system

Abstract

The system according to this embodiment aims to suggest the optimal menu and serving method, taking into account information about the ingredients in the refrigerator and the user's preferences. [Solution] The system according to the embodiment comprises a collection unit, a preference collection unit, a suggestion unit, and a plating suggestion unit. The collection unit collects information on ingredients in the refrigerator. The preference collection unit collects information on the user's preferences. The suggestion unit proposes an optimal menu based on the information collected by the collection unit and the preference collection unit. The plating suggestion unit proposes a plating method based on the menu proposed by the suggestion unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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] [[ID=2I]]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, an optimal menu and plating method considering the food ingredient information in the refrigerator and the user's preferences has not been sufficiently proposed, and there is room for improvement.

[0005] The system according to the embodiment aims to propose an optimal menu and plating method in consideration of the food ingredient information in the refrigerator and the user's preferences.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, a preference collection unit, a suggestion unit, and a plating suggestion unit. The collection unit collects information on ingredients in the refrigerator. The preference collection unit collects information on the user's preferences. The suggestion unit proposes an optimal menu based on the information collected by the collection unit and the preference collection unit. The plating suggestion unit proposes a plating method based on the menu proposed by the suggestion unit. [Effects of the Invention]

[0007] The system according to this embodiment can suggest the optimal menu and plating method, taking into account the information of the ingredients in the refrigerator and the user's preferences. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) An AI service according to an embodiment of the present invention is a system that proposes menus and plating methods that take into account the stock in the refrigerator and the user's preferences. This system collects information on ingredients in the refrigerator and information on the user's preferences. Based on this information, the AI ​​proposes an optimal menu. Furthermore, it also proposes plating methods based on the proposed menu. This mechanism allows the user to use up the ingredients in the refrigerator without waste and enjoy meals that suit their preferences. For example, when collecting information on ingredients in the refrigerator, cameras and sensors installed inside the refrigerator are used to grasp the types and quantities of ingredients. For example, information on vegetables, meat, and seasonings inside the refrigerator is collected. This allows the system to grasp the inventory status of ingredients in the refrigerator. Next, information on the user's preferences is collected. For example, the system collects information on the types of ingredients and dishes the user likes, allergy information, etc. This allows the system to propose menus that suit the user's preferences. Based on the collected information on ingredients in the refrigerator and the user's preferences, the AI ​​proposes an optimal menu. For example, it proposes dishes that suit the user's preferences, such as stir-fries using vegetables and meat in the refrigerator, or stews using fish. This allows the user to use up the ingredients in the refrigerator without waste. Furthermore, based on the suggested menu, the system also suggests plating methods. For example, it suggests arrangements for beautifully plating dishes and plating methods that take into account the colors of the ingredients. This allows users to enjoy meals that are visually appealing as well. This system allows users to use up all the ingredients in their refrigerator without waste and enjoy meals that suit their preferences. In addition, plating methods are suggested, so users can enjoy meals that are visually appealing. For example, if the system suggests a stir-fry using vegetables and meat from the refrigerator, it will suggest plating methods that balance the vegetables and meat and take color into consideration. This allows users to enjoy meals that are not only delicious but also visually appealing. In this way, the AI ​​service can use up all the ingredients in the refrigerator without waste and provide meals that suit the user's preferences.

[0029] The AI ​​service according to this embodiment comprises a collection unit, a preference collection unit, a suggestion unit, and a serving suggestion unit. The collection unit collects information about ingredients in the refrigerator. The collection unit collects information about the types and quantities of ingredients using, for example, cameras and sensors installed inside the refrigerator. For example, the collection unit can collect information about vegetables, meat, seasonings, etc., inside the refrigerator. The collection unit can also periodically update the information to understand the inventory status of ingredients inside the refrigerator. The preference collection unit collects information about the user's preferences. The preference collection unit collects information about the user's preferences through, for example, an app. For example, the preference collection unit can collect information about the types of ingredients and dishes that the user likes, allergy information, etc. The preference collection unit can also periodically update the user's preference information. The suggestion unit suggests an optimal menu based on the information collected by the collection unit and the preference collection unit. The suggestion unit suggests an optimal menu using, for example, an algorithm that considers ingredient combinations and nutritional balance. For example, the suggestion unit can suggest dishes that suit the user's preferences, such as stir-fries using vegetables and meat inside the refrigerator, or stews using fish. Furthermore, the suggestion unit can also propose menus that take nutritional balance into consideration. The plating suggestion unit proposes plating methods based on the menu proposed by the suggestion unit. For example, the plating suggestion unit can propose plating methods that take into consideration color and arrangement. For example, the plating suggestion unit can propose plating methods that take into consideration the arrangement for beautifully plating the food and the colors of the ingredients. The plating suggestion unit can also propose plating methods that are visually appealing. As a result, the AI ​​service according to the embodiment can use up the ingredients in the refrigerator without waste and provide meals that suit the user's preferences.

