system

Abstract

The system according to this embodiment aims to manage the best-before and expiration dates of ingredients and to suggest personalized recipes. [Solution] The system according to the embodiment comprises a collection unit, a management unit, a suggestion unit, and a supply unit. The collection unit collects food information by reading the two-dimensional code of the food ingredients. The management unit manages the best-before date and expiration date based on the food information collected by the collection unit. The suggestion unit suggests a personalized recipe based on the food information managed by the management unit. The supply unit provides cooking tips and techniques based on the recipe suggested by the suggestion unit.

Description

Technical Field

[0004] ,

[0006] , , , ,

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

Background Art

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

Prior Art Documents

Patent Documents

[0007] The system according to this embodiment can manage the best-before and expiration dates of ingredients and suggest personalized recipes. [Brief explanation of the drawing]

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

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

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

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

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

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

[0014] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F controls communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 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 GourmetGenie Platform, according to an embodiment of the present invention, is a platform designed to provide a new experience through food by utilizing innovative AI technology. This platform targets a wide range of users, from beginners to professionals, and suggests personalized recipes, cooking tips, and optimal ingredient combinations. It also includes a function that allows for easy management of best-before and expiration dates using the QR codes (e.g., QR codes) of purchased ingredients. For example, by scanning the QR codes of purchased ingredients into the app, the app automatically manages the best-before and expiration dates. For instance, when a user scans the QR codes of vegetables or meat purchased at a supermarket with their smartphone, the information is registered in the app, and a notification is sent when the expiration date approaches. This reduces food waste and allows for efficient use of ingredients. Next, it suggests personalized recipes based on the user's preferences and nutritional needs. For example, if a user is vegetarian, the app suggests meat-free recipes. Also, if a user wants to consume more of a specific nutrient, the app suggests recipes using ingredients containing that nutrient. This allows users to enjoy meals tailored to their preferences and health conditions. Furthermore, the app suggests optimal ingredient combinations based on the ingredients the user possesses. For example, when a user enters the ingredients they have in their refrigerator, the app displays recipes using those ingredients. This allows users to gain new cooking ideas and use up ingredients without waste. The app also provides cooking tips and techniques. For instance, when a user is preparing a specific dish, the app will teach them the appropriate cooking methods and tips. This allows even beginners to create professional-level dishes, increasing the enjoyment of cooking. In this way, the GourmetGenie Platform comprehensively supports the user's cooking experience, from ingredient management to recipe suggestions and cooking assistance. By utilizing AI technology, users can efficiently manage their ingredients and enjoy healthy and delicious meals. In this way, the GourmetGenie Platform can provide more enjoyment and convenience to users' daily lives.

[0029] The GourmetGenie Platform according to this embodiment comprises a collection unit, a management unit, a suggestion unit, and a provision unit. The collection unit collects food information by reading the 2D code of the food. The collection unit scans the 2D code of the food using, for example, a 2D code reader (e.g., a QR code reader) and collects information such as the type of food, place of origin, and nutritional components. The management unit manages best-before and expiration dates based on the food information collected by the collection unit. The management unit stores the collected food information in a database and sends notifications to the user when the best-before or expiration date is approaching. The suggestion unit proposes personalized recipes based on the food information managed by the management unit. The suggestion unit selects appropriate recipes based on the user's preferences and nutritional needs and proposes them to the user. The provision unit provides cooking tips and techniques based on the recipes proposed by the suggestion unit. The provision unit provides cooking support by, for example, providing the user with cooking methods and tips suitable for the recipe. As a result, the GourmetGenie Platform according to this embodiment can comprehensively support the user's cooking experience, from managing ingredients to suggesting recipes and providing cooking support.

[0030] The data collection unit collects food information by reading the 2D code on the food ingredients. For example, the unit uses a 2D code reader (e.g., a QR code reader) to scan the 2D code on the food ingredients and collect information such as the type of ingredient, origin, and nutritional content. Specifically, the 2D code reader is installed in a smartphone or a dedicated scanner, allowing users to instantly obtain information by scanning the 2D code printed on the food ingredient packaging. The collected information is automatically transmitted to and stored in a cloud-based database. This allows users to manage detailed information about food ingredients without any hassle. The data collection unit also has the ability to read barcodes and RFID tags on food ingredients, allowing it to collect food information in various formats. For example, a barcode reader can be used to scan the barcode on food ingredients to obtain information such as product name, manufacturer, and price. Using RFID tags allows for contactless reading of food ingredient information, enabling more efficient data collection. This allows the data collection unit to quickly and accurately collect diverse food ingredient information, supporting users' food ingredient management. Furthermore, the data collection unit can connect with external databases via the internet to obtain the latest food ingredient information. For example, nutritional information and allergen data for food ingredients can be obtained from reliable external databases and provided to users. This allows the data collection unit to always manage food ingredients based on the latest information, contributing to improved health management and dietary habits for users.

[0031] The management department manages best-before and expiration dates based on food information collected by the collection department. For example, the management department stores the collected food information in a database and sends notifications to users when best-before or expiration dates are approaching. Specifically, the management department analyzes the collected food information and automatically calculates the best-before and expiration dates for each food item. This eliminates the need for users to manually enter expiration dates. The management department sets the optimal storage period according to the type of food item and storage method, and notifies users via push notifications or email when the expiration date is approaching. For example, it sets appropriate expiration dates based on the storage conditions for food items that require refrigeration or can be frozen. The management department also has a function to reflect information in the database when a user consumes food items. This allows for constant monitoring of the latest inventory status, preventing unnecessary purchases and food waste. Furthermore, the management department can analyze food consumption history to understand user consumption patterns. This enables the understanding of user preferences and eating habits, allowing for more personalized services. For example, users who frequently use a particular ingredient can be suggested new recipes using that ingredient. This allows the management department to streamline the management of users' food ingredients and improve the quality of their diet.

[0032] The suggestion department proposes personalized recipes based on ingredient information managed by the management department. For example, the suggestion department selects and proposes appropriate recipes to users based on their preferences and nutritional needs. Specifically, the suggestion department selects the optimal recipe by considering the user's past consumption history, preferred ingredients, allergy information, etc. Using AI, it analyzes the user's preferences and nutritional balance to generate recipes that are optimal for each individual user. For example, if a user likes to use a particular ingredient, it will propose a new recipe using that ingredient. Also, if a user needs to consume a particular nutrient, it will prioritize suggesting recipes that contain that nutrient. The suggestion department can also collect user feedback and continuously improve its suggestions. For example, after a user actually cooks a suggested recipe, it collects their evaluation and comments and incorporates them into future suggestions. This allows the suggestion department to provide recipes that are more suitable to the user's preferences and needs. Furthermore, the suggestion department can also propose special recipes according to the season or event. For example, it will propose special recipes tailored to events such as Christmas or Halloween, enriching the user's cooking experience. In this way, the suggestion department can diversify and provide enjoyment to users' eating habits.

[0033] The service provider offers cooking tips and techniques based on recipes proposed by the suggestion team. For example, the service provider provides users with cooking methods and tips suitable for each recipe, supporting their cooking process. Specifically, the service provider provides detailed cooking procedures and precautions for each recipe. For instance, it explains specific cooking tips such as how to prepare ingredients, cooking times, and how to adjust the heat. The service provider can also provide visually easy-to-understand cooking guides using videos and images. This allows users to intuitively understand the cooking process and increases their chances of success. Furthermore, the service provider has a function to provide real-time support for questions and problems that arise during cooking. For example, it can use a chatbot to instantly answer user questions and provide cooking support. This allows users to cook with confidence. The service provider also offers a function to share photos of dishes users have cooked, promoting interaction with other users. This allows users to share the joy of cooking and increase their motivation. The service provider can support users in improving their cooking skills and provide a richer culinary experience.

