing food inventory and suggesting recipes to automatically ordering ingredients, sharing within a community, and linking with AI-powered cooking appliances. [Solution] The system according to the embodiment comprises a recognition unit, a suggestion unit, an ordering unit, a sharing unit, and an interlocking unit. The recognition unit recognizes food inventory. The suggestion unit suggests recipes based on the information recognized by the recognition unit. The ordering unit automatically orders any missing ingredients based on the recipes suggested by the suggestion unit. The sharing unit shares the ingredients ordered by the ordering unit within the community. The interlocking unit interacts with AI cooking appliances based on the recipes suggested by the suggestion unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, 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 that responds 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, the management of food inventory, recipe proposal, automatic ordering of ingredients, sharing within a community, and linkage with AI cooking appliances are not sufficiently performed, and there is room for improvement.

[0005] The system according to the embodiment aims to efficiently perform from the management of food inventory to recipe proposal, automatic ordering of ingredients, sharing within a community, and linkage with AI cooking appliances.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a recognition unit, a suggestion unit, an ordering unit, a sharing unit, and an interlocking unit. The recognition unit recognizes food inventory. The suggestion unit suggests recipes based on the information recognized by the recognition unit. The ordering unit automatically orders any missing ingredients based on the recipes suggested by the suggestion unit. The sharing unit shares the ingredients ordered by the ordering unit within the community. The interlocking unit interacts with AI cooking appliances based on the recipes suggested by the suggestion unit. [Effects of the Invention]

[0007] The system according to this embodiment can efficiently handle everything from managing food inventory and suggesting recipes to automatically ordering ingredients, sharing within a community, and linking with AI-powered cooking appliances. [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 manages communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The food management system according to an embodiment of the present invention is an innovative system for homes and small restaurants that combines AI technology with the latest IoT devices. This food management system helps homes, restaurants, and retailers efficiently reduce food waste. The food management system allows users to automatically recognize food inventory in their refrigerators using a camera, and easily register food outside the refrigerator by scanning barcodes. This makes it possible to understand food expiration dates and inventory status in real time. Next, the AI ​​suggests recipes tailored to the user's preferences, health condition, and the season, and automatically orders any missing ingredients. This ordering is coordinated with local retailers and other partners to ensure prompt food delivery. Furthermore, a system is introduced to share food nearing its expiration date within the community, reducing food waste by sharing food with nearby homes and restaurants. In addition, by linking with AI cooking appliances (such as smart microwave ovens and rice cookers), the cooking process is automated and optimized, saving time and effort in cooking. This makes it easy for busy homes and restaurants to provide delicious meals. For example, the food management system allows users to automatically recognize food inventory in their refrigerators using a camera, and register food outside the refrigerator by scanning barcodes. This information is analyzed by AI, allowing for real-time tracking of food expiration dates and inventory levels. Next, the AI ​​suggests recipes tailored to the user's preferences, health status, and the season. For example, if the user is health-conscious, it will suggest low-calorie recipes. Next, it automatically orders any missing ingredients. The AI ​​analyzes the user's inventory and orders necessary ingredients from local retailers, saving the user the trouble of going shopping. Next, it introduces a system for sharing food nearing its expiration date within the community. For example, if a user has food nearing its expiration date, it can notify nearby households and restaurants to share the food. Finally, by linking with AI-powered cooking appliances, it automates and optimizes the cooking process. For example, a smart microwave can automatically cook based on a recipe, saving the user the trouble of cooking. In this way, the food management system can streamline food management in homes and small restaurants and reduce food waste.This allows food management systems to streamline food management in homes and small restaurants, and reduce food waste.

[0029] The food management system according to this embodiment comprises a recognition unit, a suggestion unit, an ordering unit, a sharing unit, and an interlocking unit. The recognition unit recognizes food inventory. For example, the recognition unit automatically recognizes food inventory inside a refrigerator using a camera. The recognition unit can also register food outside the refrigerator by barcode scanning. For example, the recognition unit photographs food inside the refrigerator with a camera and identifies the food using image recognition technology. The recognition unit can also scan food outside the refrigerator using a barcode reader and register it in the database. The suggestion unit suggests recipes based on the information recognized by the recognition unit. For example, the suggestion unit suggests recipes that suit the user's preferences, health condition, and the season. For example, the suggestion unit suggests recipes that the user will like based on the user's past selection history. The suggestion unit can also suggest low-calorie or high-nutrient recipes considering the user's health condition. The suggestion unit can also suggest recipes using seasonal ingredients. The ordering unit automatically orders any missing ingredients based on the recipes suggested by the suggestion unit. The ordering unit analyzes the user's inventory status and orders necessary ingredients from local retailers. For example, the ordering unit automatically lists necessary ingredients based on the user's inventory data and places orders. The ordering unit can also collaborate with local retailers to deliver food quickly. The sharing unit shares the ingredients ordered by the ordering unit within the community. For example, the sharing unit can notify nearby households and restaurants of food nearing its expiration date and share the food. For example, the sharing unit sends notifications to users who have food nearing its expiration date, encouraging them to share the food with nearby households and restaurants. The sharing unit can also share food through a digital platform. The integration unit interacts with AI cooking appliances based on recipes suggested by the suggestion unit. For example, the integration unit interacts with AI cooking appliances such as smart microwaves and rice cookers to automate and optimize the cooking process. For example, the integration unit automatically sets up the smart microwave based on the recipe and starts cooking. The integration unit can also automatically adjust the settings of the rice cooker to perform optimal rice cooking.As a result, the food management system according to this embodiment will be able to recognize food inventory, suggest recipes, automatically order ingredients, share within the community, and link with AI cooking appliances.

[0030] The recognition unit recognizes food inventory. For example, the recognition unit automatically recognizes food inventory inside a refrigerator using a camera. Specifically, a high-resolution camera installed inside the refrigerator periodically takes images of the interior, and these images are analyzed using image recognition technology. The image recognition technology uses an object detection algorithm based on deep learning, which can accurately identify the type and quantity of food. For example, it can individually recognize vegetables, fruits, meats, dairy products, etc., inside the refrigerator and record the inventory status of each in a database. The recognition unit can also register food outside the refrigerator by scanning its barcode. By reading the barcode of food purchased by the user with a dedicated scanner, information such as the type of food, expiration date, and purchase date is automatically registered in the database. This makes it possible to centrally manage food inventory both inside and outside the refrigerator. Furthermore, the recognition unit can also track the location information of food in real time using RFID tags. This improves the accuracy of inventory management, such as issuing alerts when food is nearing its expiration date.

[0031] The suggestion unit proposes recipes based on information recognized by the recognition unit. For example, the suggestion unit proposes recipes tailored to the user's preferences, health condition, and the season. Specifically, it utilizes generative AI to generate optimal recipes for each individual user by analyzing the user's past selection history and eating habits. The generative AI considers the user's preferences, allergy information, and nutritional balance to generate the best possible recipe. For example, it learns the user's preferred seasonings and cooking methods based on data from recipes and ingredients the user has previously selected, and proposes new recipes based on that. The suggestion unit can also propose low-calorie or high-nutrient recipes, taking the user's health condition into consideration. For example, if the user is on a diet, it will prioritize suggesting recipes using low-calorie ingredients. The suggestion unit can also propose recipes using seasonal ingredients. For example, it will suggest cold dishes and light-flavored recipes in the summer, and warm dishes and highly nutritious recipes in the winter. In this way, the suggestion unit can provide recipes that meet the diverse needs of users and improve the quality of their diet.

[0032] The ordering department automatically orders any missing ingredients based on the recipes proposed by the suggestion department. For example, the ordering department analyzes the user's inventory status and orders the necessary ingredients from local retailers. Specifically, based on inventory data provided by the recognition department, it generates a list of ingredients required for the suggested recipe and automatically identifies ingredients that are out of stock. The ordering department has a system in place to automatically order these missing ingredients from local retailers and online stores. For example, if an ingredient required for a recipe chosen by the user is missing from the refrigerator, the ordering department automatically lists that ingredient and places an order with a partner retailer. The ordering department can also select the best supplier based on the user's preferences and budget. Furthermore, the ordering department can manage delivery schedules and adjust them so that ingredients arrive at the date and time desired by the user. This allows users to secure the necessary ingredients without hassle, resulting in efficient ingredient management.

