system

Abstract

The system according to this embodiment aims to suggest the optimal menu by taking into account the family's food preferences, allergy information, weather, health status, and other factors. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a list generation unit, a special offer information reflection unit, and a support unit. The collection unit collects information such as the family's food preferences and allergy information, weather, daily meal content, health status, and ingredients in the refrigerator. The analysis unit analyzes the information collected by the collection unit and proposes an optimal menu. The list generation unit generates a shopping list based on the menu proposed by the analysis unit. The special offer information reflection unit reflects special offer information in the shopping list generated by the list generation unit. The support unit assists the cooking process based on the shopping list generated by the list generation unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, an optimal menu proposal considering the dietary preferences and allergy information of family members, weather, health conditions, etc. has not been sufficiently made, and there is room for improvement.

[0005] The system according to the embodiment aims to propose an optimal menu considering the dietary preferences and allergy information of family members, weather, health conditions, etc.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, an analysis unit, a list generation unit, a special offer information reflection unit, and a support unit. The collection unit collects information such as family members' food preferences and allergy information, weather, daily meal content, health status, and ingredients in the refrigerator. The analysis unit analyzes the information collected by the collection unit and proposes an optimal menu. The list generation unit generates a shopping list based on the menu proposed by the analysis unit. The special offer information reflection unit reflects special offer information in the shopping list generated by the list generation unit. The support unit assists the cooking process based on the shopping list generated by the list generation unit. [Effects of the Invention]

[0007] The system according to this embodiment can suggest the optimal menu by taking into account the family's food preferences, allergy information, weather, health status, and other factors. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The Cooking Concierge System according to an embodiment of the present invention is a personal AI agent that solves the problems of users who plan their daily meals. This Cooking Concierge System collects information such as family members' food preferences and allergy information, weather, daily eating habits, health status, and ingredients in the refrigerator, and proposes the optimal menu. Furthermore, it generates a shopping list based on the proposed menu and reflects sale information. Finally, it assists in the cooking process and improves the efficiency of common preparation steps. This supports a healthy and economical diet. First, the Cooking Concierge System collects information such as family members' food preferences and allergy information, weather, daily eating habits, health status, and ingredients in the refrigerator. In this process, it utilizes not only information entered by the user, but also sensors to grasp the information of ingredients in the refrigerator in real time and an internet connection to obtain weather information. Next, the Cooking Concierge System analyzes the collected information and proposes the optimal menu. For example, if the user enters, "It's cold today, so I want to eat warm soup," the AI ​​agent checks the ingredients in the refrigerator and proposes a recipe for warm soup. Furthermore, the Cooking Concierge System generates a shopping list based on the proposed menu and reflects sale information. For example, based on sale information, it suggests menus using this week's sale items and generates a shopping list. Finally, the cooking concierge system assists with the cooking process and streamlines common preparation steps. For instance, it provides real-time instructions on "what to do next" during cooking. It also suggests methods for streamlining common preparation steps, such as cutting vegetables all at once or marinating meat all at once. In this way, the cooking concierge system comprehensively supports the user's eating habits, saving the user the trouble of planning daily menus and enabling a healthy and economical lifestyle.

[0029] The cooking concierge system according to this embodiment comprises a collection unit, an analysis unit, a list generation unit, a special offer information reflection unit, and a support unit. The collection unit collects information such as family members' food preferences and allergy information, weather, daily meal content, health status, and ingredients in the refrigerator. The collection unit collects information entered by the user, for example. The collection unit can also use sensors to grasp information about ingredients in the refrigerator in real time. Furthermore, the collection unit can also obtain weather information using an internet connection. For example, the collection unit detects information about ingredients in the refrigerator using sensors and grasps it in real time. The collection unit obtains weather information using an internet connection and reflects it in menu suggestions. The analysis unit analyzes the information collected by the collection unit and proposes the optimal menu. For example, the analysis unit proposes a menu based on information entered by the user. For example, if the user enters "It's cold today, so I want to eat warm soup," the analysis unit checks the ingredients in the refrigerator and proposes a recipe for warm soup. The analysis unit can also use AI to analyze the collected information and propose the optimal menu. The list generation unit generates a shopping list based on the menu proposed by the analysis unit. For example, the list generation unit lists the ingredients needed for the proposed menu. The list generation unit can also generate a shopping list based on sale information. For example, the list generation unit reflects the sale information, proposes a menu using this week's sale items, and generates a shopping list. The sale information reflection unit reflects the sale information in the shopping list generated by the list generation unit. For example, the sale information reflection unit optimizes the shopping list based on the sale information. The sale information reflection unit can also update the sale information in real time to reflect the latest information. The support unit assists the cooking process based on the shopping list generated by the list generation unit. For example, the support unit gives real-time instructions on "what to do next" during cooking. To improve the efficiency of common preparation, the support unit suggests methods such as cutting vegetables all at once or marinating meat all at once. The support unit can also use AI to assist the cooking process.As a result, the cooking concierge system according to this embodiment can comprehensively support the user's eating habits, eliminate the hassle of planning daily menus, and enable a healthy and economical diet.

[0030] The data collection unit collects information such as family members' dietary preferences and allergy information, weather, daily meal content, health status, and the contents of the refrigerator. Specifically, it provides an interface for collecting user-inputted information, making it easy for users to input information. For example, users can input family members' dietary preferences and allergy information through a smartphone app or web application. The data collection unit can also utilize sensors to monitor the contents of the refrigerator in real time. This includes cameras, weight sensors, and RFID tags installed inside the refrigerator. These sensors detect the type and quantity of food in the refrigerator and transmit the information to the data collection unit. Furthermore, the data collection unit can obtain weather information using an internet connection. For example, it can obtain weather information from a weather forecast service via an API and incorporate it into menu suggestions. The data collection unit centrally manages this information and stores it in a database. This allows the data collection unit to efficiently collect diverse information about the user's eating habits and provide it to the analysis unit and other departments. The data collection unit regularly updates user input information and sensor data to maintain the latest information. In addition, the data collection unit implements data encryption and access control to protect user privacy. This allows the data collection unit to safely and efficiently collect information about users' dietary habits, thereby improving the overall performance of the system.

[0031] The analysis department analyzes the information collected by the data collection department and proposes the most suitable menu. Specifically, it proposes menus based on information entered by the user. For example, if a user enters "It's cold today, so I want to eat some warm soup," the analysis department will check the ingredients in the refrigerator and propose a recipe for warm soup. The analysis department can also use AI to analyze the collected information and propose the most suitable menu. The AI ​​uses natural language processing technology to understand the user's input and select an appropriate menu. For example, if a user enters "I want to make a healthy dinner," the AI ​​will propose a low-calorie, nutritionally balanced menu based on the collected information. In addition, the AI ​​can learn from past data and user preferences to provide menus that are optimal for each individual user. Furthermore, the analysis department proposes menus considering weather information and health conditions. For example, it will suggest warm dishes on cold days and refreshing dishes on hot days. It will also suggest nutritionally balanced menus according to the user's health condition. In this way, the analysis department can propose the most suitable menu to meet the user's needs and improve the quality of their eating habits.

[0032] The list generation unit generates a shopping list based on the menu suggested by the analysis unit. Specifically, it lists the ingredients needed for the suggested menu. The list generation unit checks the ingredients in the refrigerator and adds any missing ingredients to the list. For example, if an ingredient needed for the suggested menu is not in the refrigerator, it adds that ingredient to the shopping list. The list generation unit can also generate a shopping list based on sale information. For example, it can reflect sale information to suggest a menu using this week's sale items and generate a shopping list. The list generation unit updates the sale information in real time to reflect the latest information. This allows the list generation unit to generate an economical shopping list, saving users money on groceries. Furthermore, the list generation unit provides the shopping list to the user through a smartphone app or web application. Users can check the shopping list and efficiently purchase the necessary ingredients. In this way, the list generation unit can support users' shopping and improve the quality of their diet.