[0034] The collection unit can collect food ingredient information by reading 2D codes. For example, the collection unit can scan the 2D code of a food ingredient using a 2D code reader and collect information such as the type of food ingredient, its place of origin, and its nutritional content. The collection unit can also read the 2D code using a smartphone camera and register the food ingredient information in an app. The collection unit can also read the 2D code of a food ingredient using a dedicated 2D code scanner and save it to a database. This makes the collection of food ingredient information more efficient by using 2D codes. The 2D code may include, but is not limited to, information such as the type of food ingredient, its place of origin, and its nutritional content. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input the data read from the 2D code into a generating AI and have the generating AI perform the analysis of the food ingredient information.

[0035] The management unit can manage best-before and expiration dates based on collected food information. For example, the management unit can store the collected food information in a database and send notifications to users when best-before or expiration dates are approaching. The management unit can also automatically calculate best-before and expiration dates and notify users. The management unit can also manage best-before and expiration dates based on the type of food and storage method. This allows for efficient management of food best-before and expiration dates. Best-before and expiration dates include, but are not limited to, the period during which flavor and quality are maintained and the period during which food can be safely consumed. Some or all of the above processes in the management unit may be performed using AI, for example, or without AI. For example, the management unit can input collected food information into a generating AI and have the generating AI manage best-before and expiration dates.

[0036] The suggestion unit can suggest personalized recipes based on the user's preferences and nutritional needs. For example, the suggestion unit can select and suggest appropriate recipes based on the user's preferences and nutritional needs. The suggestion unit can also suggest appropriate recipes based on the user's allergy information and dietary restrictions. The suggestion unit can also suggest appropriate recipes based on the user's health condition and nutritional balance. This allows for the suggestion of recipes tailored to the user's preferences and health condition. User preferences and nutritional needs include, but are not limited to, vegetarian, gluten-free, and low-sugar diets. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's preferences and nutritional needs into a generating AI and have the generating AI suggest personalized recipes.

[0037] The service provider can provide cooking tips and techniques based on the proposed recipe. For example, the service provider can provide users with cooking methods and tips suitable for the recipe, thereby supporting them in their cooking. The service provider can also provide users with cooking procedures and points to note. The service provider can also provide cooking tips and techniques in the form of videos or images. This enhances the enjoyment of cooking by providing cooking tips and techniques. Cooking tips and techniques include, but are not limited to, cooking procedures, tips, and points to note. Some or all of the above processing in the service provider may be performed using, for example, AI, or not using AI. For example, the service provider can input the proposed recipe into a generating AI and have the generating AI provide cooking tips and techniques.

[0038] The management department can send notifications to users when the expiration date is approaching. For example, the management department can notify users of information about food items nearing their expiration date to encourage consumption. The management department can also send notifications to users using, for example, smartphone push notifications or email notifications. The management department can also list food items nearing their expiration date and notify users of this. This reduces food waste by notifying users of food items nearing their expiration date. Notifications include, but are not limited to, smartphone push notifications and email notifications. Some or all of the above processes in the management department may be performed using, for example, AI, or not using AI. For example, the management department can input information about food items nearing their expiration date into a generating AI and have the generating AI send notifications.

[0039] The suggestion unit can propose appropriate ingredient combinations based on the ingredients the user possesses. For example, if the user inputs the ingredients they have, the suggestion unit will propose recipes using those ingredients. The suggestion unit can also propose the optimal ingredient combination based on the ingredients in the refrigerator. The suggestion unit can also propose recipes that use up ingredients without waste, taking into account the user's inventory. This allows the user to use up all of their ingredients without waste. Appropriate ingredient combinations include, but are not limited to, nutritional balance and flavor compatibility. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input information about the ingredients the user possesses into a generating AI and have the generating AI propose the optimal ingredient combination.

[0040] The data collection unit can analyze the user's past food purchase history and select the optimal 2D code reading method. For example, the data collection unit may prioritize reading 2D codes of food items that the user has frequently purchased in the past. The data collection unit can also automatically recognize 2D codes of specific food items from the user's purchase history and simplify the reading process. The data collection unit can also analyze the user's purchase patterns and suggest the optimal reading method. This allows for efficient collection of food information by selecting the optimal 2D code reading method based on the user's past purchase history. The optimal 2D code reading method includes, but is not limited to, reading speed and accuracy. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past food purchase history into a generating AI and have the generating AI select the optimal 2D code reading method.

[0041] The collection unit can filter 2D codes based on the user's current food inventory when reading them. For example, the collection unit prioritizes reading 2D codes for necessary ingredients based on the ingredients the user has in their refrigerator. The collection unit can also filter out duplicate 2D codes for ingredients, taking into account the user's inventory status. The collection unit can also suggest the most suitable 2D codes for ingredients based on the user's inventory information. This allows for the avoidance of duplicate ingredient information by filtering based on the user's current food inventory status. Filtering includes, but is not limited to, methods for excluding duplicate ingredients and setting priorities. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input the user's current food inventory status into a generating AI and have the generating AI perform the filtering.

[0042] The data collection unit can prioritize the collection of highly relevant food ingredient information by considering the user's geographical location when reading 2D codes. For example, if the user is in a specific region, the data collection unit will prioritize reading 2D codes for food ingredients available in that region. The data collection unit can also collect local food ingredient information based on the user's location information. For example, if the user is traveling, the data collection unit can prioritize the collection of local food ingredient information. This allows for the efficient collection of highly relevant food ingredient information by considering the user's geographical location information. Geographical location information includes, but is not limited to, GPS data and location services. Some or all of the processing described above in the data collection unit may be performed using, for example, AI, or not using AI. For example, the data collection unit can input the user's geographical location information into a generating AI and have the generating AI collect highly relevant food ingredient information.

[0043] The data collection unit can analyze the user's social media activity and collect relevant food information when reading a 2D code. For example, the data collection unit can prioritize reading relevant 2D codes based on food information shared by the user on social media. The data collection unit can also analyze the content of the user's social media posts and collect 2D codes of foods of interest. For example, the data collection unit can collect relevant 2D codes based on food information shared by the user's followers or friends. This allows for the efficient collection of relevant food information by analyzing the user's social media activity. Social media activity includes, but is not limited to, posts and the number of likes. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity into a generating AI and have the generating AI collect relevant food information.

[0044] The management department can adjust the level of detail in managing best-before and expiration dates based on how the food is stored. For example, the management department can provide detailed storage instructions and frequent notifications for food that requires refrigeration. For example, the management department can provide simplified storage instructions and reduce notification frequency for food that can be stored at room temperature. For example, the management department can provide long-term storage instructions and timely notifications for food that can be frozen. This allows for proper management by adjusting the level of detail based on how the food is stored. Storage methods include, but are not limited to, refrigeration, freezing, and room temperature storage. Some or all of the above processes in the management department may be performed using, for example, AI, or not using AI. For example, the management department can input the food storage methods into a generating AI and have the generating AI adjust the level of detail in management.

[0045] The management department can apply different management algorithms depending on the food category when managing best-before and expiration dates. For example, the management department can apply a management algorithm that prioritizes freshness to vegetables and fruits. For example, the management department can also apply a management algorithm that prioritizes hygiene to meat and fish. For example, the management department can also apply a management algorithm that prioritizes shelf life to processed foods. This allows for appropriate management by applying different management algorithms depending on the food category. Management algorithms include, but are not limited to, machine learning algorithms and rule-based algorithms. Some or all of the above processes in the management department may be performed using, for example, AI, or not using AI. For example, the management department can input food categories into a generating AI and have the generating AI execute the application of management algorithms.

[0046] The management department can determine management priorities based on the purchase date of ingredients when managing best-before and expiration dates. For example, the management department can prioritize the management and notification of ingredients that have been purchased some time ago. For example, the management department can also set a lower management priority for ingredients that have just been purchased. For example, the management department can provide and notify appropriate management methods based on the purchase date. This enables proper management by determining management priorities based on the purchase date of ingredients. Purchase date includes, but is not limited to, purchase history data and receipt information. Some or all of the above processes in the management department may be performed using, for example, AI, or not using AI. For example, the management department can input the purchase date of ingredients into a generating AI and have the generating AI perform the determination of management priorities.