[0030] The data collection unit collects information about food items inside the refrigerator. For example, it uses cameras and sensors installed inside the refrigerator to collect information on the type and quantity of food items. Specifically, cameras periodically photograph shelves and drawers inside the refrigerator, and use image recognition technology to identify the type and quantity of food items. Sensors, such as weight sensors and RFID tags, provide real-time information on food inventory. For example, weight sensors are installed on each shelf and drawer to measure the weight of food items and determine inventory levels. RFID tags are attached to food items, and a reader inside the refrigerator reads the tags to identify the type of food item and its expiration date. This allows the data collection unit to accurately and efficiently collect information about food items inside the refrigerator and understand inventory levels in real time. Furthermore, the data collection unit can transmit the collected data to a cloud server and share it with other departments. This allows the data collection unit to centrally manage information about food items inside the refrigerator and collaborate with other systems and departments as needed. For example, the collected data can be made accessible to the proposal department and the food presentation proposal department. Additionally, the data collection unit can adjust the frequency and accuracy of data collection to provide flexible responses to specific situations and conditions. This allows the data collection unit to collect data efficiently and effectively, improving the overall performance of the system.

[0031] The Preference Collection Department collects user preference information. For example, it collects user preference information through the app. Specifically, it collects data such as food and dish preferences, allergy information, and meal frequency and timing that users input into the app. The app has a function to analyze preference trends based on dishes and ratings previously selected by the user. For example, if a user frequently selects a particular dish, it determines that the user likes that dish and reflects this in their preference information. The app also has a function that allows users to upload photos of their meals, and image recognition technology can be used to identify the type of dish and ingredients, adding them to the preference information. Furthermore, the Preference Collection Department can periodically update user preference information. For example, if a user tries a new dish or their preferences change, the department collects this information through the app and updates the preference information. This allows the Preference Collection Department to always know the latest user preference information and provide it to the Suggestion Department and the Plating Suggestion Department. The Preference Collection Department can store the collected preference information on a cloud server and share it with other departments. This allows the preference collection unit to centrally manage user preference information and collaborate with other systems and departments as needed. For example, the collected preference information can be made accessible to the proposal department. Furthermore, the preference collection unit can adjust the frequency and accuracy of data collection, enabling flexible responses to specific situations and conditions. As a result, the preference collection unit can collect data efficiently and effectively, improving the overall system performance.

[0032] The suggestion department proposes the optimal menu based on information collected by the data collection department and the preference collection department. For example, the suggestion department proposes the optimal menu using algorithms that consider ingredient combinations and nutritional balance. Specifically, it analyzes the collected ingredient information and preference information using AI to generate a menu that matches the user's preferences and nutritional balance. For example, it can suggest dishes that match the user's preferences, such as stir-fries using vegetables and meat in the refrigerator, or stews using fish. The AI ​​considers the nutritional value, calories, and cooking methods of ingredients to generate a balanced menu. In addition, the suggestion department can propose new menus based on the user's eating history and changes in preferences, while referring to previously suggested menus. Furthermore, the suggestion department can also propose menus at the appropriate time, considering the user's meal frequency and timing. For example, it can suggest a menu suitable for breakfast, based on the time the user eats breakfast. The suggestion department can also propose menus that cater to situations where the user wants to use up a specific ingredient or consume a specific nutrient. In this way, the suggestion department can propose the optimal menu that takes into account the user's preferences and nutritional balance, supporting the user's eating habits. The proposal department can save the generated menus to a cloud server and share them with other departments. This allows the proposal department to centrally manage the generated menus and collaborate with other systems and departments as needed. For example, the generated menus can be made accessible to the plating proposal department. Furthermore, the proposal department can adjust the frequency and accuracy of data analysis to respond flexibly to specific situations and conditions. This enables the proposal department to propose menus efficiently and effectively, improving the overall performance of the system.