[0033] The special offer information reflection unit reflects special offer information in the shopping list generated by the list generation unit. Specifically, it optimizes the shopping list based on the special offer information. The special offer information reflection unit can also update special offer information in real time and reflect the latest information. For example, the special offer information reflection unit collects special offer information provided by supermarkets and online stores and reflects it in the shopping list. This allows users to make economical purchases by using special offer items. The special offer information reflection unit provides an interface for collecting special offer information and efficiently collects information from supermarkets and online stores. The special offer information reflection unit stores the collected special offer information in a database and provides it to the list generation unit and support unit. In this way, the special offer information reflection unit can support users' shopping and help them save on grocery expenses. Furthermore, the special offer information reflection unit optimizes special offer information considering the user's preferences and past purchase history. For example, it prioritizes displaying special offer information for ingredients that the user frequently purchases. In this way, the special offer information reflection unit can provide special offer information that meets the user's needs and improve the efficiency of shopping.

[0034] The support unit assists with the cooking process based on the shopping list generated by the list generation unit. Specifically, it provides real-time instructions on "what to do next" during cooking. To streamline common preparation steps, the support unit suggests methods such as cutting vegetables all at once or marinating meat all at once. The support unit can also use AI to assist with the cooking process. The AI ​​considers the user's cooking skills and environment to suggest the optimal cooking procedure. For example, it provides detailed instructions and points to note for beginner users, and suggests efficient cooking methods for experienced users. The support unit provides cooking instructions through a smartphone app or web application. Users can check the app while cooking to understand the next steps. The support unit can also use a voice assistant to provide voice guidance for the cooking procedure. This allows users to check the cooking procedure without using their hands. Furthermore, the support unit manages the timing during cooking and instructs the next step at the appropriate time. For example, it may instruct "add the vegetables next" in the middle of a stew. In this way, the support unit can efficiently support the user's cooking and improve the quality of the dish.

[0035] The data collection unit can grasp information about the food items inside the refrigerator in real time. For example, the data collection unit can detect information about the food items inside the refrigerator using sensors and grasp it in real time. The data collection unit can collect information such as the type, quantity, and expiration date of the food items inside the refrigerator. The data collection unit can also grasp information about the food items inside the refrigerator in real time using sensor technology. For example, the data collection unit can detect information about the food items inside the refrigerator using sensors, transmit it to the cloud using an internet connection, and grasp it in real time. This allows for accurate menu suggestions by grasping information about the food items inside the refrigerator in real time.

[0036] The data collection unit can obtain weather information using an internet connection. For example, the data collection unit can obtain weather information using an internet connection and reflect it in menu suggestions. The data collection unit can obtain various types of weather information, such as temperature, precipitation, and humidity. The data collection unit can also obtain weather information using APIs. For example, the data collection unit can obtain the latest weather information using the API of a weather information service. The data collection unit can also obtain weather information using web scraping technology. For example, the data collection unit can scrape information from a website that provides weather information. By obtaining weather information, it becomes possible to suggest menus that are appropriate for the season and weather.

[0037] The analysis department can suggest the optimal menu based on the information entered by the user. For example, if the user enters "It's cold today, so I want to eat some warm soup," the analysis department will check the ingredients in the refrigerator and suggest a recipe for warm soup. The analysis department can consider user input information such as food preferences, allergy information, and health status. The analysis department can also use AI to analyze the information entered by the user and suggest the optimal menu. For example, the analysis department can input the information entered by the user into the AI, which will then suggest the optimal menu. This makes it possible to suggest menus that meet the user's needs by suggesting the optimal menu based on the information entered by the user.

[0038] The list generation unit can generate shopping lists based on sale information. For example, it can suggest menus using this week's sale items and generate a shopping list based on the sale information. The list generation unit can consider various types of sale information, such as discount rate, sale period, and target products. The list generation unit can also generate shopping lists based on sale information using AI. For example, the list generation unit can input sale information into the AI, which will then generate an optimal shopping list. This allows for more economical shopping by generating shopping lists based on sale information.

[0039] The support unit can provide real-time instructions on "what to do next" during cooking. For example, the support unit can use a voice assistant to give instructions on "what to do next" during cooking. The support unit can also give instructions during cooking using a smartphone app. For example, the support unit can display the next step in cooking through the smartphone app. The support unit can also use AI to give instructions during cooking. For example, the support unit can input the progress of cooking into the AI, and the AI ​​will give instructions on the next step. This improves cooking efficiency by providing instructions in real time during cooking.

[0040] The support department can propose methods to streamline common food preparation, such as cutting vegetables all at once or marinating meat all at once. For example, the support department can propose methods for cutting vegetables all at once. The support department can also propose methods for marinating meat all at once. The support department can also use AI to propose methods for streamlining common food preparation. For example, the support department can input methods for streamlining common food preparation into the AI, and the AI ​​can propose the optimal method. This will reduce the effort required for cooking by streamlining common food preparation.

[0041] The data collection unit can identify ingredients that should be used first, taking into account their expiration dates. For example, the unit can monitor the expiration dates of ingredients in the refrigerator in real time and suggest menus that prioritize the use of ingredients nearing their expiration date. The unit can also list ingredients that are nearing their expiration date and collect recipes that use them. The unit can also suggest cooking and storage methods for ingredients that are nearing their expiration date. This reduces food waste by considering the expiration dates of ingredients in the refrigerator.

[0042] The data collection unit can monitor changes in the family's health status in real time and update the information it collects as needed. For example, it can monitor the family's health status and collect suitable ingredients and recipes when symptoms of a cold or allergy appear. The data collection unit can also collect information to suggest nutritionally balanced meal menus in response to changes in health status. Based on changes in health status, the data collection unit can also collect ingredients and recipes that are rich in specific nutrients. This allows for the suggestion of health-conscious menus by updating information in response to changes in the family's health status.

[0043] The data collection unit can collect not only weather information but also seasonal and local event information and incorporate it into menu suggestions. For example, the data collection unit can collect information on seasonal ingredients and dishes and incorporate it into menu suggestions. The data collection unit can also collect information on ingredients and dishes related to local events and festivals and incorporate it into menu suggestions. The data collection unit can also collect information on seasonal ingredients and dishes based on weather information and incorporate it into menu suggestions. As a result, by collecting seasonal and local event information, a wider variety of menu suggestions become possible.

[0044] The data collection unit can improve accuracy by referring to past meal history when collecting family members' dietary preferences and allergy information. For example, the data collection unit analyzes the family's past meal history to collect preferences and allergy information. Based on past meal history, the data collection unit can also collect ingredients and recipes that match the family's preferences. The data collection unit can also accurately collect allergy information by referring to past meal history and reflect it in menu suggestions. This makes it possible to collect more accurate information by referring to past meal history.

[0045] The analysis department can propose menus that are optimal for a person's health condition, taking into account the nutritional value of ingredients. For example, the analysis department can analyze the nutritional value of ingredients and propose balanced meal menus. The analysis department can also propose menus that use ingredients rich in specific nutrients. The analysis department can also propose menus that use highly nutritious ingredients according to a person's health condition. In this way, by considering the nutritional value of ingredients, it becomes possible to propose health-conscious menus.