[0047] The management department can improve the accuracy of its management of best-before and expiration dates by referring to related recipe information for ingredients. For example, the management department can suggest recipes using ingredients nearing their expiration date to encourage consumption. The management department can also provide appropriate management methods based on related recipe information for ingredients. The management department can also optimize the expiration dates of ingredients by referring to recipe information. This improves the accuracy of management by referring to related recipe information for ingredients. Related recipe information includes, but is not limited to, a recipe database and a user's past recipe usage history. Some or all of the above processes in the management department may be performed using, for example, AI, or not using AI. For example, the management department can input related recipe information for ingredients into a generating AI and have the generating AI perform the improvement of management accuracy.

[0048] The suggestion unit can adjust the level of detail of its recipe suggestions based on the user's health condition. For example, if the user is in good health, the suggestion unit will suggest a nutritionally balanced recipe. If the user is unwell, the suggestion unit may also suggest an easily digestible recipe. If the user needs a specific nutrient, the suggestion unit may also suggest a recipe containing that nutrient. By adjusting the level of detail of the suggestions based on the user's health condition, the system can suggest more appropriate recipes. Health condition includes, but is not limited to, health checkup data and self-reported data. Some or all of the above processing in the suggestion unit may be performed using, for example, AI, or not using AI. For example, the suggestion unit can input the user's health condition into a generating AI and have the generating AI adjust the level of detail of the suggestions.

[0049] The suggestion unit can apply different suggestion algorithms depending on the user's ingredient inventory when suggesting recipes. For example, the suggestion unit can suggest the optimal recipe based on the ingredients the user has in their refrigerator. For example, the suggestion unit can also suggest recipes that use up ingredients without waste, taking into account the user's inventory. For example, the suggestion unit can suggest recipes that do not require the purchase of new ingredients, based on the user's inventory information. In this way, by applying different suggestion algorithms depending on the user's ingredient inventory, ingredients can be used up without waste. Suggestion algorithms include, but are not limited to, machine learning algorithms and rule-based algorithms. Some or all of the above processing in the suggestion unit may be performed using, for example, AI, or without AI. For example, the suggestion unit can input the user's ingredient inventory into a generating AI and have the generating AI execute the application of the suggestion algorithm.

[0050] The suggestion unit can determine the priority of recipe suggestions based on the user's ingredient purchase history. For example, the suggestion unit can prioritize suggesting recipes related to ingredients the user has purchased in the past. The suggestion unit can also prioritize suggesting recipes that include frequently used ingredients based on the user's purchase history. The suggestion unit can also analyze the user's purchase patterns and suggest the most suitable recipe. This allows for the suggestion of more appropriate recipes by determining the priority of suggestions based on the user's ingredient purchase history. Ingredient purchase history includes, but is not limited to, purchase history data and receipt information. Some or all of the above processing in the suggestion unit may be performed using, for example, AI, or not using AI. For example, the suggestion unit can input the user's ingredient purchase history into a generating AI and have the generating AI determine the priority of suggestions.

[0051] The suggestion unit can analyze the user's food consumption trends and suggest relevant recipes when proposing recipes. For example, the suggestion unit can suggest relevant recipes based on the ingredients the user frequently consumes. The suggestion unit can also analyze the user's consumption trends and suggest recipes that use ingredients without waste. The suggestion unit can also suggest new cooking ideas based on the user's consumption patterns. This allows for the use of ingredients without waste by analyzing the user's food consumption trends. Food consumption trends include, but are not limited to, consumption frequency and consumption amount. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's food consumption trends into a generating AI and have the generating AI suggest relevant recipes.

[0052] The service provider can adjust the level of detail provided based on the user's cooking skill when offering cooking tips and techniques. For example, it can provide basic cooking methods to beginner users, advanced cooking techniques to intermediate users, and professional cooking techniques to advanced users. This allows for more appropriate support by adjusting the level of detail based on the user's cooking skill. Cooking skills include, but are not limited to, self-reported data and past cooking history. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's cooking skills into a generating AI and have the generating AI adjust the level of detail of the information provided.

[0053] The service provider can apply different service algorithms depending on the user's cooking history when providing cooking tips and techniques. For example, the service provider can provide relevant cooking tips based on dishes the user has made in the past. The service provider can also analyze the user's cooking history and provide optimal cooking techniques. The service provider can also suggest new cooking methods based on the user's cooking patterns. By applying different service algorithms depending on the user's cooking history, more appropriate support can be provided. Service algorithms include, but are not limited to, machine learning algorithms and rule-based algorithms. Some or all of the above processing in the service provider may be performed using, for example, AI, or not using AI. For example, the service provider can input the user's cooking history into a generating AI and have the generating AI apply the service algorithm.

[0054] The service provider can select the optimal method of providing cooking tips and techniques by considering the user's cooking equipment information. For example, the service provider can suggest the optimal cooking method based on the cooking equipment the user owns. The service provider can also provide appropriate cooking techniques based on the user's cooking equipment information. For example, if the user uses a specific cooking utensil, the service provider can provide tips suitable for that utensil. This allows for more appropriate support by considering the user's cooking equipment information. Cooking equipment information includes, but is not limited to, the types of cooking utensils owned and how often they are used. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's cooking equipment information into a generating AI and have the generating AI select the optimal method of providing the tips.

[0055] The service provider can improve the accuracy of its recommendations by referring to the user's ingredient inventory when providing cooking tips and techniques. For example, the service provider can provide optimal cooking tips based on the ingredients the user has in their refrigerator. For example, the service provider can also provide cooking techniques that utilize ingredients efficiently, taking into account the user's inventory. For example, the service provider can also provide new recipe ideas based on the user's inventory information. This allows for more appropriate support by referring to the user's ingredient inventory. Ingredient inventory includes, but is not limited to, inventory management data and purchase history data. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's ingredient inventory into a generating AI and have the generating AI perform improvements to the accuracy of its recommendations.

[0056] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0057] The GourmetGenie Platform can monitor the storage environment of a user's food items and suggest optimal storage methods. For example, it can use sensors to detect temperature and humidity inside the refrigerator and provide advice on maintaining the appropriate storage temperature. It can also send notifications to the user if the storage environment is not suitable, prompting them to improve their storage methods. Furthermore, it can recalculate the expiration date of food items based on the storage environment, enabling more accurate expiration date management. This helps maintain the quality of food items and reduces waste.

[0058] The GourmetGenie Platform can analyze a user's grocery purchase history and predict when their next purchase will be. For example, it can learn the user's consumption patterns for frequently purchased ingredients and notify them of the next purchase timing. It can also send reminders in advance if a particular ingredient is running low, preventing users from forgetting to buy it. Furthermore, based on the purchase history, it can suggest new ingredients and recipes that suit the user's preferences. This allows users to manage their groceries efficiently and shop in a planned manner.

[0059] The GourmetGenie Platform can analyze users' food consumption patterns and provide advice to reduce food waste. For example, it can identify ingredients that users frequently throw away and suggest recipes to use them up. It can also advise on appropriate purchase quantities based on the rate of food consumption. Furthermore, it can provide tips on how to store and cook ingredients to prevent spoilage. As a result, users can use their ingredients without waste and live an economical and environmentally friendly life.

[0060] The GourmetGenie Platform can analyze the nutritional value of a user's food choices and suggest balanced meals. For example, it can check the balance of nutrients a user is consuming and suggest recipes to supplement any deficiencies. It can also create and provide meal plans tailored to specific health goals. Furthermore, it can advise on how to choose and cook healthy ingredients based on their nutritional value. This allows users to maintain a healthy diet and enjoy nutritionally balanced meals.