[0033] The Sharing Department shares food ordered by the Ordering Department within the community. For example, the Sharing Department can notify nearby households and restaurants of food nearing its expiration date, allowing for food sharing. Specifically, the Sharing Department sends notifications to users who have food nearing its expiration date, encouraging them to share it with nearby households and restaurants. The Sharing Department can facilitate food sharing through a digital platform. This platform provides a system where users can register information about their surplus food, and other users can view this information and request the food they need. For example, if a user registers food nearing its expiration date, a notification is sent to nearby users who can request the food they need. The Sharing Department also coordinates the method and location of food delivery to support smooth sharing. This reduces food waste and promotes the effective use of food resources within the community. Furthermore, the Sharing Department has a system in place to manage food sharing history and evaluate user contributions and trustworthiness. This builds trust among users and enables smoother sharing.

[0034] The interconnected unit interacts with AI cooking appliances based on recipes proposed by the suggestion unit. The interconnected unit interacts with AI cooking appliances such as smart microwave ovens and rice cookers, automating and optimizing the cooking process. Specifically, the interconnected unit transmits the cooking procedure and setting information of the proposed recipe to the AI ​​cooking appliance, automatically starting cooking. For example, the interconnected unit automatically configures the smart microwave oven based on the recipe, starting cooking at the appropriate temperature and time. It can also automatically adjust the settings of a rice cooker for optimal cooking. Furthermore, the interconnected unit monitors the progress of cooking in real time and makes adjustments as needed. For example, it can detect changes in temperature and humidity during cooking and make appropriate adjustments to maintain the quality of the food. This allows users to easily prepare delicious meals and improve the quality of their diet. Additionally, the interconnected unit manages maintenance information and usage history of cooking appliances, prompting maintenance at the appropriate time. This extends the lifespan of cooking appliances and enables efficient operation.

[0035] The recognition unit can automatically recognize food inventory inside the refrigerator using a camera and register food outside the refrigerator by scanning its barcode. For example, the recognition unit can photograph food inside the refrigerator with a camera and identify the food using image recognition technology. The recognition unit can also scan food outside the refrigerator using a barcode reader and register it in a database. For example, the recognition unit can photograph food inside the refrigerator with a high-resolution camera, and AI can analyze the image to identify the food. The recognition unit can also scan food outside the refrigerator using a barcode reader, and AI can analyze the barcode information to identify the food. This allows for efficient recognition and registration of food inventory inside and outside the refrigerator. Some or all of the above-described processes in the recognition unit may be performed using AI or not. For example, the recognition unit can input image data captured by the camera into a generating AI and have the generating AI identify food from the image data.

[0036] The suggestion unit can propose recipes tailored to the user's preferences, health condition, and the season. For example, the suggestion unit can suggest recipes the user will like based on the user's past selection history. It can also suggest low-calorie or high-nutrient recipes considering the user's health condition. Furthermore, it can suggest recipes using seasonal ingredients. This allows the suggestion unit to propose recipes tailored to the user's preferences, health condition, and the season. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input the user's past selection history into a generating AI and have the generating AI suggest recipes the user will like.

[0037] The ordering department can work with local retailers to deliver food quickly. For example, the ordering department can analyze the user's inventory status and order the necessary ingredients from local retailers. The ordering department can also work with local retailers to deliver food quickly. For example, the ordering department can automatically list the necessary ingredients based on the user's inventory data and place an order. The ordering department can also work with local retailers to deliver food quickly. This allows for quick food delivery in cooperation with local retailers. Some or all of the above processes in the ordering department may be performed using AI or not. For example, the ordering department can input the user's inventory data into a generating AI and have the generating AI execute the order for the necessary ingredients.

[0038] The sharing function allows users to share food nearing its expiration date within the community. For example, the sharing function can notify nearby households and restaurants about food nearing its expiration date, thereby sharing the food. The sharing function can also send notifications to users who have food nearing its expiration date, encouraging them to share it with nearby households and restaurants. Furthermore, the sharing function can facilitate food sharing through a digital platform. This allows for the sharing of food nearing its expiration date within the community. Some or all of the above processes in the sharing function may be performed using AI or not. For example, the sharing function can input information about food nearing its expiration date into a generating AI and have the generating AI execute sharing notifications.

[0039] The interlocking unit can work in conjunction with AI cooking appliances such as smart microwave ovens and rice cookers to automate and optimize the cooking process. For example, the interlocking unit can automatically configure the settings of a smart microwave oven based on a recipe and start cooking. It can also automatically adjust the settings of a rice cooker to perform optimal rice cooking. In this way, the cooking process can be automated and optimized in conjunction with AI cooking appliances. Some or all of the above-described processes in the interlocking unit may be performed using AI or not. For example, the interlocking unit can input recipe information into a generating AI and have the generating AI execute the settings for the cooking appliances.

[0040] The recognition unit can automatically detect changes in the shape and packaging of food products and improve recognition accuracy. For example, if a new packaging design is introduced, the AI ​​in the recognition unit can automatically learn and improve recognition accuracy. The recognition unit can also detect changes in the shape of food products and adjust the recognition algorithm accordingly. Furthermore, the recognition unit can detect changes in the color and label of the packaging and improve recognition accuracy. In this way, it can detect changes in the shape and packaging of food products and improve recognition accuracy. Some or all of the above-described processes in the recognition unit may be performed using AI or not. For example, the recognition unit can input data on changes in the shape and packaging of food products into a generating AI and have the generating AI perform the improvement of recognition accuracy.

[0041] The recognition unit can correct the recognition result by taking into account the storage conditions of the food (temperature and humidity). For example, if the temperature inside the refrigerator is high, the recognition unit will correct the recognition result by taking into account the deterioration of the food. The recognition unit can also correct the recognition result by taking into account the storage conditions of the food if the humidity is high. Furthermore, in the case of frozen food, the recognition unit can also correct the recognition result by taking into account the thawing state. In this way, the recognition result can be corrected by taking into account the storage conditions of the food. Some or all of the above processing in the recognition unit may be performed using AI or not. For example, the recognition unit can input storage condition data into a generating AI and have the generating AI perform the correction of the recognition result.

[0042] The recognition unit can supplement the recognition results by referring to the user's purchase history. For example, the recognition unit can supplement the recognition results based on the food items the user has purchased in the past. The recognition unit can also prioritize the recognition of frequently purchased food items from the user's purchase history. Furthermore, the recognition unit can analyze the user's purchase history and optimize the recognition results. This allows the recognition results to be supplemented by referring to the user's purchase history. Some or all of the above processing in the recognition unit may be performed using AI or not. For example, the recognition unit can input the user's purchase history data into a generating AI and have the generating AI perform the supplementation of the recognition results.

[0043] The recognition unit can automatically acquire nutritional information of food and provide it to the user. For example, the recognition unit can automatically acquire nutritional information of recognized food and provide it to the user. The recognition unit can also automatically acquire calorie information of recognized food and provide it to the user. Furthermore, the recognition unit can automatically acquire allergy information of recognized food and provide it to the user. This enables the automatic acquisition and provision of nutritional information of food to the user. Some or all of the above processing in the recognition unit may be performed using AI or not. For example, the recognition unit can input nutritional information data of food into a generating AI and have the generating AI perform the acquisition of nutritional information.

[0044] The suggestion unit can analyze the user's past recipe selection history and propose the most suitable recipe. For example, the suggestion unit can propose the most suitable recipe based on the recipe the user has selected in the past. The suggestion unit can also analyze the user's preferences from their past recipe selection history and make suggestions. Furthermore, the suggestion unit can propose seasonal recipes based on the user's past recipe selection history. In this way, the system can analyze the user's past recipe selection history and propose the most suitable recipe. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input the user's past recipe selection history data into a generating AI and have the generating AI propose the most suitable recipe.

[0045] The suggestion unit can customize recipes by considering the nutritional value and calorie information of the food. For example, the suggestion unit can suggest low-calorie recipes tailored to the user's health condition. It can also suggest high-nutrient recipes considering the user's nutritional balance. Furthermore, it can suggest calorie-restricted recipes tailored to the user's weight loss goals. This allows for the customization of recipes by considering the nutritional value and calorie information of the food. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input nutritional value and calorie information data of the food into a generating AI and have the generating AI perform the recipe customization.

[0046] The suggestion unit can filter recipes considering the user's allergy information. For example, the suggestion unit can suggest recipes that do not contain allergens based on the user's allergy information. The suggestion unit can also suggest recipes that use alternative ingredients, taking the user's allergy information into consideration. Furthermore, the suggestion unit can prioritize displaying recipes that do not contain allergens based on the user's allergy information. This allows for recipe filtering that takes the user's allergy information into account. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input the user's allergy information data into a generating AI and have the generating AI perform recipe filtering.