[0046] The analysis department can suggest multiple menu options based on family dietary preferences and allergy information. For example, the analysis department can suggest multiple menu options considering family preferences and allergy information. The analysis department can also suggest menu options using ingredients that suit family preferences. Furthermore, based on allergy information, the analysis department can suggest menu options using safe ingredients. This allows for the suggestion of a wider variety of menu options by considering family preferences and allergy information.

[0047] The analysis department can suggest seasonal menus based on weather information. For example, in winter, the analysis department can suggest warm soups and stews. In summer, it can suggest cold salads and light meals. On rainy days, the analysis department can suggest meals that can be enjoyed at home. In this way, by suggesting menus based on weather information, it becomes possible to have meals that are appropriate for the season.

[0048] The analysis department can analyze your daily eating habits and suggest a balanced diet. For example, the analysis department can analyze your daily eating habits and suggest a nutritionally balanced menu. The analysis department can also suggest menus to improve unbalanced eating habits. Based on your daily eating habits, the analysis department can suggest healthy meal menus. In this way, by analyzing your daily eating habits, it can suggest a nutritionally balanced diet.

[0049] The list generation unit can monitor the inventory status of food items in the refrigerator in real time and add only the necessary items to the list. For example, the list generation unit can monitor the inventory status of food items in the refrigerator in real time and add only the necessary items to the list. The list generation unit can also prioritize adding items with low inventory to the list. The list generation unit can also add necessary items to the list considering the expiration dates of the food items in the refrigerator. This allows for efficient shopping by monitoring the inventory status of food items in the refrigerator in real time.

[0050] The list generation unit can generate cost-effective shopping lists by taking sale information into consideration. For example, the list generation unit can add cost-effective ingredients to the list based on sale information. The list generation unit can also optimize the shopping list within a budget by taking sale information into consideration. The list generation unit can also suggest bulk purchases based on sale information and generate an economical shopping list. In this way, economical shopping becomes possible by taking sale information into consideration.

[0051] The list generation unit can prioritize adding environmentally friendly eco-products to the shopping list. For example, the list generation unit will prioritize adding environmentally friendly eco-products to the list. The list generation unit can also optimize the shopping list based on special sale information for eco-products. The list generation unit can also provide an option to add only environmentally friendly products to the list. This makes environmentally friendly shopping possible by prioritizing the addition of environmentally friendly eco-products.

[0052] The list generation unit can add new and recommended products to the shopping list based on the user's preferences. For example, the list generation unit can add new products to the list based on the user's preferences. The list generation unit can also add recommended products to the list based on the user's past purchase history. The list generation unit can also provide an option to add new and recommended products that match the user's preferences to the list. This allows for a more satisfying shopping experience by adding new and recommended products tailored to the user's preferences.

[0053] The special sale information display unit can update special sale information in real time and reflect the latest information. For example, the special sale information display unit can acquire special sale information in real time and reflect the latest information. The special sale information display unit can also increase the frequency of special sale information updates and always provide the latest information. The special sale information display unit can also immediately reflect any changes to special sale information. As a result, by updating special sale information in real time, it is possible to always provide the latest information.

[0054] The special offer information section can suggest menus that offer high cost-saving benefits based on special offer information. For example, the special offer information section can suggest menus that offer good value for money based on special offer information. The special offer information section can also suggest the best menu within the budget, taking special offer information into consideration. The special offer information section can also suggest bulk purchases based on special offer information, and suggest economical menus. In this way, economical meals become possible by suggesting menus based on special offer information.

[0055] The special sale information reflection unit can reflect not only special sale information, but also coupon information and point reward information. For example, the special sale information reflection unit can reflect coupon information in addition to special sale information. The special sale information reflection unit can also reflect point reward information in addition to special sale information. The special sale information reflection unit can also generate an optimal shopping list based on coupon information and point reward information. This makes it possible to shop more economically by reflecting coupon information and point reward information.

[0056] The special sale information display unit can support economical shopping by suggesting bulk purchases based on special sale information. For example, the special sale information display unit can suggest bulk purchases based on special sale information. It can also present the cost-saving effects of bulk purchases. Based on the bulk purchase suggestions, the special sale information display unit can generate an economical shopping list. This makes economical shopping possible by suggesting bulk purchases.

[0057] The support unit can monitor the progress of cooking in real time and provide appropriate instructions for the next steps. For example, the support unit can monitor the progress of cooking in real time and provide appropriate instructions for the next steps. The support unit can also provide timely instructions for the next steps according to the progress of cooking. The support unit can also optimize cooking time based on the progress of cooking. As a result, cooking efficiency is improved by monitoring the progress of cooking in real time.

[0058] The support department can also offer suggestions on how to use and maintain cooking utensils as part of its support for the cooking process. For example, the support department can suggest how to use cooking utensils. The support department can also suggest how to maintain cooking utensils. The support department can also provide videos demonstrating how to use and maintain cooking utensils. By offering suggestions on how to use and maintain cooking utensils, the efficiency of cooking can be improved.

[0059] The support department can propose cooking methods that allow the whole family to participate in the cooking process. For example, the support department can propose cooking methods that allow the whole family to participate. The support department can also propose cooking methods that the whole family can enjoy. The support department can also propose recipes that allow the whole family to cook together. By proposing cooking methods that allow the whole family to participate, it becomes possible to make cooking enjoyable for the whole family.

[0060] The support department can also offer suggestions for time-saving recipes and single-dish meals as part of its assistance with the cooking process. For example, the support department can suggest time-saving recipes. The support department can also suggest single-dish meal recipes. Based on the suggestions for time-saving recipes and single-dish meals, the support department can optimize cooking time. In this way, the efficiency of cooking is improved by suggesting time-saving recipes and single-dish meals.

[0061] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0062] The data collection unit can identify ingredients that should be used first, taking into account their expiration dates. For example, it can monitor the expiration dates of ingredients in the refrigerator in real time and suggest menus that prioritize the use of ingredients nearing their expiration date. It can also list ingredients that are nearing their expiration date and collect recipes that use them. It can also suggest cooking and storage methods for ingredients that are nearing their expiration date. In this way, food waste can be reduced by considering the expiration dates of ingredients in the refrigerator.

[0063] The data collection unit can monitor changes in family health in real time and update the collected information as needed. For example, it can monitor family health and collect suitable ingredients and recipes when symptoms of a cold or allergy appear. It can also collect information to suggest nutritionally balanced meal menus in response to changes in health. Based on changes in health, it can also collect ingredients and recipes that are rich in specific nutrients. This allows for health-conscious menu suggestions by updating information in response to changes in family health.

[0064] The data collection unit can gather not only weather information but also seasonal and local event information and incorporate it into menu suggestions. For example, it can collect information on seasonal ingredients and dishes and incorporate it into menu suggestions. It can also collect information on ingredients and dishes related to local events and festivals and incorporate it into menu suggestions. Based on weather information, it can also collect information on seasonal ingredients and dishes and incorporate it into menu suggestions. In this way, by collecting seasonal and local event information, a wider variety of menu suggestions becomes possible.

[0065] The analysis department can propose menus that are optimal for a person's health condition, taking into account the nutritional value of ingredients. For example, it can analyze the nutritional value of ingredients and propose balanced meal menus. It can also propose menus using ingredients that are rich in specific nutrients. Depending on the person's health condition, it can also propose menus using highly nutritious ingredients. In this way, by considering the nutritional value of ingredients, it becomes possible to propose health-conscious menus.