[0061] The GourmetGenie Platform can manage users' food allergy information and suggest safe recipes. For example, users can register the foods they are allergic to, and the platform will suggest recipes that do not contain those foods. It can also provide advice on points to be aware of when purchasing ingredients based on allergy information. Furthermore, it can suggest recipes to provide safe meals for family and friends with allergies. This allows users to enjoy meals with peace of mind and reduces the risks associated with allergies.

[0062] The following briefly describes the processing flow for example form 1.

[0063] Step 1: The collection unit collects food information by reading the 2D code on the food. For example, it uses a 2D code reader to scan the 2D code on the food and collects information such as the type of food, place of origin, and nutritional content. Step 2: The management department manages best-before and expiration dates based on the food information collected by the collection department. For example, it stores the collected food information in a database and sends notifications to users when the best-before or expiration date is approaching. Step 3: The proposal department proposes personalized recipes based on ingredient information managed by the management department. For example, it selects and proposes appropriate recipes to users based on their preferences and nutritional needs. Step 4: The provisioning department provides cooking tips and techniques based on the recipe proposed by the suggestion department. For example, it provides users with cooking methods and tips suitable for the recipe, and supports them in cooking.

[0064] (Example of form 2) The GourmetGenie Platform, according to an embodiment of the present invention, is a platform designed to provide a new experience through food by utilizing innovative AI technology. This platform targets a wide range of users, from beginners to professionals, and suggests personalized recipes, cooking tips, and optimal ingredient combinations. It also includes a function that makes it easy to manage expiration dates and best-before dates using the QR codes (e.g., QR codes) of purchased ingredients. For example, by scanning the QR codes of purchased ingredients into the app, the app automatically manages the expiration dates. For instance, if a user scans the QR codes of vegetables or meat purchased at a supermarket with their smartphone, the information is registered in the app, and a notification is sent when the expiration date approaches. This reduces food waste and allows for efficient use of ingredients. Next, it suggests personalized recipes based on the user's preferences and nutritional needs. For example, if a user is vegetarian, the app suggests meat-free recipes. Also, if a user wants to consume more of a specific nutrient, the app suggests recipes using ingredients containing that nutrient. This allows users to enjoy meals tailored to their preferences and health conditions. Furthermore, the app suggests optimal ingredient combinations based on the ingredients the user possesses. For example, when a user enters the ingredients they have in their refrigerator, the app displays recipes using those ingredients. This allows users to gain new cooking ideas and use up ingredients without waste. The app also provides cooking tips and techniques. For instance, when a user is preparing a specific dish, the app will teach them the appropriate cooking methods and tips. This allows even beginners to create professional-level dishes, increasing the enjoyment of cooking. In this way, the GourmetGenie Platform comprehensively supports the user's cooking experience, from ingredient management to recipe suggestions and cooking assistance. By utilizing AI technology, users can efficiently manage their ingredients and enjoy healthy and delicious meals. In this way, the GourmetGenie Platform can provide more enjoyment and convenience to users' daily lives.

[0065] The GourmetGenie Platform according to this embodiment comprises a collection unit, a management unit, a suggestion unit, and a provision unit. The collection unit collects food information by reading the two-dimensional code (e.g., QR code) of the food. The collection unit scans the two-dimensional code of the food using a two-dimensional code reader (e.g., a QR code reader) and collects information such as the type of food, place of origin, and nutritional components. The management unit manages best-before dates and expiration dates based on the food information collected by the collection unit. The management unit stores the collected food information in a database and sends notifications to the user when the best-before or expiration date is approaching. The suggestion unit proposes personalized recipes based on the food information managed by the management unit. The suggestion unit selects appropriate recipes based on the user's preferences and nutritional needs and proposes them to the user. The provision unit provides cooking tips and techniques based on the recipes proposed by the suggestion unit. The provision unit provides cooking support by providing the user with cooking methods and tips suitable for the recipes. As a result, the GourmetGenie Platform according to this embodiment can comprehensively support the user's cooking experience, from managing ingredients to suggesting recipes and providing cooking support.

[0066] The data collection unit collects food information by reading the 2D codes on the food items. For example, the unit uses a 2D code reader to scan the 2D codes on food items and collect information such as the type of food, origin, and nutritional content. Specifically, the 2D code reader is installed in a smartphone or a dedicated scanner, allowing users to instantly obtain information by scanning the 2D code printed on the food item's packaging. The collected information is automatically transmitted to and stored in a cloud-based database. This allows users to manage detailed information about food items without any hassle. The data collection unit also has the ability to read barcodes and RFID tags on food items, allowing it to collect food information in various formats. For example, it can scan the barcode on a food item using a barcode reader to obtain information such as the product name, manufacturer, and price. When using RFID tags, food information can be read contactlessly, enabling more efficient data collection. As a result, the data collection unit can quickly and accurately collect diverse food information and support users' food management. Furthermore, the data collection unit can also connect with external databases via the internet to obtain the latest food information. For example, nutritional information and allergen data for food ingredients can be obtained from reliable external databases and provided to users. This allows the data collection unit to always manage food ingredients based on the latest information, contributing to improved health management and dietary habits for users.

[0067] The management department manages best-before and expiration dates based on food information collected by the collection department. For example, the management department stores the collected food information in a database and sends notifications to users when best-before or expiration dates are approaching. Specifically, the management department analyzes the collected food information and automatically calculates the best-before and expiration dates for each food item. This eliminates the need for users to manually enter expiration dates. The management department sets the optimal storage period according to the type of food item and storage method, and notifies users via push notifications or email when the expiration date is approaching. For example, it sets appropriate expiration dates based on the storage conditions for food items that require refrigeration or can be frozen. The management department also has a function to reflect information in the database when a user consumes food items. This allows for constant monitoring of the latest inventory status, preventing unnecessary purchases and food waste. Furthermore, the management department can analyze food consumption history to understand user consumption patterns. This enables the understanding of user preferences and eating habits, allowing for more personalized services. For example, users who frequently use a particular ingredient can be suggested new recipes using that ingredient. This allows the management department to streamline the management of users' food ingredients and improve the quality of their diet.

[0068] The suggestion department proposes personalized recipes based on ingredient information managed by the management department. For example, the suggestion department selects and proposes appropriate recipes to users based on their preferences and nutritional needs. Specifically, the suggestion department selects the optimal recipe by considering the user's past consumption history, preferred ingredients, allergy information, etc. Using AI, it analyzes the user's preferences and nutritional balance to generate recipes that are optimal for each individual user. For example, if a user likes to use a particular ingredient, it will propose a new recipe using that ingredient. Also, if a user needs to consume a particular nutrient, it will prioritize suggesting recipes that contain that nutrient. The suggestion department can also collect user feedback and continuously improve its suggestions. For example, after a user actually cooks a suggested recipe, it collects their evaluation and comments and incorporates them into future suggestions. This allows the suggestion department to provide recipes that are more suitable to the user's preferences and needs. Furthermore, the suggestion department can also propose special recipes according to the season or event. For example, it will propose special recipes tailored to events such as Christmas or Halloween, enriching the user's cooking experience. In this way, the suggestion department can diversify and provide enjoyment to users' eating habits.

[0069] The service provider offers cooking tips and techniques based on recipes proposed by the suggestion team. For example, the service provider provides users with cooking methods and tips suitable for each recipe, supporting their cooking process. Specifically, the service provider provides detailed cooking procedures and precautions for each recipe. For instance, it explains specific cooking tips such as how to prepare ingredients, cooking times, and how to adjust the heat. The service provider can also provide visually easy-to-understand cooking guides using videos and images. This allows users to intuitively understand the cooking process and increases their chances of success. Furthermore, the service provider has a function to provide real-time support for questions and problems that arise during cooking. For example, it can use a chatbot to instantly answer user questions and provide cooking support. This allows users to cook with confidence. The service provider also offers a function to share photos of dishes users have cooked, promoting interaction with other users. This allows users to share the joy of cooking and increase their motivation. The service provider can support users in improving their cooking skills and provide a richer culinary experience.