[0047] The suggestion unit can propose appropriate recipes according to the user's meal times. For example, at breakfast, the suggestion unit can propose simple and nutritious recipes. It can also propose balanced recipes at lunchtime. Furthermore, it can propose relaxing recipes at dinnertime. This allows the suggestion unit to propose appropriate recipes according to the user's meal times. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input the user's meal time data into a generating AI and have the generating AI propose appropriate recipes.

[0048] The ordering department can analyze a user's past purchase history and determine the optimal order quantity. For example, the ordering department can determine the optimal order quantity based on the user's past purchase history. The ordering department can also analyze the user's purchasing patterns and adjust the order quantity. Furthermore, the ordering department can predict seasonal order quantities from the user's past purchase history. This allows the ordering department to analyze the user's past purchase history and determine the optimal order quantity. Some or all of the above processes in the ordering department may be performed using AI or not. For example, the ordering department can input the user's past purchase history data into a generating AI and have the generating AI determine the optimal order quantity.

[0049] The ordering department can optimize orders by considering the shelf life of food products. For example, the ordering department can prioritize ordering food products with short shelf lives. It can also order food products with long shelf lives in appropriate quantities. Furthermore, the ordering department can adjust order contents by considering the shelf life. This allows for the optimization of order contents by considering the shelf life of food products. Some or all of the above processes in the ordering department may be performed using AI or not. For example, the ordering department can input food shelf life data into a generating AI and have the generating AI perform the optimization of order contents.

[0050] The ordering department can select the optimal delivery method considering the user's geographical location. For example, the ordering department can select the optimal delivery method based on the user's geographical location. The ordering department can also select a fast delivery method considering the user's geographical location. Furthermore, the ordering department can select a cost-effective delivery method based on the user's geographical location. This allows the ordering department to select the optimal delivery method considering the user's geographical location. Some or all of the above processing in the ordering department may be performed using AI or not. For example, the ordering department can input the user's geographical location data into a generating AI and have the generating AI select the optimal delivery method.

[0051] The ordering department can analyze user purchasing patterns and concentrate orders during specific time periods. For example, the ordering department can analyze user purchasing patterns and determine the optimal time for placing orders. It can also select time periods for concentrating orders based on user purchasing patterns. Furthermore, the ordering department can adjust the timing of orders, taking user purchasing patterns into consideration. This allows for the analysis of user purchasing patterns and the concentration of orders during specific time periods. Some or all of the above processes in the ordering department may be performed using AI or not. For example, the ordering department can input user purchasing pattern data into a generating AI and have the generating AI adjust the timing of orders.

[0052] The sharing unit can analyze a user's past sharing history and suggest the most suitable sharing partner. For example, the sharing unit can suggest the most suitable sharing partner based on the user's past sharing history. The sharing unit can also suggest a trustworthy partner based on the user's sharing history. Furthermore, the sharing unit can analyze the user's sharing history and suggest the most efficient sharing partner. This allows the sharing unit to analyze the user's past sharing history and suggest the most suitable sharing partner. Some or all of the above processing in the sharing unit may be performed using AI or not. For example, the sharing unit can input the user's sharing history data into a generating AI and have the generating AI suggest the most suitable sharing partner.

[0053] The sharing unit can select the optimal sharing partner by considering the user's geographical location information. For example, the sharing unit can select the optimal sharing partner based on the user's geographical location information. The sharing unit can also select a sharing partner quickly by considering the user's geographical location information. Furthermore, the sharing unit can select a reliable sharing partner based on the user's geographical location information. This allows the optimal sharing partner to be selected by considering the user's geographical location information. Some or all of the above processing in the sharing unit may be performed using AI or not. For example, the sharing unit can input the user's geographical location information data into a generating AI and have the generating AI perform the selection of the optimal sharing partner.

[0054] The linked unit can analyze the user's past cooking history and suggest the optimal cooking method. For example, the linked unit can suggest the optimal cooking method based on the user's past cooking history. The linked unit can also analyze the user's cooking history to suggest their preferred cooking method. Furthermore, the linked unit can suggest a cooking method appropriate for the season based on the user's past cooking history. In this way, the system can analyze the user's past cooking history and suggest the optimal cooking method. Some or all of the above processing in the linked unit may be performed using AI or not. For example, the linked unit can input the user's past cooking history data into a generating AI and have the generating AI suggest the optimal cooking method.

[0055] The interconnected unit can automatically adjust the settings of cooking appliances according to the type and quantity of food. For example, the interconnected unit can automatically adjust the temperature setting of cooking appliances according to the type of food. It can also automatically adjust the cooking time according to the quantity of food. Furthermore, the interconnected unit can automatically determine the optimal cooking settings considering the type and quantity of food. This allows for the automatic adjustment of cooking appliance settings according to the type and quantity of food. Some or all of the above-described processes in the interconnected unit may be performed using AI or not. For example, the interconnected unit can input data on the type and quantity of food into a generating AI and have the generating AI perform the adjustment of the cooking appliance settings.

[0056] The interlocking unit can propose the optimal operating method considering the user's device information. For example, if the user is using a smartphone, the interlocking unit can provide an operating method adapted to the screen size. It can also provide an operating method optimized for a larger screen if the user is using a tablet. Furthermore, if the user is using a smartwatch, the interlocking unit can provide a concise and highly visible operating method. This allows the system to propose the optimal operating method considering the user's device information. Some or all of the above processing in the interlocking unit may be performed using AI, or without AI. For example, the interlocking unit can input user device information data into a generating AI and have the generating AI propose the optimal operating method.

[0057] The interlocking unit can monitor the operating status of cooking appliances in real time and notify the user if an abnormality is detected. For example, the interlocking unit can monitor the operating status of cooking appliances in real time and notify the user if an abnormality is detected. The interlocking unit can also notify the user in real time if there is an abnormality in the temperature or time setting of the cooking appliance. Furthermore, the interlocking unit can immediately notify the user if the operation of the cooking appliance stops. This allows for real-time monitoring of the operating status of cooking appliances and notification of abnormalities if detected. Some or all of the above processing in the interlocking unit may be performed using AI or not. For example, the interlocking unit can input cooking appliance operation data into a generating AI and have the generating AI perform abnormality detection and notification.

[0058] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0059] The food management system can also include a storage suggestion unit that analyzes the user's purchase history and proposes the optimal storage method. For example, the storage suggestion unit could suggest the best storage method based on how the user has stored food in the past. Furthermore, the storage suggestion unit could prioritize suggesting storage methods for frequently purchased foods based on the user's purchase history. In addition, the storage suggestion unit could analyze the user's purchase history and optimize storage methods. This allows the system to suggest the optimal storage method by referring to the user's purchase history.

[0060] The food management system can also include a consumption plan suggestion unit that analyzes the user's purchase history and proposes an optimal consumption plan. For example, the consumption plan suggestion unit could propose an optimal consumption plan based on the user's past food purchases. Furthermore, the unit could prioritize suggesting consumption plans for frequently purchased foods based on the user's purchase history. In addition, the consumption plan suggestion unit could analyze the user's purchase history and optimize the consumption plan. This allows the system to propose an optimal consumption plan by referencing the user's purchase history.

[0061] The food management system can also include a waste minimization suggestion unit that analyzes the user's purchase history and proposes the optimal waste minimization method. For example, the waste minimization suggestion unit could propose the optimal waste minimization method based on how the user has disposed of food in the past. Furthermore, the waste minimization suggestion unit could prioritize suggesting disposal methods for frequently purchased food items based on the user's purchase history. In addition, the waste minimization suggestion unit could analyze the user's purchase history and optimize disposal methods. This allows the system to propose the optimal waste minimization method by referring to the user's purchase history.

[0062] The food management system may also include a purchase list generation unit that analyzes the user's purchase history and generates an optimal purchase list. For example, the purchase list generation unit generates an optimal purchase list based on a list of foods the user has purchased in the past. Furthermore, the purchase list generation unit can prioritize listing frequently purchased foods based on the user's purchase history. In addition, the purchase list generation unit can analyze the user's purchase history and optimize the purchase list. This allows for the generation of an optimal purchase list by referencing the user's purchase history.

[0063] The food management system can also include a shelf-life notification unit that analyzes the user's purchase history and proposes the optimal shelf-life notification method. For example, the shelf-life notification unit proposes the optimal notification method based on the shelf-life of food items the user has purchased in the past. Furthermore, the shelf-life notification unit can prioritize notifications for frequently purchased food items based on the user's purchase history. In addition, the shelf-life notification unit can analyze the user's purchase history and optimize the shelf-life notification method. This allows it to propose the optimal shelf-life notification method by referring to the user's purchase history.

[0064] The following briefly describes the processing flow for example form 1.