[0066] The analysis department can suggest multiple menu options based on family dietary preferences and allergy information. For example, it can suggest multiple menu options considering family preferences and allergy information. It can also suggest menu options using ingredients that suit family preferences. Based on allergy information, it can also suggest menu options using safe ingredients. In this way, by considering family preferences and allergy information, a wider variety of menu options can be suggested.

[0067] The analysis department can suggest seasonal menus based on weather information. For example, in winter, it can suggest warm soups and stews. In summer, it can suggest cold salads and light meals. On rainy days, it can suggest dishes that can be enjoyed at home. In this way, by suggesting menus based on weather information, it becomes possible to have meals that are appropriate for the season.

[0068] The following briefly describes the processing flow for example form 1.

[0069] Step 1: The data collection unit gathers information such as family members' food preferences and allergy information, weather, daily meal content, health status, and the contents of the refrigerator. For example, in addition to collecting information entered by the user, it can also use sensors to monitor the contents of the refrigerator in real time. It can also obtain weather information using an internet connection. Step 2: The analysis unit analyzes the information collected by the collection unit and proposes the optimal menu. For example, it can propose a menu based on information entered by the user, or it can use AI to analyze the collected information and propose the optimal menu. Step 3: The list generation unit generates a shopping list based on the menu suggested by the analysis unit. For example, it can list the ingredients needed for the suggested menu, or it can generate a shopping list based on sale information. Step 4: The special offer information reflection unit reflects the special offer information in the shopping list generated by the list generation unit. For example, it can optimize the shopping list based on the special offer information, or it can update the special offer information in real time to reflect the latest information. Step 5: The support unit assists with the cooking process based on the shopping list generated by the list generation unit. For example, it provides real-time instructions on "what to do next" during cooking, and suggests methods to streamline common preparation steps, such as cutting vegetables all at once or seasoning meat all at once.

[0070] (Example of form 2) The Cooking Concierge System according to an embodiment of the present invention is a personal AI agent that solves the problems of users who plan their daily meals. This Cooking Concierge System collects information such as family members' food preferences and allergy information, weather, daily eating habits, health status, and ingredients in the refrigerator, and proposes the optimal menu. Furthermore, it generates a shopping list based on the proposed menu and reflects sale information. Finally, it assists in the cooking process and improves the efficiency of common preparation steps. This supports a healthy and economical diet. First, the Cooking Concierge System collects information such as family members' food preferences and allergy information, weather, daily eating habits, health status, and ingredients in the refrigerator. In this process, it utilizes not only information entered by the user, but also sensors to grasp the information of ingredients in the refrigerator in real time and an internet connection to obtain weather information. Next, the Cooking Concierge System analyzes the collected information and proposes the optimal menu. For example, if the user enters, "It's cold today, so I want to eat warm soup," the AI ​​agent checks the ingredients in the refrigerator and proposes a recipe for warm soup. Furthermore, the Cooking Concierge System generates a shopping list based on the proposed menu and reflects sale information. For example, based on sale information, it suggests menus using this week's sale items and generates a shopping list. Finally, the cooking concierge system assists with the cooking process and streamlines common preparation steps. For instance, it provides real-time instructions on "what to do next" during cooking. It also suggests methods for streamlining common preparation steps, such as cutting vegetables all at once or marinating meat all at once. In this way, the cooking concierge system comprehensively supports the user's eating habits, saving the user the trouble of planning daily menus and enabling a healthy and economical lifestyle.

[0071] The cooking concierge system according to this embodiment comprises a collection unit, an analysis unit, a list generation unit, a special offer information reflection unit, and a support unit. The collection unit collects information such as family members' food preferences and allergy information, weather, daily meal content, health status, and ingredients in the refrigerator. The collection unit collects information entered by the user, for example. The collection unit can also use sensors to grasp information about ingredients in the refrigerator in real time. Furthermore, the collection unit can also obtain weather information using an internet connection. For example, the collection unit detects information about ingredients in the refrigerator using sensors and grasps it in real time. The collection unit obtains weather information using an internet connection and reflects it in menu suggestions. The analysis unit analyzes the information collected by the collection unit and proposes the optimal menu. For example, the analysis unit proposes a menu based on information entered by the user. For example, if the user enters "It's cold today, so I want to eat warm soup," the analysis unit checks the ingredients in the refrigerator and proposes a recipe for warm soup. The analysis unit can also use AI to analyze the collected information and propose the optimal menu. The list generation unit generates a shopping list based on the menu proposed by the analysis unit. For example, the list generation unit lists the ingredients needed for the proposed menu. The list generation unit can also generate a shopping list based on sale information. For example, the list generation unit reflects the sale information, proposes a menu using this week's sale items, and generates a shopping list. The sale information reflection unit reflects the sale information in the shopping list generated by the list generation unit. For example, the sale information reflection unit optimizes the shopping list based on the sale information. The sale information reflection unit can also update the sale information in real time to reflect the latest information. The support unit assists the cooking process based on the shopping list generated by the list generation unit. For example, the support unit gives real-time instructions on "what to do next" during cooking. To improve the efficiency of common preparation, the support unit suggests methods such as cutting vegetables all at once or marinating meat all at once. The support unit can also use AI to assist the cooking process.As a result, the cooking concierge system according to this embodiment can comprehensively support the user's eating habits, eliminate the hassle of planning daily menus, and enable a healthy and economical diet.

[0072] The data collection unit collects information such as family members' dietary preferences and allergy information, weather, daily meal content, health status, and the contents of the refrigerator. Specifically, it provides an interface for collecting user-inputted information, making it easy for users to input information. For example, users can input family members' dietary preferences and allergy information through a smartphone app or web application. The data collection unit can also utilize sensors to monitor the contents of the refrigerator in real time. This includes cameras, weight sensors, and RFID tags installed inside the refrigerator. These sensors detect the type and quantity of food in the refrigerator and transmit the information to the data collection unit. Furthermore, the data collection unit can obtain weather information using an internet connection. For example, it can obtain weather information from a weather forecast service via an API and incorporate it into menu suggestions. The data collection unit centrally manages this information and stores it in a database. This allows the data collection unit to efficiently collect diverse information about the user's eating habits and provide it to the analysis unit and other departments. The data collection unit regularly updates user input information and sensor data to maintain the latest information. In addition, the data collection unit implements data encryption and access control to protect user privacy. This allows the data collection unit to safely and efficiently collect information about users' dietary habits, thereby improving the overall performance of the system.

[0073] The analysis department analyzes the information collected by the data collection department and proposes the most suitable menu. Specifically, it proposes menus based on information entered by the user. For example, if a user enters "It's cold today, so I want to eat some warm soup," the analysis department will check the ingredients in the refrigerator and propose a recipe for warm soup. The analysis department can also use AI to analyze the collected information and propose the most suitable menu. The AI ​​uses natural language processing technology to understand the user's input and select an appropriate menu. For example, if a user enters "I want to make a healthy dinner," the AI ​​will propose a low-calorie, nutritionally balanced menu based on the collected information. In addition, the AI ​​can learn from past data and user preferences to provide menus that are optimal for each individual user. Furthermore, the analysis department proposes menus considering weather information and health conditions. For example, it will suggest warm dishes on cold days and refreshing dishes on hot days. It will also suggest nutritionally balanced menus according to the user's health condition. In this way, the analysis department can propose the most suitable menu to meet the user's needs and improve the quality of their eating habits.