[0070] The collection unit can collect food ingredient information by reading 2D codes. For example, the collection unit can scan the 2D code of a food ingredient using a 2D code reader and collect information such as the type of food ingredient, its place of origin, and its nutritional content. The collection unit can also read the 2D code using a smartphone camera and register the food ingredient information in an app. The collection unit can also read the 2D code of a food ingredient using a dedicated 2D code scanner and save it to a database. This makes the collection of food ingredient information more efficient by using 2D codes. The 2D code may include, but is not limited to, information such as the type of food ingredient, its place of origin, and its nutritional content. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input the data read from the 2D code into a generating AI and have the generating AI perform the analysis of the food ingredient information.

[0071] The management unit can manage best-before and expiration dates based on collected food information. For example, the management unit can store the collected food information in a database and send notifications to users when best-before or expiration dates are approaching. The management unit can also automatically calculate best-before and expiration dates and notify users. The management unit can also manage best-before and expiration dates based on the type of food and storage method. This allows for efficient management of food best-before and expiration dates. Best-before and expiration dates include, but are not limited to, the period during which flavor and quality are maintained and the period during which food can be safely consumed. Some or all of the above processes in the management unit may be performed using AI, for example, or without AI. For example, the management unit can input collected food information into a generating AI and have the generating AI manage best-before and expiration dates.

[0072] The suggestion unit can suggest personalized recipes based on the user's preferences and nutritional needs. For example, the suggestion unit can select and suggest appropriate recipes based on the user's preferences and nutritional needs. The suggestion unit can also suggest appropriate recipes based on the user's allergy information and dietary restrictions. The suggestion unit can also suggest appropriate recipes based on the user's health condition and nutritional balance. This allows for the suggestion of recipes tailored to the user's preferences and health condition. User preferences and nutritional needs include, but are not limited to, vegetarian, gluten-free, and low-sugar diets. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's preferences and nutritional needs into a generating AI and have the generating AI suggest personalized recipes.

[0073] The service provider can provide cooking tips and techniques based on the proposed recipe. For example, the service provider can provide users with cooking methods and tips suitable for the recipe, thereby supporting them in their cooking. The service provider can also provide users with cooking procedures and points to note. The service provider can also provide cooking tips and techniques in the form of videos or images. This enhances the enjoyment of cooking by providing cooking tips and techniques. Cooking tips and techniques include, but are not limited to, cooking procedures, tips, and points to note. Some or all of the above processing in the service provider may be performed using, for example, AI, or not using AI. For example, the service provider can input the proposed recipe into a generating AI and have the generating AI provide cooking tips and techniques.

[0074] The management department can send notifications to users when the expiration date is approaching. For example, the management department can notify users of information about food items nearing their expiration date to encourage consumption. The management department can also send notifications to users using, for example, smartphone push notifications or email notifications. The management department can also list food items nearing their expiration date and notify users of this. This reduces food waste by notifying users of food items nearing their expiration date. Notifications include, but are not limited to, smartphone push notifications and email notifications. Some or all of the above processes in the management department may be performed using, for example, AI, or not using AI. For example, the management department can input information about food items nearing their expiration date into a generating AI and have the generating AI send notifications.

[0075] The suggestion unit can propose appropriate ingredient combinations based on the ingredients the user possesses. For example, if the user inputs the ingredients they have, the suggestion unit will propose recipes using those ingredients. The suggestion unit can also propose the optimal ingredient combination based on the ingredients in the refrigerator. The suggestion unit can also propose recipes that use up ingredients without waste, taking into account the user's inventory. This allows the user to use up all of their ingredients without waste. Appropriate ingredient combinations include, but are not limited to, nutritional balance and flavor compatibility. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input information about the ingredients the user possesses into a generating AI and have the generating AI propose the optimal ingredient combination.

[0076] The data collection unit can estimate the user's emotions and adjust the timing of reading the 2D code based on the estimated emotions. For example, if the user is stressed, the data collection unit can simplify and streamline the reading of the 2D code. For example, if the user is relaxed, the data collection unit can display detailed information and adjust the reading timing. For example, if the user is in a hurry, the data collection unit can quickly read the 2D code and immediately obtain the necessary information. This allows for the collection of ingredient information at a more appropriate time by adjusting the timing of 2D code reading according to the user's emotions. The user's emotions are estimated by methods such as facial recognition and voice analysis, but are not limited to these examples. Some or all of the above processing in the data collection unit is implemented using emotion estimation functions, such as using an emotion engine or generative AI. Generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0077] The data collection unit can analyze the user's past food purchase history and select the optimal 2D code reading method. For example, the data collection unit may prioritize reading 2D codes of food items that the user has frequently purchased in the past. The data collection unit can also automatically recognize 2D codes of specific food items from the user's purchase history and simplify the reading process. The data collection unit can also analyze the user's purchase patterns and suggest the optimal reading method. This allows for efficient collection of food information by selecting the optimal 2D code reading method based on the user's past purchase history. The optimal 2D code reading method includes, but is not limited to, reading speed and accuracy. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past food purchase history into a generating AI and have the generating AI select the optimal 2D code reading method.

[0078] The collection unit can filter 2D codes based on the user's current food inventory when reading them. For example, the collection unit prioritizes reading 2D codes for necessary ingredients based on the ingredients the user has in their refrigerator. The collection unit can also filter out duplicate 2D codes for ingredients, taking into account the user's inventory status. The collection unit can also suggest the most suitable 2D codes for ingredients based on the user's inventory information. This allows for the avoidance of duplicate ingredient information by filtering based on the user's current food inventory status. Filtering includes, but is not limited to, methods for excluding duplicate ingredients and setting priorities. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input the user's current food inventory status into a generating AI and have the generating AI perform the filtering.

[0079] The data collection unit can estimate the user's emotions and determine the priority of QR codes to read based on the estimated emotions. For example, if the user is stressed, the data collection unit will prioritize reading QR codes for important ingredients. If the user is relaxed, the data collection unit may also prioritize reading QR codes containing detailed information. If the user is in a hurry, the data collection unit may also quickly read only the essential QR codes. This allows for the priority collection of important ingredient information by prioritizing QR codes according to the user's emotions. The priority of QR codes may include, but are not limited to, importance and frequency of use. Some or all of the above processing in the data collection unit is implemented using emotion estimation functions, for example, with an emotion engine or generative AI. Generative AI may include, but are not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0080] The data collection unit can prioritize the collection of highly relevant food ingredient information by considering the user's geographical location when reading 2D codes. For example, if the user is in a specific region, the data collection unit will prioritize reading 2D codes for food ingredients available in that region. The data collection unit can also collect local food ingredient information based on the user's location information. For example, if the user is traveling, the data collection unit can prioritize the collection of local food ingredient information. This allows for the efficient collection of highly relevant food ingredient information by considering the user's geographical location information. Geographical location information includes, but is not limited to, GPS data and location services. Some or all of the processing described above in the data collection unit may be performed using, for example, AI, or not using AI. For example, the data collection unit can input the user's geographical location information into a generating AI and have the generating AI collect highly relevant food ingredient information.

[0081] The data collection unit can analyze the user's social media activity and collect relevant food information when reading a 2D code. For example, the data collection unit can prioritize reading relevant 2D codes based on food information shared by the user on social media. The data collection unit can also analyze the content of the user's social media posts and collect 2D codes of foods of interest. For example, the data collection unit can collect relevant 2D codes based on food information shared by the user's followers or friends. This allows for the efficient collection of relevant food information by analyzing the user's social media activity. Social media activity includes, but is not limited to, posts and the number of likes. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity into a generating AI and have the generating AI collect relevant food information.