[0065] Step 1: The recognition unit recognizes food inventory. For example, it automatically recognizes food inventory inside the refrigerator using a camera and registers food outside the refrigerator by scanning its barcode. The recognition unit identifies food using image recognition technology and registers it in the database using a barcode reader. Step 2: The suggestion unit proposes recipes based on the information recognized by the recognition unit. For example, it suggests recipes that are appropriate for the user's preferences, health condition, and the season. Based on the user's past selection history, the suggestion unit proposes recipes that the user will like, as well as low-calorie or high-nutrient recipes that take health conditions into consideration, and recipes that use seasonal ingredients. Step 3: The ordering department automatically orders any missing ingredients based on the recipes suggested by the proposal department. For example, it analyzes the user's inventory status and orders the necessary ingredients from local retailers. The ordering department lists the necessary ingredients based on inventory data, places orders, and works with local retailers to deliver the food quickly. Step 4: The Sharing Department shares the ingredients ordered by the Ordering Department within the community. For example, it notifies nearby households and restaurants about food nearing its expiration date and shares the food. The Sharing Department sends notifications to users who have food nearing its expiration date, encouraging them to share the food with nearby households and restaurants, and facilitates food sharing through a digital platform. Step 5: The interlocking unit interacts with AI cooking appliances based on the recipe suggested by the suggestion unit. For example, it interacts with AI cooking appliances such as smart microwave ovens and rice cookers to automate and optimize the cooking process. The interlocking unit automatically configures the smart microwave oven settings based on the recipe and starts cooking, and automatically adjusts the rice cooker settings to perform optimal rice cooking.

[0066] (Example of form 2) The food management system according to an embodiment of the present invention is an innovative system for homes and small restaurants that combines AI technology with the latest IoT devices. This food management system helps homes, restaurants, and retailers efficiently reduce food waste. The food management system allows users to automatically recognize food inventory in their refrigerators using a camera, and easily register food outside the refrigerator by scanning barcodes. This makes it possible to understand food expiration dates and inventory status in real time. Next, the AI ​​suggests recipes tailored to the user's preferences, health condition, and the season, and automatically orders any missing ingredients. This ordering is coordinated with local retailers and other partners to ensure prompt food delivery. Furthermore, a system is introduced to share food nearing its expiration date within the community, reducing food waste by sharing food with nearby homes and restaurants. In addition, by linking with AI cooking appliances (such as smart microwave ovens and rice cookers), the cooking process is automated and optimized, saving time and effort in cooking. This makes it easy for busy homes and restaurants to provide delicious meals. For example, the food management system allows users to automatically recognize food inventory in their refrigerators using a camera, and register food outside the refrigerator by scanning barcodes. This information is analyzed by AI, allowing for real-time tracking of food expiration dates and inventory levels. Next, the AI ​​suggests recipes tailored to the user's preferences, health status, and the season. For example, if the user is health-conscious, it will suggest low-calorie recipes. Next, it automatically orders any missing ingredients. The AI ​​analyzes the user's inventory and orders necessary ingredients from local retailers, saving the user the trouble of going shopping. Next, it introduces a system for sharing food nearing its expiration date within the community. For example, if a user has food nearing its expiration date, it can notify nearby households and restaurants to share the food. Finally, by linking with AI-powered cooking appliances, it automates and optimizes the cooking process. For example, a smart microwave can automatically cook based on a recipe, saving the user the trouble of cooking. In this way, the food management system can streamline food management in homes and small restaurants and reduce food waste.This allows food management systems to streamline food management in homes and small restaurants, and reduce food waste.

[0067] The food management system according to this embodiment comprises a recognition unit, a suggestion unit, an ordering unit, a sharing unit, and an interlocking unit. The recognition unit recognizes food inventory. For example, the recognition unit automatically recognizes food inventory inside a refrigerator using a camera. The recognition unit can also register food outside the refrigerator by barcode scanning. For example, the recognition unit photographs food inside the refrigerator with a camera and identifies the food using image recognition technology. The recognition unit can also scan food outside the refrigerator using a barcode reader and register it in the database. The suggestion unit suggests recipes based on the information recognized by the recognition unit. For example, the suggestion unit suggests recipes that suit the user's preferences, health condition, and the season. For example, the suggestion unit suggests recipes that the user will like based on the user's past selection history. The suggestion unit can also suggest low-calorie or high-nutrient recipes considering the user's health condition. The suggestion unit can also suggest recipes using seasonal ingredients. The ordering unit automatically orders any missing ingredients based on the recipes suggested by the suggestion unit. The ordering unit analyzes the user's inventory status and orders necessary ingredients from local retailers. For example, the ordering unit automatically lists necessary ingredients based on the user's inventory data and places orders. The ordering unit can also collaborate with local retailers to deliver food quickly. The sharing unit shares the ingredients ordered by the ordering unit within the community. For example, the sharing unit can notify nearby households and restaurants of food nearing its expiration date and share the food. For example, the sharing unit sends notifications to users who have food nearing its expiration date, encouraging them to share the food with nearby households and restaurants. The sharing unit can also share food through a digital platform. The integration unit interacts with AI cooking appliances based on recipes suggested by the suggestion unit. For example, the integration unit interacts with AI cooking appliances such as smart microwaves and rice cookers to automate and optimize the cooking process. For example, the integration unit automatically sets up the smart microwave based on the recipe and starts cooking. The integration unit can also automatically adjust the settings of the rice cooker to perform optimal rice cooking.As a result, the food management system according to this embodiment will be able to recognize food inventory, suggest recipes, automatically order ingredients, share within the community, and link with AI cooking appliances.

[0068] The recognition unit recognizes food inventory. For example, the recognition unit automatically recognizes food inventory inside a refrigerator using a camera. Specifically, a high-resolution camera installed inside the refrigerator periodically takes images of the interior, and these images are analyzed using image recognition technology. The image recognition technology uses an object detection algorithm based on deep learning, which can accurately identify the type and quantity of food. For example, it can individually recognize vegetables, fruits, meats, dairy products, etc., inside the refrigerator and record the inventory status of each in a database. The recognition unit can also register food outside the refrigerator by scanning its barcode. By reading the barcode of food purchased by the user with a dedicated scanner, information such as the type of food, expiration date, and purchase date is automatically registered in the database. This makes it possible to centrally manage food inventory both inside and outside the refrigerator. Furthermore, the recognition unit can also track the location information of food in real time using RFID tags. This improves the accuracy of inventory management, such as issuing alerts when food is nearing its expiration date.

[0069] The suggestion unit proposes recipes based on information recognized by the recognition unit. For example, the suggestion unit proposes recipes tailored to the user's preferences, health condition, and the season. Specifically, it utilizes generative AI to generate optimal recipes for each individual user by analyzing the user's past selection history and eating habits. The generative AI considers the user's preferences, allergy information, and nutritional balance to generate the best possible recipe. For example, it learns the user's preferred seasonings and cooking methods based on data from recipes and ingredients the user has previously selected, and proposes new recipes based on that. The suggestion unit can also propose low-calorie or high-nutrient recipes, taking the user's health condition into consideration. For example, if the user is on a diet, it will prioritize suggesting recipes using low-calorie ingredients. The suggestion unit can also propose recipes using seasonal ingredients. For example, it will suggest cold dishes and light-flavored recipes in the summer, and warm dishes and highly nutritious recipes in the winter. In this way, the suggestion unit can provide recipes that meet the diverse needs of users and improve the quality of their diet.

[0070] The ordering department automatically orders any missing ingredients based on the recipes proposed by the suggestion department. For example, the ordering department analyzes the user's inventory status and orders the necessary ingredients from local retailers. Specifically, based on inventory data provided by the recognition department, it generates a list of ingredients required for the suggested recipe and automatically identifies ingredients that are out of stock. The ordering department has a system in place to automatically order these missing ingredients from local retailers and online stores. For example, if an ingredient required for a recipe chosen by the user is missing from the refrigerator, the ordering department automatically lists that ingredient and places an order with a partner retailer. The ordering department can also select the best supplier based on the user's preferences and budget. Furthermore, the ordering department can manage delivery schedules and adjust them so that ingredients arrive at the date and time desired by the user. This allows users to secure the necessary ingredients without hassle, resulting in efficient ingredient management.