[0074] The list generation unit generates a shopping list based on the menu suggested by the analysis unit. Specifically, it lists the ingredients needed for the suggested menu. The list generation unit checks the ingredients in the refrigerator and adds any missing ingredients to the list. For example, if an ingredient needed for the suggested menu is not in the refrigerator, it adds that ingredient to the shopping list. The list generation unit can also generate a shopping list based on sale information. For example, it can reflect sale information to suggest a menu using this week's sale items and generate a shopping list. The list generation unit updates the sale information in real time to reflect the latest information. This allows the list generation unit to generate an economical shopping list, saving users money on groceries. Furthermore, the list generation unit provides the shopping list to the user through a smartphone app or web application. Users can check the shopping list and efficiently purchase the necessary ingredients. In this way, the list generation unit can support users' shopping and improve the quality of their diet.

[0075] The special offer information reflection unit reflects special offer information in the shopping list generated by the list generation unit. Specifically, it optimizes the shopping list based on the special offer information. The special offer information reflection unit can also update special offer information in real time and reflect the latest information. For example, the special offer information reflection unit collects special offer information provided by supermarkets and online stores and reflects it in the shopping list. This allows users to make economical purchases by using special offer items. The special offer information reflection unit provides an interface for collecting special offer information and efficiently collects information from supermarkets and online stores. The special offer information reflection unit stores the collected special offer information in a database and provides it to the list generation unit and support unit. In this way, the special offer information reflection unit can support users' shopping and help them save on grocery expenses. Furthermore, the special offer information reflection unit optimizes special offer information considering the user's preferences and past purchase history. For example, it prioritizes displaying special offer information for ingredients that the user frequently purchases. In this way, the special offer information reflection unit can provide special offer information that meets the user's needs and improve the efficiency of shopping.

[0076] The support unit assists with the cooking process based on the shopping list generated by the list generation unit. Specifically, it provides real-time instructions on "what to do next" during cooking. To streamline common preparation steps, the support unit suggests methods such as cutting vegetables all at once or marinating meat all at once. The support unit can also use AI to assist with the cooking process. The AI ​​considers the user's cooking skills and environment to suggest the optimal cooking procedure. For example, it provides detailed instructions and points to note for beginner users, and suggests efficient cooking methods for experienced users. The support unit provides cooking instructions through a smartphone app or web application. Users can check the app while cooking to understand the next steps. The support unit can also use a voice assistant to provide voice guidance for the cooking procedure. This allows users to check the cooking procedure without using their hands. Furthermore, the support unit manages the timing during cooking and instructs the next step at the appropriate time. For example, it may instruct "add the vegetables next" in the middle of a stew. In this way, the support unit can efficiently support the user's cooking and improve the quality of the dish.

[0077] The data collection unit can grasp information about the food items inside the refrigerator in real time. For example, the data collection unit can detect information about the food items inside the refrigerator using sensors and grasp it in real time. The data collection unit can collect information such as the type, quantity, and expiration date of the food items inside the refrigerator. The data collection unit can also grasp information about the food items inside the refrigerator in real time using sensor technology. For example, the data collection unit can detect information about the food items inside the refrigerator using sensors, transmit it to the cloud using an internet connection, and grasp it in real time. This allows for accurate menu suggestions by grasping information about the food items inside the refrigerator in real time.

[0078] The data collection unit can obtain weather information using an internet connection. For example, the data collection unit can obtain weather information using an internet connection and reflect it in menu suggestions. The data collection unit can obtain various types of weather information, such as temperature, precipitation, and humidity. The data collection unit can also obtain weather information using APIs. For example, the data collection unit can obtain the latest weather information using the API of a weather information service. The data collection unit can also obtain weather information using web scraping technology. For example, the data collection unit can scrape information from a website that provides weather information. By obtaining weather information, it becomes possible to suggest menus that are appropriate for the season and weather.

[0079] The analysis department can suggest the optimal menu based on the information entered by the user. For example, if the user enters "It's cold today, so I want to eat some warm soup," the analysis department will check the ingredients in the refrigerator and suggest a recipe for warm soup. The analysis department can consider user input information such as food preferences, allergy information, and health status. The analysis department can also use AI to analyze the information entered by the user and suggest the optimal menu. For example, the analysis department can input the information entered by the user into the AI, which will then suggest the optimal menu. This makes it possible to suggest menus that meet the user's needs by suggesting the optimal menu based on the information entered by the user.

[0080] The list generation unit can generate shopping lists based on sale information. For example, it can suggest menus using this week's sale items and generate a shopping list based on the sale information. The list generation unit can consider various types of sale information, such as discount rate, sale period, and target products. The list generation unit can also generate shopping lists based on sale information using AI. For example, the list generation unit can input sale information into the AI, which will then generate an optimal shopping list. This allows for more economical shopping by generating shopping lists based on sale information.

[0081] The support unit can provide real-time instructions on "what to do next" during cooking. For example, the support unit can use a voice assistant to give instructions on "what to do next" during cooking. The support unit can also give instructions during cooking using a smartphone app. For example, the support unit can display the next step in cooking through the smartphone app. The support unit can also use AI to give instructions during cooking. For example, the support unit can input the progress of cooking into the AI, and the AI ​​will give instructions on the next step. This improves cooking efficiency by providing instructions in real time during cooking.

[0082] The support department can propose methods to streamline common food preparation, such as cutting vegetables all at once or marinating meat all at once. For example, the support department can propose methods for cutting vegetables all at once. The support department can also propose methods for marinating meat all at once. The support department can also use AI to propose methods for streamlining common food preparation. For example, the support department can input methods for streamlining common food preparation into the AI, and the AI ​​can propose the optimal method. This will reduce the effort required for cooking by streamlining common food preparation.

[0083] The data collection unit can estimate the user's emotions and prioritize the information to collect based on those emotions. For example, if the user is stressed, the unit will prioritize collecting information related to relaxing meal options. If the user is tired, the unit may also prioritize collecting information on easy-to-cook ingredients and recipes. If the user is health-conscious, the unit may also prioritize collecting information on nutritious ingredients and healthy recipes. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. This allows for the collection of more relevant information by prioritizing information based on the user's emotions.

[0084] The data collection unit can identify ingredients that should be used first, taking into account their expiration dates. For example, the unit can monitor the expiration dates of ingredients in the refrigerator in real time and suggest menus that prioritize the use of ingredients nearing their expiration date. The unit can also list ingredients that are nearing their expiration date and collect recipes that use them. The unit can also suggest cooking and storage methods for ingredients that are nearing their expiration date. This reduces food waste by considering the expiration dates of ingredients in the refrigerator.

[0085] The data collection unit can monitor changes in the family's health status in real time and update the information it collects as needed. For example, it can monitor the family's health status and collect suitable ingredients and recipes when symptoms of a cold or allergy appear. The data collection unit can also collect information to suggest nutritionally balanced meal menus in response to changes in health status. Based on changes in health status, the data collection unit can also collect ingredients and recipes that are rich in specific nutrients. This allows for the suggestion of health-conscious menus by updating information in response to changes in the family's health status.

[0086] The data collection unit can estimate the user's emotions and adjust the level of detail of the information it collects based on the estimated emotions. For example, if the user is relaxed, the data collection unit can collect information on detailed recipes and cooking methods. If the user is in a hurry, the data collection unit can also collect information on easy and quick recipes. If the user is excited, the data collection unit can also collect information on new ingredients and recipes. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. This allows for the provision of more relevant information by adjusting the level of detail of the information based on the user's emotions.

[0087] The data collection unit can collect not only weather information but also seasonal and local event information and incorporate it into menu suggestions. For example, the data collection unit can collect information on seasonal ingredients and dishes and incorporate it into menu suggestions. The data collection unit can also collect information on ingredients and dishes related to local events and festivals and incorporate it into menu suggestions. The data collection unit can also collect information on seasonal ingredients and dishes based on weather information and incorporate it into menu suggestions. As a result, by collecting seasonal and local event information, a wider variety of menu suggestions become possible.