[0082] The management unit can estimate the user's emotions and adjust the management methods for best-before and expiration dates based on the estimated emotions. For example, if the user is stressed, the management unit can provide simpler management methods and reduce the frequency of notifications. For example, if the user is relaxed, the management unit can also provide more detailed management methods and increase the frequency of notifications. For example, if the user is in a hurry, the management unit can prioritize the management of important ingredients and provide quick notifications. This allows for more appropriate management by adjusting management methods according to the user's emotions. Examples of best-before and expiration date management methods include, but are not limited to, notification frequency and how the management screen is displayed. Some or all of the above processing in the management unit is implemented using emotion estimation functions, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0083] The management department can adjust the level of detail in managing best-before and expiration dates based on how the food is stored. For example, the management department can provide detailed storage instructions and frequent notifications for food that requires refrigeration. For example, the management department can provide simplified storage instructions and reduce notification frequency for food that can be stored at room temperature. For example, the management department can provide long-term storage instructions and timely notifications for food that can be frozen. This allows for proper management by adjusting the level of detail based on how the food is stored. Storage methods include, but are not limited to, refrigeration, freezing, and room temperature storage. Some or all of the above processes in the management department may be performed using, for example, AI, or not using AI. For example, the management department can input the food storage methods into a generating AI and have the generating AI adjust the level of detail in management.

[0084] The management department can apply different management algorithms depending on the food category when managing best-before and expiration dates. For example, the management department can apply a management algorithm that prioritizes freshness to vegetables and fruits. For example, the management department can also apply a management algorithm that prioritizes hygiene to meat and fish. For example, the management department can also apply a management algorithm that prioritizes shelf life to processed foods. This allows for appropriate management by applying different management algorithms depending on the food category. Management algorithms include, but are not limited to, machine learning algorithms and rule-based algorithms. Some or all of the above processes in the management department may be performed using, for example, AI, or not using AI. For example, the management department can input food categories into a generating AI and have the generating AI execute the application of management algorithms.

[0085] The management unit can estimate the user's emotions and adjust the timing of expiration date and best-before date notifications based on the estimated emotions. For example, if the user is stressed, the management unit can reduce the notification frequency and only notify about important ingredients. For example, if the user is relaxed, the management unit can provide detailed and frequent notifications. For example, if the user is in a hurry, the management unit can provide quick notifications and prioritize the management of important ingredients. This allows for notifications to be sent at a more appropriate time by adjusting the notification timing according to the user's emotions. Notification timing includes, but is not limited to, the number of days before the expiration date to notify and the frequency of notifications. Some or all of the above processing in the management unit is implemented using emotion estimation functions, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0086] The management department can determine management priorities based on the purchase date of ingredients when managing best-before and expiration dates. For example, the management department can prioritize the management and notification of ingredients that have been purchased some time ago. For example, the management department can also set a lower management priority for ingredients that have just been purchased. For example, the management department can provide and notify appropriate management methods based on the purchase date. This enables proper management by determining management priorities based on the purchase date of ingredients. Purchase date includes, but is not limited to, purchase history data and receipt information. Some or all of the above processes in the management department may be performed using, for example, AI, or not using AI. For example, the management department can input the purchase date of ingredients into a generating AI and have the generating AI perform the determination of management priorities.

[0087] The management department can improve the accuracy of its management of best-before and expiration dates by referring to related recipe information for ingredients. For example, the management department can suggest recipes using ingredients nearing their expiration date to encourage consumption. The management department can also provide appropriate management methods based on related recipe information for ingredients. The management department can also optimize the expiration dates of ingredients by referring to recipe information. This improves the accuracy of management by referring to related recipe information for ingredients. Related recipe information includes, but is not limited to, a recipe database and a user's past recipe usage history. Some or all of the above processes in the management department may be performed using, for example, AI, or not using AI. For example, the management department can input related recipe information for ingredients into a generating AI and have the generating AI perform the improvement of management accuracy.

[0088] The suggestion unit can estimate the user's emotions and adjust the recipe suggestion method based on the estimated emotions. For example, if the user is stressed, the suggestion unit may suggest a simple and easy recipe. If the user is relaxed, the suggestion unit may suggest a recipe that can be enjoyed over time. If the user is in a hurry, the suggestion unit may suggest a recipe that can be prepared in a short time. By adjusting the recipe suggestion method according to the user's emotions, more appropriate recipes can be suggested. The recipe suggestion method includes, but is not limited to, the frequency of suggestions and the way suggestions are displayed. Some or all of the above processing in the suggestion unit is implemented using emotion estimation functions, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0089] The suggestion unit can adjust the level of detail of its recipe suggestions based on the user's health condition. For example, if the user is in good health, the suggestion unit will suggest a nutritionally balanced recipe. If the user is unwell, the suggestion unit may also suggest an easily digestible recipe. If the user needs a specific nutrient, the suggestion unit may also suggest a recipe containing that nutrient. By adjusting the level of detail of the suggestions based on the user's health condition, the system can suggest more appropriate recipes. Health condition includes, but is not limited to, health checkup data and self-reported data. Some or all of the above processing in the suggestion unit may be performed using, for example, AI, or not using AI. For example, the suggestion unit can input the user's health condition into a generating AI and have the generating AI adjust the level of detail of the suggestions.

[0090] The suggestion unit can apply different suggestion algorithms depending on the user's ingredient inventory when suggesting recipes. For example, the suggestion unit can suggest the optimal recipe based on the ingredients the user has in their refrigerator. For example, the suggestion unit can also suggest recipes that use up ingredients without waste, taking into account the user's inventory. For example, the suggestion unit can suggest recipes that do not require the purchase of new ingredients, based on the user's inventory information. In this way, by applying different suggestion algorithms depending on the user's ingredient inventory, ingredients can be used up without waste. Suggestion algorithms include, but are not limited to, machine learning algorithms and rule-based algorithms. Some or all of the above processing in the suggestion unit may be performed using, for example, AI, or without AI. For example, the suggestion unit can input the user's ingredient inventory into a generating AI and have the generating AI execute the application of the suggestion algorithm.

[0091] The suggestion unit can estimate the user's emotions and adjust the order of recipe suggestions based on the estimated emotions. For example, if the user is stressed, the suggestion unit will prioritize suggesting easy and quick recipes. For example, if the user is relaxed, the suggestion unit may prioritize suggesting recipes that can be enjoyed over time. For example, if the user is in a hurry, the suggestion unit may prioritize suggesting recipes that can be prepared quickly. In this way, by adjusting the order of recipe suggestions according to the user's emotions, more appropriate recipes can be suggested. The order of recipe suggestions may include, but are not limited to, importance and frequency of use. Some or all of the above processing in the suggestion unit is implemented using emotion estimation functions, for example, using an emotion engine or generative AI. Generative AI may include, but are not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0092] The suggestion unit can determine the priority of recipe suggestions based on the user's ingredient purchase history. For example, the suggestion unit can prioritize suggesting recipes related to ingredients the user has purchased in the past. The suggestion unit can also prioritize suggesting recipes that include frequently used ingredients based on the user's purchase history. The suggestion unit can also analyze the user's purchase patterns and suggest the most suitable recipe. This allows for the suggestion of more appropriate recipes by determining the priority of suggestions based on the user's ingredient purchase history. Ingredient purchase history includes, but is not limited to, purchase history data and receipt information. Some or all of the above processing in the suggestion unit may be performed using, for example, AI, or not using AI. For example, the suggestion unit can input the user's ingredient purchase history into a generating AI and have the generating AI determine the priority of suggestions.

[0093] The suggestion unit can analyze the user's food consumption trends and suggest relevant recipes when proposing recipes. For example, the suggestion unit can suggest relevant recipes based on the ingredients the user frequently consumes. The suggestion unit can also analyze the user's consumption trends and suggest recipes that use ingredients without waste. The suggestion unit can also suggest new cooking ideas based on the user's consumption patterns. This allows for the use of ingredients without waste by analyzing the user's food consumption trends. Food consumption trends include, but are not limited to, consumption frequency and consumption amount. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's food consumption trends into a generating AI and have the generating AI suggest relevant recipes.