[0071] The Sharing Department shares food ordered by the Ordering Department within the community. For example, the Sharing Department can notify nearby households and restaurants of food nearing its expiration date, allowing for food sharing. Specifically, the Sharing Department sends notifications to users who have food nearing its expiration date, encouraging them to share it with nearby households and restaurants. The Sharing Department can facilitate food sharing through a digital platform. This platform provides a system where users can register information about their surplus food, and other users can view this information and request the food they need. For example, if a user registers food nearing its expiration date, a notification is sent to nearby users who can request the food they need. The Sharing Department also coordinates the method and location of food delivery to support smooth sharing. This reduces food waste and promotes the effective use of food resources within the community. Furthermore, the Sharing Department has a system in place to manage food sharing history and evaluate user contributions and trustworthiness. This builds trust among users and enables smoother sharing.

[0072] The interconnected unit interacts with AI cooking appliances based on recipes proposed by the suggestion unit. The interconnected unit interacts with AI cooking appliances such as smart microwave ovens and rice cookers, automating and optimizing the cooking process. Specifically, the interconnected unit transmits the cooking procedure and setting information of the proposed recipe to the AI ​​cooking appliance, automatically starting cooking. For example, the interconnected unit automatically configures the smart microwave oven based on the recipe, starting cooking at the appropriate temperature and time. It can also automatically adjust the settings of a rice cooker for optimal cooking. Furthermore, the interconnected unit monitors the progress of cooking in real time and makes adjustments as needed. For example, it can detect changes in temperature and humidity during cooking and make appropriate adjustments to maintain the quality of the food. This allows users to easily prepare delicious meals and improve the quality of their diet. Additionally, the interconnected unit manages maintenance information and usage history of cooking appliances, prompting maintenance at the appropriate time. This extends the lifespan of cooking appliances and enables efficient operation.

[0073] The recognition unit can automatically recognize food inventory inside the refrigerator using a camera and register food outside the refrigerator by scanning its barcode. For example, the recognition unit can photograph food inside the refrigerator with a camera and identify the food using image recognition technology. The recognition unit can also scan food outside the refrigerator using a barcode reader and register it in a database. For example, the recognition unit can photograph food inside the refrigerator with a high-resolution camera, and AI can analyze the image to identify the food. The recognition unit can also scan food outside the refrigerator using a barcode reader, and AI can analyze the barcode information to identify the food. This allows for efficient recognition and registration of food inventory inside and outside the refrigerator. Some or all of the above-described processes in the recognition unit may be performed using AI or not. For example, the recognition unit can input image data captured by the camera into a generating AI and have the generating AI identify food from the image data.

[0074] The suggestion unit can propose recipes tailored to the user's preferences, health condition, and the season. For example, the suggestion unit can suggest recipes the user will like based on the user's past selection history. It can also suggest low-calorie or high-nutrient recipes considering the user's health condition. Furthermore, it can suggest recipes using seasonal ingredients. This allows the suggestion unit to propose recipes tailored to the user's preferences, health condition, and the season. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input the user's past selection history into a generating AI and have the generating AI suggest recipes the user will like.

[0075] The ordering department can work with local retailers to deliver food quickly. For example, the ordering department can analyze the user's inventory status and order the necessary ingredients from local retailers. The ordering department can also work with local retailers to deliver food quickly. For example, the ordering department can automatically list the necessary ingredients based on the user's inventory data and place an order. The ordering department can also work with local retailers to deliver food quickly. This allows for quick food delivery in cooperation with local retailers. Some or all of the above processes in the ordering department may be performed using AI or not. For example, the ordering department can input the user's inventory data into a generating AI and have the generating AI execute the order for the necessary ingredients.

[0076] The sharing function allows users to share food nearing its expiration date within the community. For example, the sharing function can notify nearby households and restaurants about food nearing its expiration date, thereby sharing the food. The sharing function can also send notifications to users who have food nearing its expiration date, encouraging them to share it with nearby households and restaurants. Furthermore, the sharing function can facilitate food sharing through a digital platform. This allows for the sharing of food nearing its expiration date within the community. Some or all of the above processes in the sharing function may be performed using AI or not. For example, the sharing function can input information about food nearing its expiration date into a generating AI and have the generating AI execute sharing notifications.

[0077] The interlocking unit can work in conjunction with AI cooking appliances such as smart microwave ovens and rice cookers to automate and optimize the cooking process. For example, the interlocking unit can automatically configure the settings of a smart microwave oven based on a recipe and start cooking. It can also automatically adjust the settings of a rice cooker to perform optimal rice cooking. In this way, the cooking process can be automated and optimized in conjunction with AI cooking appliances. Some or all of the above-described processes in the interlocking unit may be performed using AI or not. For example, the interlocking unit can input recipe information into a generating AI and have the generating AI execute the settings for the cooking appliances.

[0078] The recognition unit can estimate the user's emotions and adjust the accuracy of food inventory recognition based on the estimated emotions. For example, if the user is stressed, the recognition unit can use multiple camera angles to improve recognition accuracy. Alternatively, if the user is relaxed, the recognition unit can process with standard recognition accuracy. Furthermore, if the user is in a hurry, the recognition unit can use a simplified recognition method to provide quick recognition results. This allows for adjustment of recognition accuracy based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the recognition unit may be performed using AI or not. For example, the recognition unit can input user emotion data into a generative AI and have the generative AI adjust the recognition accuracy.

[0079] The recognition unit can automatically detect changes in the shape and packaging of food products and improve recognition accuracy. For example, if a new packaging design is introduced, the AI ​​in the recognition unit can automatically learn and improve recognition accuracy. The recognition unit can also detect changes in the shape of food products and adjust the recognition algorithm accordingly. Furthermore, the recognition unit can detect changes in the color and label of the packaging and improve recognition accuracy. In this way, it can detect changes in the shape and packaging of food products and improve recognition accuracy. Some or all of the above-described processes in the recognition unit may be performed using AI or not. For example, the recognition unit can input data on changes in the shape and packaging of food products into a generating AI and have the generating AI perform the improvement of recognition accuracy.

[0080] The recognition unit can correct the recognition result by taking into account the storage conditions of the food (temperature and humidity). For example, if the temperature inside the refrigerator is high, the recognition unit will correct the recognition result by taking into account the deterioration of the food. The recognition unit can also correct the recognition result by taking into account the storage conditions of the food if the humidity is high. Furthermore, in the case of frozen food, the recognition unit can also correct the recognition result by taking into account the thawing state. In this way, the recognition result can be corrected by taking into account the storage conditions of the food. Some or all of the above processing in the recognition unit may be performed using AI or not. For example, the recognition unit can input storage condition data into a generating AI and have the generating AI perform the correction of the recognition result.

[0081] The recognition unit can estimate the user's emotions and adjust the display method of the recognition results based on the estimated emotions. For example, if the user is tense, the recognition unit can provide a simple and highly visible display method. If the user is relaxed, the recognition unit can also provide a display method that includes detailed information. If the user is in a hurry, the recognition unit can also provide a display method that gets straight to the point. This allows the display method of the recognition results to be adjusted based on 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 recognition unit may be performed using AI or not. For example, the recognition unit can input user emotion data into the generative AI and have the generative AI perform the adjustment of the display method.

[0082] The recognition unit can supplement the recognition results by referring to the user's purchase history. For example, the recognition unit can supplement the recognition results based on the food items the user has purchased in the past. The recognition unit can also prioritize the recognition of frequently purchased food items from the user's purchase history. Furthermore, the recognition unit can analyze the user's purchase history and optimize the recognition results. This allows the recognition results to be supplemented by referring to the user's purchase history. Some or all of the above processing in the recognition unit may be performed using AI or not. For example, the recognition unit can input the user's purchase history data into a generating AI and have the generating AI perform the supplementation of the recognition results.

[0083] The recognition unit can automatically acquire nutritional information of food and provide it to the user. For example, the recognition unit can automatically acquire nutritional information of recognized food and provide it to the user. The recognition unit can also automatically acquire calorie information of recognized food and provide it to the user. Furthermore, the recognition unit can automatically acquire allergy information of recognized food and provide it to the user. This enables the automatic acquisition and provision of nutritional information of food to the user. Some or all of the above processing in the recognition unit may be performed using AI or not. For example, the recognition unit can input nutritional information data of food into a generating AI and have the generating AI perform the acquisition of nutritional information.

[0084] The suggestion unit can estimate the user's emotions and adjust the recipe suggestions based on those emotions. For example, if the user is stressed, the suggestion unit can suggest a simple and easy recipe. If the user is relaxed, it can suggest a recipe that can be enjoyed at a leisurely pace. If the user is in a hurry, it can suggest a recipe that can be prepared quickly. In this way, the recipe suggestions can be adjusted based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI adjust the recipe suggestions.