[0088] The data collection unit can improve accuracy by referring to past meal history when collecting family members' dietary preferences and allergy information. For example, the data collection unit analyzes the family's past meal history to collect preferences and allergy information. Based on past meal history, the data collection unit can also collect ingredients and recipes that match the family's preferences. The data collection unit can also accurately collect allergy information by referring to past meal history and reflect it in menu suggestions. This makes it possible to collect more accurate information by referring to past meal history.

[0089] The analytics unit can estimate the user's emotions and adjust the menu suggestion method based on the estimated emotions. For example, if the user is relaxed, the analytics unit can suggest detailed recipes and cooking methods. If the user is in a hurry, the analytics unit can also suggest simple, quick recipes. If the user is excited, the analytics unit can also suggest new ingredients and recipes. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. This allows for more appropriate menu suggestions by adjusting the menu suggestion method based on the user's emotions.

[0090] The analysis department can propose menus that are optimal for a person's health condition, taking into account the nutritional value of ingredients. For example, the analysis department can analyze the nutritional value of ingredients and propose balanced meal menus. The analysis department can also propose menus that use ingredients rich in specific nutrients. The analysis department can also propose menus that use highly nutritious ingredients according to a person's health condition. In this way, by considering the nutritional value of ingredients, it becomes possible to propose health-conscious menus.

[0091] The analysis department can suggest multiple menu options based on family dietary preferences and allergy information. For example, the analysis department can suggest multiple menu options considering family preferences and allergy information. The analysis department can also suggest menu options using ingredients that suit family preferences. Furthermore, based on allergy information, the analysis department can suggest menu options using safe ingredients. This allows for the suggestion of a wider variety of menu options by considering family preferences and allergy information.

[0092] The analytics unit can estimate the user's emotions and increase the menu variations based on those emotions. For example, if the user is relaxed, the analytics unit can suggest a variety of menu options. If the user is in a hurry, the analytics unit can suggest simple, quick-to-prepare menu options. If the user is excited, the analytics unit can suggest menu options incorporating new ingredients or recipes. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. This allows for more diverse menu suggestions by increasing menu variations based on the user's emotions.

[0093] The analysis department can suggest seasonal menus based on weather information. For example, in winter, the analysis department can suggest warm soups and stews. In summer, it can suggest cold salads and light meals. On rainy days, the analysis department can suggest meals that can be enjoyed at home. In this way, by suggesting menus based on weather information, it becomes possible to have meals that are appropriate for the season.

[0094] The analysis department can analyze your daily eating habits and suggest a balanced diet. For example, the analysis department can analyze your daily eating habits and suggest a nutritionally balanced menu. The analysis department can also suggest menus to improve unbalanced eating habits. Based on your daily eating habits, the analysis department can suggest healthy meal menus. In this way, by analyzing your daily eating habits, it can suggest a nutritionally balanced diet.

[0095] The list generation unit can estimate the user's emotions and determine the priority of the shopping list based on those emotions. For example, if the user is feeling stressed, the list generation unit will prioritize adding relaxing ingredients to the list. If the user is tired, the list generation unit can also prioritize adding easy-to-cook ingredients to the list. If the user is health-conscious, the list generation unit can also prioritize adding nutritious ingredients to the list. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. This allows for more appropriate shopping by prioritizing the shopping list based on the user's emotions.

[0096] The list generation unit can monitor the inventory status of food items in the refrigerator in real time and add only the necessary items to the list. For example, the list generation unit can monitor the inventory status of food items in the refrigerator in real time and add only the necessary items to the list. The list generation unit can also prioritize adding items with low inventory to the list. The list generation unit can also add necessary items to the list considering the expiration dates of the food items in the refrigerator. This allows for efficient shopping by monitoring the inventory status of food items in the refrigerator in real time.

[0097] The list generation unit can generate cost-effective shopping lists by taking sale information into consideration. For example, the list generation unit can add cost-effective ingredients to the list based on sale information. The list generation unit can also optimize the shopping list within a budget by taking sale information into consideration. The list generation unit can also suggest bulk purchases based on sale information and generate an economical shopping list. In this way, economical shopping becomes possible by taking sale information into consideration.

[0098] The list generation unit can estimate the user's emotions and adjust the level of detail in the shopping list based on the estimated emotions. For example, if the user is relaxed, the list generation unit will generate a detailed shopping list. If the user is in a hurry, the list generation unit can also generate a concise shopping list. If the user is excited, the list generation unit can also generate a shopping list that includes information on new ingredients or recipes. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. This allows for the provision of more appropriate shopping lists by adjusting the level of detail in the shopping list based on the user's emotions.

[0099] The list generation unit can prioritize adding environmentally friendly eco-products to the shopping list. For example, the list generation unit will prioritize adding environmentally friendly eco-products to the list. The list generation unit can also optimize the shopping list based on special sale information for eco-products. The list generation unit can also provide an option to add only environmentally friendly products to the list. This makes environmentally friendly shopping possible by prioritizing the addition of environmentally friendly eco-products.

[0100] The list generation unit can add new and recommended products to the shopping list based on the user's preferences. For example, the list generation unit can add new products to the list based on the user's preferences. The list generation unit can also add recommended products to the list based on the user's past purchase history. The list generation unit can also provide an option to add new and recommended products that match the user's preferences to the list. This allows for a more satisfying shopping experience by adding new and recommended products tailored to the user's preferences.

[0101] The special offer information display unit can estimate the user's emotions and adjust how special offer information is displayed based on those emotions. For example, if the user is relaxed, the special offer information display unit will display detailed special offer information. If the user is in a hurry, the special offer information display unit can also display concise special offer information. If the user is excited, the special offer information display unit can also display visually appealing special offer information. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. This makes it possible to provide more effective special offer information by adjusting how special offer information is displayed based on the user's emotions.

[0102] The special sale information display unit can update special sale information in real time and reflect the latest information. For example, the special sale information display unit can acquire special sale information in real time and reflect the latest information. The special sale information display unit can also increase the frequency of special sale information updates and always provide the latest information. The special sale information display unit can also immediately reflect any changes to special sale information. As a result, by updating special sale information in real time, it is possible to always provide the latest information.

[0103] The special offer information section can suggest menus that offer high cost-saving benefits based on special offer information. For example, the special offer information section can suggest menus that offer good value for money based on special offer information. The special offer information section can also suggest the best menu within the budget, taking special offer information into consideration. The special offer information section can also suggest bulk purchases based on special offer information, and suggest economical menus. In this way, economical meals become possible by suggesting menus based on special offer information.

[0104] The special offer information display unit can estimate the user's emotions and prioritize special offer information based on those emotions. For example, if the user is relaxed, the special offer information display unit will prioritize displaying detailed special offer information. If the user is in a hurry, the special offer information display unit can also prioritize displaying concise special offer information. If the user is excited, the special offer information display unit can also prioritize displaying visually appealing special offer information. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. This makes it possible to provide more effective special offer information by prioritizing special offer information based on the user's emotions.

[0105] The special sale information reflection unit can reflect not only special sale information, but also coupon information and point reward information. For example, the special sale information reflection unit can reflect coupon information in addition to special sale information. The special sale information reflection unit can also reflect point reward information in addition to special sale information. The special sale information reflection unit can also generate an optimal shopping list based on coupon information and point reward information. This makes it possible to shop more economically by reflecting coupon information and point reward information.