[0033] The plating suggestion department proposes plating methods based on the menu proposed by the proposal department. For example, the plating suggestion department proposes plating methods that take into account color and arrangement. Specifically, it uses AI to analyze the characteristics of the ingredients and dishes in the proposed menu and generates visually appealing plating methods. For example, it considers the color and shape of the dishes and proposes the arrangement of ingredients and the order of plating. The AI ​​can learn from past plating data and food photos to generate beautiful plating patterns. The plating suggestion department can also propose plating methods that suit the user's preferences and dining occasion. For example, it can propose elaborate plating for special events and parties, or simple and beautiful plating for everyday meals. Furthermore, the plating suggestion department can also provide users with procedures and tips for actually plating the food. For example, it can provide specific advice on the order in which to plate the ingredients and how to arrange them. In this way, the plating suggestion department can support users in beautifully plating their food and enhance the enjoyment of meals. The plating suggestion department can save the generated plating methods to a cloud server and share them with other departments. This allows the plating suggestion department to centrally manage the generated plating methods and collaborate with other systems and departments as needed. For example, the generated plating methods can be made accessible to the suggestion department. Furthermore, the plating suggestion department can adjust the frequency and accuracy of data analysis to enable flexible responses to specific situations and conditions. As a result, the plating suggestion department can propose plating methods efficiently and effectively, improving the overall performance of the system.

[0034] The collection unit can collect information on the type and quantity of food items using cameras and sensors installed inside the refrigerator. For example, the collection unit can collect information on the type and quantity of food items as image data using cameras installed inside the refrigerator. The collection unit can also collect information on the weight and quantity of food items as data using sensors installed inside the refrigerator. For example, the collection unit can input image data captured by cameras inside the refrigerator into a generating AI to analyze the type and quantity of food items. This allows the collection unit to accurately collect information on food items inside the refrigerator. Some or all of the above-described processes in the collection unit may be performed using AI, or without AI. For example, the collection unit can input image data captured by cameras inside the refrigerator into a generating AI to analyze the type and quantity of food items.

[0035] The preference collection unit can collect user preference information through an application. For example, the preference collection unit can collect user preference information using a smartphone application. For example, the preference collection unit can collect information such as the types of ingredients and dishes that users like, and allergy information, in the form of a questionnaire. For example, the preference collection unit can store user preference information in a database and update it periodically. For example, the preference collection unit can input user preference information into a generating AI and perform analysis of the preference information. This allows the preference collection unit to efficiently collect user preference information. Some or all of the above processing in the preference collection unit may be performed using AI, for example, or without using AI. For example, the preference collection unit can input user preference information into a generating AI and perform analysis of the preference information.

[0036] The suggestion unit can propose the optimal menu using an algorithm that considers ingredient combinations and nutritional balance. The suggestion unit proposes the optimal menu based on information collected by the data collection unit and the preference collection unit, for example. The suggestion unit can propose dishes that suit the user's preferences, such as stir-fries using vegetables and meat in the refrigerator, or stews using fish, using an algorithm that considers ingredient combinations and nutritional balance. The suggestion unit can also propose menus that consider nutritional balance, for example. The suggestion unit can analyze the collected information and propose the optimal menu using a generation AI, for example. This allows the suggestion unit to propose the optimal menu that considers nutritional balance. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the collected information into a generation AI and propose the optimal menu.

[0037] The plating suggestion unit can propose plating methods that take into account color and arrangement. For example, based on the menu proposed by the suggestion unit, the plating suggestion unit proposes arrangements for beautifully plating dishes and plating methods that take into account the color of the ingredients. For example, the plating suggestion unit can propose visually appealing plating methods using color theory. For example, the plating suggestion unit can analyze the collected information using generative AI and propose the optimal plating method. In this way, the plating suggestion unit can propose visually appealing plating methods. Some or all of the above processing in the plating suggestion unit may be performed using AI, for example, or without AI. For example, the plating suggestion unit can input the collected information into generative AI and propose the optimal plating method.