[0085] The suggestion unit can analyze the user's past recipe selection history and propose the most suitable recipe. For example, the suggestion unit can propose the most suitable recipe based on the recipe the user has selected in the past. The suggestion unit can also analyze the user's preferences from their past recipe selection history and make suggestions. Furthermore, the suggestion unit can propose seasonal recipes based on the user's past recipe selection history. In this way, the system can analyze the user's past recipe selection history and propose the most suitable recipe. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input the user's past recipe selection history data into a generating AI and have the generating AI propose the most suitable recipe.

[0086] The suggestion unit can customize recipes by considering the nutritional value and calorie information of the food. For example, the suggestion unit can suggest low-calorie recipes tailored to the user's health condition. It can also suggest high-nutrient recipes considering the user's nutritional balance. Furthermore, it can suggest calorie-restricted recipes tailored to the user's weight loss goals. This allows for the customization of recipes by considering the nutritional value and calorie information of the food. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input nutritional value and calorie information data of the food into a generating AI and have the generating AI perform the recipe customization.

[0087] The suggestion unit can estimate the user's emotions and adjust the recipe display method based on the estimated emotions. For example, if the user is nervous, the suggestion unit can provide a simple and highly visible display method. If the user is relaxed, the suggestion unit can also provide a display method that includes detailed information. If the user is in a hurry, the suggestion unit can also provide a concise display method. This allows the recipe display method to be adjusted based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of the display method.

[0088] The suggestion unit can filter recipes considering the user's allergy information. For example, the suggestion unit can suggest recipes that do not contain allergens based on the user's allergy information. The suggestion unit can also suggest recipes that use alternative ingredients, taking the user's allergy information into consideration. Furthermore, the suggestion unit can prioritize displaying recipes that do not contain allergens based on the user's allergy information. This allows for recipe filtering that takes the user's allergy information into account. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input the user's allergy information data into a generating AI and have the generating AI perform recipe filtering.

[0089] The suggestion unit can propose appropriate recipes according to the user's meal times. For example, at breakfast, the suggestion unit can propose simple and nutritious recipes. It can also propose balanced recipes at lunchtime. Furthermore, it can propose relaxing recipes at dinnertime. This allows the suggestion unit to propose appropriate recipes according to the user's meal times. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input the user's meal time data into a generating AI and have the generating AI propose appropriate recipes.

[0090] The ordering unit can estimate the user's emotions and adjust the timing of orders based on those emotions. For example, if the user is stressed, the ordering unit may expedite the order. If the user is relaxed, the ordering unit can place an order at the normal time. If the user is in a hurry, the ordering unit can place an order quickly. This allows the ordering unit to adjust the timing of orders based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the ordering unit may be performed using AI or not. For example, the ordering unit can input user emotion data into a generative AI and have the generative AI adjust the timing of orders.

[0091] The ordering department can analyze a user's past purchase history and determine the optimal order quantity. For example, the ordering department can determine the optimal order quantity based on the user's past purchase history. The ordering department can also analyze the user's purchasing patterns and adjust the order quantity. Furthermore, the ordering department can predict seasonal order quantities from the user's past purchase history. This allows the ordering department to analyze the user's past purchase history and determine the optimal order quantity. Some or all of the above processes in the ordering department may be performed using AI or not. For example, the ordering department can input the user's past purchase history data into a generating AI and have the generating AI determine the optimal order quantity.

[0092] The ordering department can optimize orders by considering the shelf life of food products. For example, the ordering department can prioritize ordering food products with short shelf lives. It can also order food products with long shelf lives in appropriate quantities. Furthermore, the ordering department can adjust order contents by considering the shelf life. This allows for the optimization of order contents by considering the shelf life of food products. Some or all of the above processes in the ordering department may be performed using AI or not. For example, the ordering department can input food shelf life data into a generating AI and have the generating AI perform the optimization of order contents.

[0093] The ordering unit can estimate the user's emotions and determine ordering priorities based on those emotions. For example, if the user is stressed, the ordering unit will prioritize ordering important food items. If the user is relaxed, the ordering unit can also place orders with normal priorities. Furthermore, if the user is in a hurry, the ordering unit can quickly order the food items they need. This allows for the determination of ordering priorities based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the ordering unit may be performed using AI or not. For example, the ordering unit can input user emotion data into a generative AI and have the generative AI determine the ordering priorities.

[0094] The ordering department can select the optimal delivery method considering the user's geographical location. For example, the ordering department can select the optimal delivery method based on the user's geographical location. The ordering department can also select a fast delivery method considering the user's geographical location. Furthermore, the ordering department can select a cost-effective delivery method based on the user's geographical location. This allows the ordering department to select the optimal delivery method considering the user's geographical location. Some or all of the above processing in the ordering department may be performed using AI or not. For example, the ordering department can input the user's geographical location data into a generating AI and have the generating AI select the optimal delivery method.

[0095] The ordering department can analyze user purchasing patterns and concentrate orders during specific time periods. For example, the ordering department can analyze user purchasing patterns and determine the optimal time for placing orders. It can also select time periods for concentrating orders based on user purchasing patterns. Furthermore, the ordering department can adjust the timing of orders, taking user purchasing patterns into consideration. This allows for the analysis of user purchasing patterns and the concentration of orders during specific time periods. Some or all of the above processes in the ordering department may be performed using AI or not. For example, the ordering department can input user purchasing pattern data into a generating AI and have the generating AI adjust the timing of orders.

[0096] The sharing unit can estimate the user's emotions and adjust the sharing notification method based on the estimated emotions. For example, if the user is stressed, the sharing unit can provide a simple and highly visible notification method. If the user is relaxed, the sharing unit can also provide a notification method that includes detailed information. If the user is in a hurry, the sharing unit can also provide a notification method that gets straight to the point. This allows the sharing notification method to be adjusted based on 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 sharing unit may be performed using AI or not. For example, the sharing unit can input user emotion data into a generative AI and have the generative AI adjust the notification method.

[0097] The sharing unit can analyze a user's past sharing history and suggest the most suitable sharing partner. For example, the sharing unit can suggest the most suitable sharing partner based on the user's past sharing history. The sharing unit can also suggest a trustworthy partner based on the user's sharing history. Furthermore, the sharing unit can analyze the user's sharing history and suggest the most efficient sharing partner. This allows the sharing unit to analyze the user's past sharing history and suggest the most suitable sharing partner. Some or all of the above processing in the sharing unit may be performed using AI or not. For example, the sharing unit can input the user's sharing history data into a generating AI and have the generating AI suggest the most suitable sharing partner.

[0098] The sharing unit can estimate the user's emotions and determine sharing priorities based on those estimated emotions. For example, if the user is stressed, the sharing unit will prioritize sharing important food items. If the user is relaxed, the sharing unit can also share items with normal priorities. Furthermore, if the user is in a hurry, the sharing unit can share items quickly. This allows the sharing priority to be determined based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the sharing unit may be performed using AI or not. For example, the sharing unit can input user emotion data into a generative AI and have the generative AI determine the sharing priority.

[0099] The sharing unit can select the optimal sharing partner by considering the user's geographical location information. For example, the sharing unit can select the optimal sharing partner based on the user's geographical location information. The sharing unit can also select a sharing partner quickly by considering the user's geographical location information. Furthermore, the sharing unit can select a reliable sharing partner based on the user's geographical location information. This allows the optimal sharing partner to be selected by considering the user's geographical location information. Some or all of the above processing in the sharing unit may be performed using AI or not. For example, the sharing unit can input the user's geographical location information data into a generating AI and have the generating AI perform the selection of the optimal sharing partner.

[0100] The interlocking unit can estimate the user's emotions and adjust the operation of the cooking appliances based on the estimated emotions. For example, if the user is stressed, the interlocking unit can simplify the operation of the cooking appliances. If the user is relaxed, the interlocking unit can also provide a detailed cooking process. Furthermore, if the user is in a hurry, the interlocking unit can set the appliances to complete cooking quickly. This allows the operation of the cooking appliances to be adjusted based on 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 interlocking unit may be performed using AI or not. For example, the interlocking unit can input user emotion data into a generative AI and have the generative AI adjust the operation of the cooking appliances.

[0101] The linked unit can analyze the user's past cooking history and suggest the optimal cooking method. For example, the linked unit can suggest the optimal cooking method based on the user's past cooking history. The linked unit can also analyze the user's cooking history to suggest their preferred cooking method. Furthermore, the linked unit can suggest a cooking method appropriate for the season based on the user's past cooking history. In this way, the system can analyze the user's past cooking history and suggest the optimal cooking method. Some or all of the above processing in the linked unit may be performed using AI or not. For example, the linked unit can input the user's past cooking history data into a generating AI and have the generating AI suggest the optimal cooking method.