[0106] The special sale information display unit can support economical shopping by suggesting bulk purchases based on special sale information. For example, the special sale information display unit can suggest bulk purchases based on special sale information. It can also present the cost-saving effects of bulk purchases. Based on the bulk purchase suggestions, the special sale information display unit can generate an economical shopping list. This makes economical shopping possible by suggesting bulk purchases.

[0107] The support unit can estimate the user's emotions and adjust the cooking instructions based on those emotions. For example, if the user is relaxed, the support unit can provide detailed cooking instructions. If the user is in a hurry, the support unit can provide concise instructions. If the user is excited, the support unit can provide visually appealing cooking instructions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. This allows for more effective cooking assistance by adjusting the cooking instructions based on the user's emotions.

[0108] The support unit can monitor the progress of cooking in real time and provide appropriate instructions for the next steps. For example, the support unit can monitor the progress of cooking in real time and provide appropriate instructions for the next steps. The support unit can also provide timely instructions for the next steps according to the progress of cooking. The support unit can also optimize cooking time based on the progress of cooking. As a result, cooking efficiency is improved by monitoring the progress of cooking in real time.

[0109] The support department can also offer suggestions on how to use and maintain cooking utensils as part of its support for the cooking process. For example, the support department can suggest how to use cooking utensils. The support department can also suggest how to maintain cooking utensils. The support department can also provide videos demonstrating how to use and maintain cooking utensils. By offering suggestions on how to use and maintain cooking utensils, the efficiency of cooking can be improved.

[0110] The support unit can estimate the user's emotions and customize the cooking process support based on those emotions. For example, if the user is relaxed, the support unit can provide detailed cooking instructions. If the user is in a hurry, the support unit can provide concise instructions. If the user is excited, the support unit can provide visually appealing cooking instructions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. This allows for more effective cooking assistance by customizing the cooking process support based on the user's emotions.

[0111] The support department can propose cooking methods that allow the whole family to participate in the cooking process. For example, the support department can propose cooking methods that allow the whole family to participate. The support department can also propose cooking methods that the whole family can enjoy. The support department can also propose recipes that allow the whole family to cook together. By proposing cooking methods that allow the whole family to participate, it becomes possible to make cooking enjoyable for the whole family.

[0112] The support department can also offer suggestions for time-saving recipes and single-dish meals as part of its assistance with the cooking process. For example, the support department can suggest time-saving recipes. The support department can also suggest single-dish meal recipes. Based on the suggestions for time-saving recipes and single-dish meals, the support department can optimize cooking time. In this way, the efficiency of cooking is improved by suggesting time-saving recipes and single-dish meals.

[0113] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0114] The data collection unit can estimate the user's emotions and prioritize the information to collect based on those emotions. For example, if the user is stressed, it will prioritize collecting information related to relaxing meal menus. If the user is tired, it can also prioritize collecting information on easy-to-cook ingredients and recipes. If the user is health-conscious, it can also prioritize collecting information on nutritious ingredients and healthy recipes. By prioritizing information based on the user's emotions, more relevant information can be collected.

[0115] The data collection unit can identify ingredients that should be used first, taking into account their expiration dates. For example, it can monitor the expiration dates of ingredients in the refrigerator in real time and suggest menus that prioritize the use of ingredients nearing their expiration date. It can also list ingredients that are nearing their expiration date and collect recipes that use them. It can also suggest cooking and storage methods for ingredients that are nearing their expiration date. In this way, food waste can be reduced by considering the expiration dates of ingredients in the refrigerator.

[0116] The data collection unit can monitor changes in family health in real time and update the collected information as needed. For example, it can monitor family health and collect suitable ingredients and recipes when symptoms of a cold or allergy appear. It can also collect information to suggest nutritionally balanced meal menus in response to changes in health. Based on changes in health, it can also collect ingredients and recipes that are rich in specific nutrients. This allows for health-conscious menu suggestions by updating information in response to changes in family health.

[0117] The data collection unit can estimate the user's emotions and adjust the level of detail of the information collected based on those emotions. For example, if the user is relaxed, it can collect information on detailed recipes and cooking methods. If the user is in a hurry, it can collect information on easy, quick recipes. If the user is excited, it can collect information on new ingredients and recipes. By adjusting the level of detail of information based on the user's emotions, it can provide more relevant information.

[0118] The data collection unit can gather not only weather information but also seasonal and local event information and incorporate it into menu suggestions. For example, it can collect information on seasonal ingredients and dishes and incorporate it into menu suggestions. It can also collect information on ingredients and dishes related to local events and festivals and incorporate it into menu suggestions. Based on weather information, it can also collect information on seasonal ingredients and dishes and incorporate it into menu suggestions. In this way, by collecting seasonal and local event information, a wider variety of menu suggestions becomes possible.

[0119] The analytics department can estimate the user's emotions and adjust the menu suggestion method based on those estimates. For example, if the user is relaxed, it can suggest detailed recipes and cooking methods. If the user is in a hurry, it can suggest simple, quick recipes. If the user is excited, it can suggest new ingredients and recipes. By adjusting the menu suggestion method based on the user's emotions, more appropriate menu suggestions become possible.

[0120] The analysis department can propose menus that are optimal for a person's health condition, taking into account the nutritional value of ingredients. For example, it can analyze the nutritional value of ingredients and propose balanced meal menus. It can also propose menus using ingredients that are rich in specific nutrients. Depending on the person's health condition, it can also propose menus using highly nutritious ingredients. In this way, by considering the nutritional value of ingredients, it becomes possible to propose health-conscious menus.

[0121] The analysis department can suggest multiple menu options based on family dietary preferences and allergy information. For example, it can suggest multiple menu options considering family preferences and allergy information. It can also suggest menu options using ingredients that suit family preferences. Based on allergy information, it can also suggest menu options using safe ingredients. In this way, by considering family preferences and allergy information, a wider variety of menu options can be suggested.

[0122] The analytics department can estimate the user's emotions and increase the menu variations based on those estimates. For example, if the user is relaxed, it can suggest a variety of menu options. If the user is in a hurry, it can suggest simple, quick-to-prepare menus. If the user is excited, it can suggest menus incorporating new ingredients or recipes. By increasing menu variations based on the user's emotions, a more diverse range of menu suggestions becomes possible.

[0123] The analysis department can suggest seasonal menus based on weather information. For example, in winter, it can suggest warm soups and stews. In summer, it can suggest cold salads and light meals. On rainy days, it can suggest dishes that can be enjoyed at home. In this way, by suggesting menus based on weather information, it becomes possible to have meals that are appropriate for the season.

[0124] The following briefly describes the processing flow for example form 2.

[0125] Step 1: The data collection unit gathers information such as family members' food preferences and allergy information, weather, daily meal content, health status, and the contents of the refrigerator. For example, in addition to collecting information entered by the user, it can also use sensors to monitor the contents of the refrigerator in real time. It can also obtain weather information using an internet connection. Step 2: The analysis unit analyzes the information collected by the collection unit and proposes the optimal menu. For example, it can propose a menu based on information entered by the user, or it can use AI to analyze the collected information and propose the optimal menu. Step 3: The list generation unit generates a shopping list based on the menu suggested by the analysis unit. For example, it can list the ingredients needed for the suggested menu, or it can generate a shopping list based on sale information. Step 4: The special offer information reflection unit reflects the special offer information in the shopping list generated by the list generation unit. For example, it can optimize the shopping list based on the special offer information, or it can update the special offer information in real time to reflect the latest information. Step 5: The support unit assists with the cooking process based on the shopping list generated by the list generation unit. For example, it provides real-time instructions on "what to do next" during cooking, and suggests methods to streamline common preparation steps, such as cutting vegetables all at once or seasoning meat all at once.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] Each of the multiple elements described above, including the data collection unit, analysis unit, list generation unit, special offer information reflection unit, and support unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit collects information using the sensors and internet connection of the smart device 14 and analyzes the collected information by the specific processing unit 290 of the data processing unit 12. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and proposes an optimal menu based on the collected information. The list generation unit is implemented in the specific processing unit 290 of the data processing unit 12 and generates a shopping list based on the proposed menu. The special offer information reflection unit is implemented in the specific processing unit 290 of the data processing unit 12 and reflects special offer information in the shopping list. The support unit is implemented in the control unit 46A of the smart device 14 and supports the cooking process. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0130] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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).