[0094] The service provider can estimate the user's emotions and adjust how cooking tips and techniques are provided based on the estimated emotions. For example, if the user is stressed, the service provider can provide simple and easy-to-understand cooking tips. For example, if the user is relaxed, the service provider can also provide detailed cooking techniques. For example, if the user is in a hurry, the service provider can also provide tips for quick cooking. This allows for more appropriate support by adjusting how cooking tips and techniques are provided according to the user's emotions. The method of providing cooking tips and techniques includes, but is not limited to, the frequency of provision and the way they are displayed. Some or all of the above processing in the service provider is implemented using emotion estimation functions, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0095] The service provider can adjust the level of detail provided based on the user's cooking skill when offering cooking tips and techniques. For example, it can provide basic cooking methods to beginner users, advanced cooking techniques to intermediate users, and professional cooking techniques to advanced users. This allows for more appropriate support by adjusting the level of detail based on the user's cooking skill. Cooking skills include, but are not limited to, self-reported data and past cooking history. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's cooking skills into a generating AI and have the generating AI adjust the level of detail of the information provided.

[0096] The service provider can apply different service algorithms depending on the user's cooking history when providing cooking tips and techniques. For example, the service provider can provide relevant cooking tips based on dishes the user has made in the past. The service provider can also analyze the user's cooking history and provide optimal cooking techniques. The service provider can also suggest new cooking methods based on the user's cooking patterns. By applying different service algorithms depending on the user's cooking history, more appropriate support can be provided. Service algorithms include, but are not limited to, machine learning algorithms and rule-based algorithms. Some or all of the above processing in the service provider may be performed using, for example, AI, or not using AI. For example, the service provider can input the user's cooking history into a generating AI and have the generating AI apply the service algorithm.

[0097] The service provider can estimate the user's emotions and adjust the order in which cooking tips and techniques are provided based on the estimated emotions. For example, if the user is stressed, the service provider will prioritize providing easy and simple tips. For example, if the user is relaxed, the service provider may prioritize providing detailed cooking techniques. For example, if the user is in a hurry, the service provider may prioritize providing tips that can be done quickly. This allows for more appropriate support by adjusting the order in which cooking tips and techniques are provided according to the user's emotions. The order in which cooking tips and techniques are provided may include, but are not limited to, importance and frequency of use. Some or all of the above processing in the service provider is implemented using emotion estimation functions, for example, using an emotion engine or generative AI. Generative AI may include, but are not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0098] The service provider can select the optimal method of providing cooking tips and techniques by considering the user's cooking equipment information. For example, the service provider can suggest the optimal cooking method based on the cooking equipment the user owns. The service provider can also provide appropriate cooking techniques based on the user's cooking equipment information. For example, if the user uses a specific cooking utensil, the service provider can provide tips suitable for that utensil. This allows for more appropriate support by considering the user's cooking equipment information. Cooking equipment information includes, but is not limited to, the types of cooking utensils owned and how often they are used. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's cooking equipment information into a generating AI and have the generating AI select the optimal method of providing the tips.

[0099] The service provider can improve the accuracy of its recommendations by referring to the user's ingredient inventory when providing cooking tips and techniques. For example, the service provider can provide optimal cooking tips based on the ingredients the user has in their refrigerator. For example, the service provider can also provide cooking techniques that utilize ingredients efficiently, taking into account the user's inventory. For example, the service provider can also provide new recipe ideas based on the user's inventory information. This allows for more appropriate support by referring to the user's ingredient inventory. Ingredient inventory includes, but is not limited to, inventory management data and purchase history data. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's ingredient inventory into a generating AI and have the generating AI perform improvements to the accuracy of its recommendations.

[0100] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0101] The GourmetGenie Platform can monitor the storage environment of a user's food items and suggest optimal storage methods. For example, it can use sensors to detect temperature and humidity inside the refrigerator and provide advice on maintaining the appropriate storage temperature. It can also send notifications to the user if the storage environment is not suitable, prompting them to improve their storage methods. Furthermore, it can recalculate the expiration date of food items based on the storage environment, enabling more accurate expiration date management. This helps maintain the quality of food items and reduces waste.

[0102] The GourmetGenie Platform can analyze a user's grocery purchase history and predict when their next purchase will be. For example, it can learn the user's consumption patterns for frequently purchased ingredients and notify them of the next purchase timing. It can also send reminders in advance if a particular ingredient is running low, preventing users from forgetting to buy it. Furthermore, based on the purchase history, it can suggest new ingredients and recipes that suit the user's preferences. This allows users to manage their groceries efficiently and shop in a planned manner.

[0103] The GourmetGenie Platform can analyze users' food consumption patterns and provide advice to reduce food waste. For example, it can identify ingredients that users frequently throw away and suggest recipes to use them up. It can also advise on appropriate purchase quantities based on the rate of food consumption. Furthermore, it can provide tips on how to store and cook ingredients to prevent spoilage. As a result, users can use their ingredients without waste and live an economical and environmentally friendly life.

[0104] The GourmetGenie Platform can analyze the nutritional value of a user's food choices and suggest balanced meals. For example, it can check the balance of nutrients a user is consuming and suggest recipes to supplement any deficiencies. It can also create and provide meal plans tailored to specific health goals. Furthermore, it can advise on how to choose and cook healthy ingredients based on their nutritional value. This allows users to maintain a healthy diet and enjoy nutritionally balanced meals.

[0105] The GourmetGenie Platform can manage users' food allergy information and suggest safe recipes. For example, users can register the foods they are allergic to, and the platform will suggest recipes that do not contain those foods. It can also provide advice on points to be aware of when purchasing ingredients based on allergy information. Furthermore, it can suggest recipes to provide safe meals for family and friends with allergies. This allows users to enjoy meals with peace of mind and reduces the risks associated with allergies.

[0106] The GourmetGenie Platform can estimate a user's emotions and suggest food storage methods based on those emotions. For example, if a user is stressed, it can suggest simple, hassle-free storage methods. If the user is relaxed, it can provide more detailed storage instructions and advice on maintaining food quality. Furthermore, if the user is in a hurry, it can suggest quick storage methods. This allows the platform to provide the optimal storage method according to the user's emotions, thereby reducing food waste.

[0107] The GourmetGenie Platform can estimate a user's emotions and create a grocery shopping list based on those emotions. For example, if a user is stressed, it can add ingredients that are easy and quick to cook to the list. If the user is relaxed, it can suggest ingredients for trying new recipes. Furthermore, if the user is in a hurry, it can add ingredients that can be cooked quickly to the list. This allows for efficient shopping by creating an optimal grocery shopping list tailored to the user's emotions.

[0108] The GourmetGenie Platform can estimate a user's emotions and suggest a food consumption plan based on those emotions. For example, if a user is stressed, it will prioritize suggesting foods that are easy and quick to consume. If a user is relaxed, it can provide a food consumption plan that allows for leisurely enjoyment. Furthermore, if a user is in a hurry, it can suggest foods that can be consumed quickly. This allows for the provision of an optimal food consumption plan tailored to the user's emotions, enabling efficient use of ingredients.

[0109] The GourmetGenie Platform can estimate a user's emotions and suggest cooking methods based on those emotions. For example, if a user is stressed, it can suggest a simple and easy cooking method. If a user is relaxed, it can offer a cooking method that allows for more time and enjoyment. Furthermore, if a user is in a hurry, it can suggest a quick cooking method. This allows the platform to provide the optimal cooking method according to the user's emotions, enhancing the enjoyment of cooking.