[0102] The interconnected unit can automatically adjust the settings of cooking appliances according to the type and quantity of food. For example, the interconnected unit can automatically adjust the temperature setting of cooking appliances according to the type of food. It can also automatically adjust the cooking time according to the quantity of food. Furthermore, the interconnected unit can automatically determine the optimal cooking settings considering the type and quantity of food. This allows for the automatic adjustment of cooking appliance settings according to the type and quantity of food. Some or all of the above-described processes in the interconnected unit may be performed using AI or not. For example, the interconnected unit can input data on the type and quantity of food into a generating AI and have the generating AI perform the adjustment of the cooking appliance settings.

[0103] The interlocking unit can estimate the user's emotions and adjust the operating procedures of the cooking appliance based on the estimated emotions. For example, if the user is tense, the interlocking unit can provide simple and easy-to-understand operating procedures. If the user is relaxed, the interlocking unit can also provide detailed operating procedures. If the user is in a hurry, the interlocking unit can also provide concise operating procedures. This allows the operating procedures of the cooking appliance to be adjusted based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the interlocking unit may be performed using AI or not. For example, the interlocking unit can input user emotion data into the generative AI and have the generative AI perform the adjustment of the operating procedures.

[0104] The interlocking unit can propose the optimal operating method considering the user's device information. For example, if the user is using a smartphone, the interlocking unit can provide an operating method adapted to the screen size. It can also provide an operating method optimized for a larger screen if the user is using a tablet. Furthermore, if the user is using a smartwatch, the interlocking unit can provide a concise and highly visible operating method. This allows the system to propose the optimal operating method considering the user's device information. Some or all of the above processing in the interlocking unit may be performed using AI, or without AI. For example, the interlocking unit can input user device information data into a generating AI and have the generating AI propose the optimal operating method.

[0105] The interlocking unit can monitor the operating status of cooking appliances in real time and notify the user if an abnormality is detected. For example, the interlocking unit can monitor the operating status of cooking appliances in real time and notify the user if an abnormality is detected. The interlocking unit can also notify the user in real time if there is an abnormality in the temperature or time setting of the cooking appliance. Furthermore, the interlocking unit can immediately notify the user if the operation of the cooking appliance stops. This allows for real-time monitoring of the operating status of cooking appliances and notification of abnormalities if detected. Some or all of the above processing in the interlocking unit may be performed using AI or not. For example, the interlocking unit can input cooking appliance operation data into a generating AI and have the generating AI perform abnormality detection and notification.

[0106] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0107] The food management system can also include a preservation suggestion unit that estimates the user's emotions and proposes preservation methods based on those emotions. For example, if the user is stressed, the preservation suggestion unit might suggest a simple and easy preservation method. If the user is relaxed, it might suggest a more detailed and effective preservation method. Furthermore, if the user is in a hurry, it might suggest a preservation method that can be implemented quickly. This allows the system to propose the optimal preservation method based on the user's emotions.

[0108] The food management system may also include a shelf-life notification unit that estimates the user's emotions and notifies them of the food's expiration date based on those emotions. For example, if the user is stressed, the shelf-life notification unit may provide a simple and easily visible notification method. If the user is relaxed, it may provide a notification method that includes more detailed information. Furthermore, if the user is in a hurry, it may provide a concise notification method. This allows the expiration date notification method to be adjusted based on the user's emotions.

[0109] The food management system may also include a purchase list generation unit that estimates the user's emotions and generates a purchase list based on those emotions. For example, if the user is stressed, the purchase list generation unit can generate a simple, minimal purchase list. Conversely, if the user is relaxed, it can generate a detailed, comprehensive purchase list. Furthermore, if the user is in a hurry, it can generate a list for quick purchases. This allows for the generation of an optimal purchase list based on the user's emotions.

[0110] The food management system may also include a consumption plan suggestion unit that estimates the user's emotions and proposes a food consumption plan based on those emotions. For example, if the user is stressed, the consumption plan suggestion unit might propose a simple and hassle-free consumption plan. If the user is relaxed, it could also propose a detailed and effective consumption plan. Furthermore, if the user is in a hurry, it could propose a consumption plan that can be implemented quickly. This allows for the proposal of the optimal consumption plan based on the user's emotions.

[0111] The food management system may also include a waste minimization suggestion unit that estimates the user's emotions and proposes ways to minimize food waste based on those emotions. For example, if the user is stressed, the waste minimization suggestion unit might suggest simple, hassle-free methods of minimizing waste. If the user is relaxed, it might suggest more detailed and effective methods. Furthermore, if the user is in a hurry, it might suggest methods that can be implemented quickly. This allows the system to propose the optimal waste minimization method based on the user's emotions.

[0112] The food management system can also include a storage suggestion unit that analyzes the user's purchase history and proposes the optimal storage method. For example, the storage suggestion unit could suggest the best storage method based on how the user has stored food in the past. Furthermore, the storage suggestion unit could prioritize suggesting storage methods for frequently purchased foods based on the user's purchase history. In addition, the storage suggestion unit could analyze the user's purchase history and optimize storage methods. This allows the system to suggest the optimal storage method by referring to the user's purchase history.

[0113] The food management system can also include a consumption plan suggestion unit that analyzes the user's purchase history and proposes an optimal consumption plan. For example, the consumption plan suggestion unit could propose an optimal consumption plan based on the user's past food purchases. Furthermore, the unit could prioritize suggesting consumption plans for frequently purchased foods based on the user's purchase history. In addition, the consumption plan suggestion unit could analyze the user's purchase history and optimize the consumption plan. This allows the system to propose an optimal consumption plan by referencing the user's purchase history.

[0114] The food management system can also include a waste minimization suggestion unit that analyzes the user's purchase history and proposes the optimal waste minimization method. For example, the waste minimization suggestion unit could propose the optimal waste minimization method based on how the user has disposed of food in the past. Furthermore, the waste minimization suggestion unit could prioritize suggesting disposal methods for frequently purchased food items based on the user's purchase history. In addition, the waste minimization suggestion unit could analyze the user's purchase history and optimize disposal methods. This allows the system to propose the optimal waste minimization method by referring to the user's purchase history.

[0115] The food management system may also include a purchase list generation unit that analyzes the user's purchase history and generates an optimal purchase list. For example, the purchase list generation unit generates an optimal purchase list based on a list of foods the user has purchased in the past. Furthermore, the purchase list generation unit can prioritize listing frequently purchased foods based on the user's purchase history. In addition, the purchase list generation unit can analyze the user's purchase history and optimize the purchase list. This allows for the generation of an optimal purchase list by referencing the user's purchase history.

[0116] The food management system can also include a shelf-life notification unit that analyzes the user's purchase history and proposes the optimal shelf-life notification method. For example, the shelf-life notification unit proposes the optimal notification method based on the shelf-life of food items the user has purchased in the past. Furthermore, the shelf-life notification unit can prioritize notifications for frequently purchased food items based on the user's purchase history. In addition, the shelf-life notification unit can analyze the user's purchase history and optimize the shelf-life notification method. This allows it to propose the optimal shelf-life notification method by referring to the user's purchase history.

[0117] The following briefly describes the processing flow for example form 2.

[0118] Step 1: The recognition unit recognizes food inventory. For example, it automatically recognizes food inventory inside the refrigerator using a camera and registers food outside the refrigerator by scanning its barcode. The recognition unit identifies food using image recognition technology and registers it in the database using a barcode reader. Step 2: The suggestion unit proposes recipes based on the information recognized by the recognition unit. For example, it suggests recipes that are appropriate for the user's preferences, health condition, and the season. Based on the user's past selection history, the suggestion unit proposes recipes that the user will like, as well as low-calorie or high-nutrient recipes that take health conditions into consideration, and recipes that use seasonal ingredients. Step 3: The ordering department automatically orders any missing ingredients based on the recipes suggested by the proposal department. For example, it analyzes the user's inventory status and orders the necessary ingredients from local retailers. The ordering department lists the necessary ingredients based on inventory data, places orders, and works with local retailers to deliver the food quickly. Step 4: The Sharing Department shares the ingredients ordered by the Ordering Department within the community. For example, it notifies nearby households and restaurants about food nearing its expiration date and shares the food. The Sharing Department sends notifications to users who have food nearing its expiration date, encouraging them to share the food with nearby households and restaurants, and facilitates food sharing through a digital platform. Step 5: The interlocking unit interacts with AI cooking appliances based on the recipe suggested by the suggestion unit. For example, it interacts with AI cooking appliances such as smart microwave ovens and rice cookers to automate and optimize the cooking process. The interlocking unit automatically configures the smart microwave oven settings based on the recipe and starts cooking, and automatically adjusts the rice cooker settings to perform optimal rice cooking.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] Each of the multiple elements described above, including the recognition unit, suggestion unit, ordering unit, sharing unit, and linking unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the recognition unit recognizes food inventory using the camera 42 and barcode reader of the smart device 14 and processes the data with the control unit 46A. The suggestion unit is implemented in the specific processing unit 290 of the data processing unit 12 and suggests recipes based on the user's preferences and health condition. The ordering unit is implemented in the specific processing unit 290 of the data processing unit 12 and automatically orders necessary ingredients from local retailers. The sharing unit is implemented in the specific processing unit 46A of the smart device 14 and shares food nearing its expiration date within the community. The linking unit is implemented in the specific processing unit 46A of the smart device 14 and automates and optimizes the cooking process in conjunction with AI cooking appliances. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0123] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.).