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.).

[0142] 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.

[0143] 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.

[0144] 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.

[0145] Each of the multiple elements described above, including the data collection unit, analysis unit, list generation unit, special offer information reflection unit, and support unit, is implemented, for example, by at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit collects information using the sensors and internet connection of the smart glasses 214 and analyzes the collected information by the specific processing unit 290 of the data processing unit 12. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and proposes an optimal menu based on the collected information. The list generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and generates a shopping list based on the proposed menu. The special offer information reflection unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and reflects special offer information in the shopping list. The support unit is implemented, for example, by the control unit 46A of the smart glasses 214 and assists in the cooking process. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0146] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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).

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.).

[0158] 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.

[0159] 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.

[0160] 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.

[0161] Each of the multiple elements described above, including the data collection unit, analysis unit, list generation unit, special offer information reflection unit, and support unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit collects information using the sensors and internet connection of the headset terminal 314 and analyzes the collected information by the specific processing unit 290 of the data processing unit 12. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and proposes an optimal menu based on the collected information. The list generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and generates a shopping list based on the proposed menu. The special offer information reflection unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and reflects special offer information in the shopping list. The support unit is implemented by, for example, the control unit 46A of the headset terminal 314 and supports the cooking process. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0162] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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).

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.).

[0175] 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.

[0176] 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.

[0177] 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.

[0178] Each of the multiple elements described above, including the collection unit, analysis unit, list generation unit, special offer information reflection unit, and support unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects information using the robot 414's sensors and internet connection, and analyzes the collected information by the specific processing unit 290 of the data processing unit 12. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and proposes an optimal menu based on the collected information. The list generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and generates a shopping list based on the proposed menu. The special offer information reflection unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and reflects special offer information in the shopping list. The support unit is implemented, for example, by the control unit 46A of the robot 414, and assists in the cooking process. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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."

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] (Note 1) The data collection unit gathers information such as family members' food preferences and allergy information, weather, daily meals, health status, and ingredients in the refrigerator. The analysis unit analyzes the information collected by the aforementioned collection unit and proposes the optimal menu, A list generation unit generates a shopping list based on the menu proposed by the analysis unit, A special sale information reflection unit that reflects special sale information in the shopping list generated by the list generation unit, The system includes a support unit that assists in the cooking process based on the shopping list generated by the list generation unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is Real-time information on the contents of the refrigerator. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned collection unit is Obtain weather information using an internet connection. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned analysis unit is The system suggests the most suitable menu based on the information entered by the user. The system described in Appendix 1, characterized by the features described herein. (Note 5) The list generation unit, Generate a shopping list based on sale information. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned support unit, It provides real-time instructions on "what to do next" while cooking. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned support unit, To improve the efficiency of common preparation tasks, we propose methods such as cutting vegetables all at once and seasoning meat all at once. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is It estimates the user's emotions and prioritizes the information to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is Considering the expiration dates of the food items in the refrigerator, identify which items should be used first. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is Monitor changes in your family's health in real time and update the information you collect as needed. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is It estimates the user's emotions and adjusts the level of detail of the information collected based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is In addition to weather information, we also collect seasonal and local event information and incorporate it into our menu suggestions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned collection unit is When collecting information on family members' dietary preferences and allergies, we improve accuracy by referring to their past eating history. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit is It estimates the user's emotions and adjusts the menu suggestion method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit is We propose menus that are optimal for your health condition, taking into account the nutritional value of the ingredients. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit is Based on your family's dietary preferences and allergy information, we will suggest multiple menu options. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit is It estimates the user's emotions and increases the menu variations based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit is Based on weather information, we suggest menus suitable for the season. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit is We analyze your daily eating habits and propose a balanced diet. The system described in Appendix 1, characterized by the features described herein. (Note 20) The list generation unit, It estimates the user's emotions and prioritizes items on the shopping list based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The list generation unit, Monitor the real-time inventory status of food items in your refrigerator and add only the necessary items to your list. The system described in Appendix 1, characterized by the features described herein. (Note 22) The list generation unit, Considering special offers, generate a cost-effective shopping list. The system described in Appendix 1, characterized by the features described herein. (Note 23) The list generation unit, The system estimates the user's emotions and adjusts the level of detail in the shopping list based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The list generation unit, Prioritize adding environmentally friendly eco-products to your shopping list. The system described in Appendix 1, characterized by the features described herein. (Note 25) The list generation unit, Add new and recommended products tailored to the user's preferences to their shopping list. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned special sale information display unit is: The system estimates the user's emotions and adjusts how special offer information is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned special sale information display unit is: We update special sale information in real time to reflect the latest information. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned special sale information display unit is: Based on special offer information, we propose menus that offer high cost-saving benefits. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned special sale information display unit is: The system estimates user sentiment and prioritizes special offer information based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned special sale information display unit is: It reflects not only special sale information, but also coupon information and point reward information. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned special sale information display unit is: Based on special sale information, we offer suggestions for bulk purchases to support economical shopping. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned support unit, The system estimates the user's emotions and adjusts the cooking process instructions based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned support unit, During cooking, monitor the progress of the cooking process in real time and provide appropriate instructions for the next step. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned support unit, In providing support during the cooking process, we also offer suggestions on how to use and maintain cooking utensils. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned support unit, It estimates the user's emotions and customizes the support provided during the cooking process based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned support unit, In supporting the cooking process, we propose cooking methods that allow the whole family to participate. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned support unit, In supporting the cooking process, we also offer suggestions for time-saving recipes and single-dish meals. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0198] 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. The data collection unit gathers information such as family members' food preferences and allergy information, weather, daily meals, health status, and ingredients in the refrigerator. The analysis unit analyzes the information collected by the aforementioned collection unit and proposes the optimal menu, A list generation unit generates a shopping list based on the menu proposed by the analysis unit, A special sale information reflection unit that reflects special sale information in the shopping list generated by the list generation unit, The system includes a support unit that assists in the cooking process based on the shopping list generated by the list generation unit. A system characterized by the following features.

2. The aforementioned collection unit is Real-time information on the contents of the refrigerator. The system according to feature 1.

3. The aforementioned collection unit is Obtain weather information using an internet connection. The system according to feature 1.

4. The aforementioned analysis unit is The system suggests the most suitable menu based on the information entered by the user. The system according to feature 1.

5. The list generation unit, Generate a shopping list based on sale information. The system according to feature 1.

6. The aforementioned support unit, It provides real-time instructions on what to do next while cooking. The system according to feature 1.

7. The aforementioned support unit, To improve the efficiency of common preparation tasks, we propose methods such as cutting vegetables all at once and marinating meat all at once. The system according to feature 1.

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

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