[0110] The GourmetGenie Platform can estimate a user's emotions and adjust the shelf life of food based on those emotions. For example, if a user is stressed, it can set a shorter shelf life and suggest consuming the food sooner. If a user is relaxed, it can set a longer shelf life so they can enjoy it at their leisure. Furthermore, if a user is in a hurry, it can optimize the shelf life to manage food efficiently. This allows for setting the optimal shelf life according to the user's emotions, reducing food waste.

[0111] The following briefly describes the processing flow for example form 2.

[0112] Step 1: The collection unit collects food information by reading the 2D code on the food. For example, it uses a 2D code reader to scan the 2D code on the food and collects information such as the type of food, place of origin, and nutritional content. Step 2: The management department manages best-before and expiration dates based on the food information collected by the collection department. For example, it stores the collected food information in a database and sends notifications to users when the best-before or expiration date is approaching. Step 3: The proposal department proposes personalized recipes based on ingredient information managed by the management department. For example, it selects and proposes appropriate recipes to users based on their preferences and nutritional needs. Step 4: The provisioning department provides cooking tips and techniques based on the recipe proposed by the suggestion department. For example, it provides users with cooking methods and tips suitable for the recipe, and supports them in cooking.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] Each of the multiple elements described above, including the collection unit, management unit, suggestion unit, and provision unit, is implemented, for example, by at least one of the smart device 14 and the data processing unit 12. For example, the collection unit uses the smart device 14's 2D code reader to scan the 2D code of the ingredients and collect ingredient information. The management unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which stores the collected ingredient information in the database 24 and notifies the user when the best-before or expiration date is approaching. The suggestion unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which suggests personalized recipes based on the user's preferences and nutritional needs. The provision unit is implemented, for example, by the control unit 46A of the smart device 14, which provides cooking tips and techniques based on the suggested recipes. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0117] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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).

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] Each of the multiple elements described above, including the collection unit, management unit, suggestion unit, and provision unit, is implemented, for example, by at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit uses the 2D code reader of the smart glasses 214 to scan the 2D code of the ingredients and collect ingredient information. The management unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which stores the collected ingredient information in the database 24 and notifies the user when the best-before or expiration date is approaching. The suggestion unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which suggests personalized recipes based on the user's preferences and nutritional needs. The provision unit is implemented, for example, by the control unit 46A of the smart glasses 214, which provides cooking tips and techniques based on the suggested recipes. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0133] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] Each of the multiple elements described above, including the collection unit, management unit, suggestion unit, and provision unit, is implemented, for example, by at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit uses the headset terminal 314's 2D code reader to scan the 2D code of the ingredients and collect ingredient information. The management unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which stores the collected ingredient information in the database 24 and notifies the user when the best-before or expiration date is approaching. The suggestion unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which suggests personalized recipes based on the user's preferences and nutritional needs. The provision unit is implemented, for example, by the control unit 46A of the headset terminal 314, which provides cooking tips and techniques based on the suggested recipes. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0149] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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).

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.).

[0162] 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.

[0163] 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.

[0164] 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.

[0165] Each of the multiple elements described above, including the collection unit, management unit, suggestion unit, and provision unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the collection unit uses the robot 414's 2D code reader to scan the 2D codes of ingredients and collect ingredient information. The management unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12, which stores the collected ingredient information in the database 24 and notifies the user when the best-before or expiration date is approaching. The suggestion unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12, which suggests personalized recipes based on the user's preferences and nutritional needs. The provision unit is implemented by, for example, the control unit 46A of the robot 414, which provides cooking tips and techniques based on the suggested recipes. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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."

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] (Note 1) A collection unit that collects food information by reading the 2D code on the food ingredients, A management unit manages best-before dates and expiration dates based on the food information collected by the aforementioned collection unit, A proposal unit that proposes personalized recipes based on ingredient information managed by the aforementioned management unit, The system includes a providing unit that provides cooking tips and techniques based on the recipe proposed by the aforementioned proposal unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is Food ingredient information is collected by scanning a 2D code. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned management department, Manage best-before and expiration dates based on collected food information. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned proposal section is, Personalized recipes are suggested based on the user's preferences and nutritional needs. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned supply unit is, Provides cooking tips and techniques based on the suggested recipe. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned management department, Send a notification to the user when the expiration date is approaching. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned proposal section is, Based on the ingredients the user has, we suggest appropriate ingredient combinations. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is A unit that estimates the user's emotions and adjusts the timing of reading the 2D code based on the estimated user emotions, The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is The system analyzes the user's past food purchase history and selects the appropriate method for reading 2D codes. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is When scanning a 2D code, filtering is performed based on the user's current food inventory status. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is A unit that estimates the user's emotions and determines the priority of 2D codes to read based on the estimated user emotions, The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is When scanning a 2D code, the system prioritizes collecting highly relevant food ingredient information, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned collection unit is When a QR code is scanned, the system analyzes the user's social media activity and collects relevant food information. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned management department, A unit that estimates the user's emotions and adjusts the method of managing best-before and expiration dates based on the estimated user emotions, The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned management department, When managing best-before or expiration dates, adjust the level of detail based on how the food is stored. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned management department, When managing best-before and expiration dates, different management algorithms are applied depending on the food category. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned management department, A unit that estimates the user's emotions and adjusts the timing of notifications for best-before and expiration dates based on the estimated user emotions, The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned management department, When managing best-before and expiration dates, prioritize management based on when the ingredients were purchased. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned management department, When managing best-before and expiration dates, refer to related recipe information for ingredients to improve the accuracy of management. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned proposal section is, A unit that estimates the user's emotions and adjusts the recipe suggestion method based on the estimated user emotions, The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned proposal section is, When suggesting recipes, adjust the level of detail based on the user's health status. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned proposal section is, When suggesting recipes, different suggestion algorithms are applied depending on the user's ingredient inventory. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned proposal section is, A unit that estimates the user's emotions and adjusts the order of recipe suggestions based on the estimated user emotions, The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned proposal section is, When suggesting recipes, the system prioritizes suggestions based on the user's ingredient purchase history. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned proposal section is, When suggesting recipes, the system analyzes the user's food consumption trends and proposes relevant recipes. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned supply unit is, A unit that estimates the user's emotions and adjusts the method of providing cooking tips and techniques based on the estimated user emotions, The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned supply unit is, When providing cooking tips and techniques, adjust the level of detail based on the user's cooking skill level. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned supply unit is, When providing cooking tips and techniques, different algorithms are applied depending on the user's cooking history. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned supply unit is, A unit that estimates the user's emotions and adjusts the order in which cooking tips and techniques are provided based on the estimated user emotions, The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned supply unit is, When providing cooking tips and techniques, the optimal method of delivery is selected by considering the user's cooking equipment information. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned supply unit is, When providing cooking tips and techniques, we improve the accuracy of the suggestions by referring to the user's ingredient inventory status. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0185] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A collection unit that collects food information by reading the 2D code on the food ingredients, A management unit manages best-before dates and expiration dates based on the food information collected by the aforementioned collection unit, A proposal unit that proposes personalized recipes based on ingredient information managed by the aforementioned management unit, The system includes a providing unit that provides cooking tips and techniques based on the recipe proposed by the aforementioned proposal unit. A system characterized by the following features.

2. The aforementioned proposal section is, Personalized recipes are suggested based on the user's preferences and nutritional needs. The system according to feature 1.

3. The aforementioned supply unit is, Provides cooking tips and techniques based on the suggested recipe. The system according to feature 1.

4. The aforementioned management department, Send a notification to the user when the expiration date is approaching. The system according to feature 1.

5. The aforementioned proposal section is, Based on the ingredients the user has, we suggest appropriate ingredient combinations. The system according to feature 1.

6. The aforementioned collection unit is A unit that estimates the user's emotions and adjusts the timing of reading the 2D code based on the estimated user emotions, The system according to feature 1.

Images