[0135] 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.

[0136] 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.

[0137] 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.

[0138] Each of the multiple elements described above, including the recognition unit, suggestion unit, ordering unit, sharing unit, and linking unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the recognition unit recognizes food inventory using the camera 42 and barcode reader of the smart glasses 214 and processes the data with the control unit 46A. The suggestion unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and suggests recipes based on the user's preferences and health condition. The ordering unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and automatically orders necessary ingredients from local retailers. The sharing unit is implemented, for example, by the control unit 46A of the smart glasses 214 and shares food nearing its expiration date within the community. The linking unit is implemented, for example, by the control unit 46A of the smart glasses 214 and automates and optimizes the cooking process in conjunction with AI cooking appliances. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be modified in various ways.

[0139] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.).

[0151] 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.

[0152] 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.

[0153] 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.

[0154] Each of the multiple elements described above, including the recognition unit, suggestion unit, ordering unit, sharing unit, and linking unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the recognition unit recognizes food inventory using the camera 42 and barcode reader of the headset terminal 314 and processes the data with the control unit 46A. The suggestion unit is implemented in the identification processing unit 290 of the data processing unit 12 and suggests recipes based on the user's preferences and health condition. The ordering unit is implemented in the identification processing unit 290 of the data processing unit 12 and automatically orders necessary ingredients from local retailers. The sharing unit is implemented in the control unit 46A of the headset terminal 314 and shares food nearing its expiration date within the community. The linking unit is implemented in the control unit 46A of the headset terminal 314 and automates and optimizes the cooking process in conjunction with AI cooking appliances. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0155] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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).

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.).

[0168] 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.

[0169] 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.

[0170] 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.

[0171] Each of the multiple elements described above, including the recognition unit, suggestion unit, ordering unit, sharing unit, and interlocking unit, is implemented in at least one of the following: the robot 414 and the data processing unit 12. For example, the recognition unit recognizes food inventory using the camera 42 and barcode reader of the robot 414 and processes the data with the control unit 46A. The suggestion unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and suggests recipes based on the user's preferences and health condition. The ordering unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and automatically orders necessary ingredients from local retailers. The sharing unit is implemented, for example, by the control unit 46A of the robot 414 and shares food nearing its expiration date within the community. The interlocking unit is implemented, for example, by the control unit 46A of the robot 414 and automates and optimizes the cooking process in conjunction with AI cooking appliances. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be modified in various ways.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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."

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] (Note 1) A recognition unit that recognizes food inventory, A suggestion unit proposes a recipe based on the information recognized by the recognition unit, An ordering unit that automatically orders any missing ingredients based on the recipe proposed by the aforementioned proposal unit, The sharing department shares the ingredients ordered by the aforementioned ordering department within the community, The system includes an interlocking unit that works in conjunction with an AI cooking appliance based on a recipe proposed by the aforementioned proposal unit. A system characterized by the following features. (Note 2) The recognition unit, The system automatically recognizes the food inventory inside the refrigerator using a camera and registers food items outside the refrigerator by scanning their barcodes. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned proposal section is, We suggest recipes tailored to the user's preferences, health condition, and the season. The system described in Appendix 1, characterized by the features described herein. (Note 4) The ordering department said, We partner with local retailers to deliver food quickly. The system described in Appendix 1, characterized by the features described herein. (Note 5) The sharing unit is, Share food items nearing their expiration date within the community. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned interlocking part is, It works in conjunction with AI cooking appliances such as smart microwaves and rice cookers to automate and optimize the cooking process. The system described in Appendix 1, characterized by the features described herein. (Note 7) The recognition unit, The system estimates the user's emotions and adjusts the accuracy of food inventory recognition based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The recognition unit, Automatically detects changes in food shape and packaging to improve recognition accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 9) The recognition unit, The recognition results are corrected to take into account the storage conditions of the food. The system described in Appendix 1, characterized by the features described herein. (Note 10) The recognition unit, It estimates the user's emotions and adjusts how the recognition results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The recognition unit, The recognition results are supplemented by referring to the user's purchase history. The system described in Appendix 1, characterized by the features described herein. (Note 12) The recognition unit, Automatically retrieves nutritional information from food products and provides it to users. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned proposal section is, The system estimates the user's emotions and adjusts the recipe suggestions based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned proposal section is, It analyzes the user's past recipe selection history and suggests the most suitable recipe. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned proposal section is, Customize recipes by taking into account the nutritional value and calorie information of the foods. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned proposal section is, It estimates the user's emotions and adjusts how recipes are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned proposal section is, Filter recipes based on the user's allergy information. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned proposal section is, The system suggests appropriate recipes based on the user's meal times. The system described in Appendix 1, characterized by the features described herein. (Note 19) The ordering department said, It estimates the user's emotions and adjusts the timing of orders based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The ordering department said, Analyze the user's past purchase history to determine the optimal order quantity. The system described in Appendix 1, characterized by the features described herein. (Note 21) The ordering department said, Optimize your order based on the shelf life of the food products. The system described in Appendix 1, characterized by the features described herein. (Note 22) The ordering department said, It estimates user sentiment and determines order priorities based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 23) The ordering department said, The optimal delivery method is selected considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 24) The ordering department said, Analyze user purchasing patterns and concentrate orders during specific time periods. The system described in Appendix 1, characterized by the features described herein. (Note 25) The sharing unit is, It estimates the user's emotions and adjusts the sharing notification method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The sharing unit is, We analyze the user's past sharing history and suggest the most suitable sharing partners. The system described in Appendix 1, characterized by the features described herein. (Note 27) The sharing unit is, It estimates the user's emotions and determines sharing priorities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The sharing unit is, The system selects the most suitable sharing partner by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned interlocking part is, It estimates the user's emotions and adjusts the operation of the cooking appliance based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned interlocking part is, It analyzes the user's past cooking history and suggests the optimal cooking method. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned interlocking part is, The settings of the cooking appliance will automatically adjust according to the type and quantity of food. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned interlocking part is, The system estimates the user's emotions and adjusts the operation procedures of the cooking appliance based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned interlocking part is, We propose the optimal operating method considering the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned interlocking part is, It monitors the operating status of cooking appliances in real time and notifies you if an abnormality is detected. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0191] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A recognition unit that recognizes food inventory, A suggestion unit proposes a recipe based on the information recognized by the recognition unit, An ordering unit that automatically orders any missing ingredients based on the recipe proposed by the aforementioned proposal unit, The sharing department shares the ingredients ordered by the aforementioned ordering department within the community, The system includes an interlocking unit that works in conjunction with an AI cooking appliance based on the recipe proposed by the aforementioned proposal unit. A system characterized by the following features.

2. The recognition unit, The system automatically recognizes the food inventory inside the refrigerator using a camera and registers food items outside the refrigerator by scanning their barcodes. The system according to feature 1.

3. The aforementioned proposal section is, We suggest recipes tailored to the user's preferences, health condition, and the season. The system according to feature 1.

4. The ordering department said, We partner with local retailers to deliver food quickly. The system according to feature 1.

5. The sharing unit is, Share food items nearing their expiration date within the community. The system according to feature 1.

6. The aforementioned interlocking part is, It works in conjunction with AI cooking appliances such as smart microwaves and rice cookers to automate and optimize the cooking process. The system according to feature 1.

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

8. The recognition unit, Automatically detects changes in food shape and packaging to improve recognition accuracy. The system according to feature 1.

9. The recognition unit, The recognition results are corrected to take into account the storage conditions of the food. The system according to feature 1.

10. The recognition unit, It estimates the user's emotions and adjusts how the recognition results are displayed based on the estimated emotions. The system according to feature 1.

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