[0038] The collection unit can monitor the freshness of ingredients in real time and suggest using ingredients that have lost their freshness as a priority. The collection unit periodically checks the freshness of ingredients using, for example, cameras and sensors inside the refrigerator. If the collection unit detects ingredients that have lost their freshness, it can notify the user and suggest using them as a priority. The collection unit can also automatically list ingredients that have lost their freshness and reflect this in menu suggestions. This allows the collection unit to reduce food waste by prioritizing the use of ingredients that have lost their freshness. Some or all of the above processing in the collection unit may be performed using, for example, AI, or not using AI. For example, the collection unit can input image data captured by a camera inside the refrigerator into a generating AI to analyze the freshness of the ingredients.

[0039] The collection unit can automatically recognize the expiration dates of ingredients and prioritize suggesting ingredients that are nearing their expiration date. For example, the collection unit can read the expiration date printed on the ingredient packaging using a camera and store it in a database. For example, if the collection unit detects ingredients that are nearing their expiration date, it can notify the user and suggest using them preferentially. The collection unit can also automatically list ingredients that are nearing their expiration date and reflect this in menu suggestions. This allows the collection unit to reduce food waste by prioritizing the use of ingredients that are nearing their expiration date. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can read the expiration date printed on the ingredient packaging using a camera, input it into a generating AI, and analyze the expiration date.

[0040] The collection unit can optimize the placement of food items in the refrigerator, enabling efficient food collection. For example, the collection unit optimizes the placement of food items in the refrigerator based on the type of food item and its frequency of use. For example, the collection unit can notify the user when changing the placement of food items and suggest an efficient collection method. For example, the collection unit can periodically review the placement of food items to maintain the optimal placement. In this way, the collection unit can efficiently collect food items by optimizing the placement of food items in the refrigerator. Some or all of the above processes in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input data on the placement of food items in the refrigerator into a generating AI and suggest an optimal placement.

[0041] The collection unit can automatically calculate the nutritional value of ingredients and collect them while considering nutritional balance. For example, the collection unit can obtain the nutritional value of ingredients from a database and calculate it automatically. For example, the collection unit can determine the priority of ingredients to collect while considering nutritional balance. For example, if the nutritional balance is skewed, the collection unit can notify the user and suggest a balanced collection method. This enables the collection unit to collect ingredients while considering nutritional balance. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input the nutritional value data of ingredients into a generating AI and analyze the nutritional balance.

[0042] The preference collection unit can analyze the user's past eating history, detect changes in preferences, and update the information it collects. For example, the preference collection unit can retrieve the user's past eating history from a database and analyze changes in preferences. For example, if the preference collection unit detects a change in preferences, it can automatically update the information it collects. For example, the preference collection unit can also collect new preference information based on changes in preferences. In this way, the preference collection unit can always collect the latest preference information by updating the information it collects in accordance with changes in the user's preferences. Some or all of the above processing in the preference collection unit may be performed using AI, for example, or without AI. For example, the preference collection unit can input the user's past eating history data into a generating AI and analyze changes in preferences.

[0043] The preference collection unit can monitor the user's health status and collect preference information corresponding to that health status. For example, the preference collection unit can acquire and monitor the user's health status from the device. For example, the preference collection unit can adjust the method of collecting preference information according to the health status. For example, the preference collection unit can also collect new preference information if the health status changes. As a result, the preference collection unit can suggest healthier menus by collecting preference information according to the user's health status. Some or all of the above processing in the preference collection unit may be performed using AI, for example, or without AI. For example, the preference collection unit can input the user's health data into a generating AI and analyze preference information according to the health status.

[0044] The preference collection unit can also collect preference information of the user's family and roommates and propose a menu that satisfies everyone. For example, the preference collection unit can collect preference information of the user's family and roommates through the app. For example, the preference collection unit can store the preference information of family and roommates in a database and reflect it in menu suggestions. For example, the preference collection unit can integrate and analyze preference information in order to propose a menu that satisfies everyone. In this way, the preference collection unit can propose a menu that satisfies everyone by collecting preference information of family and roommates. Some or all of the above processing in the preference collection unit may be performed using AI, for example, or without AI. For example, the preference collection unit can input the preference information of family and roommates into a generating AI and propose a menu that satisfies everyone.

[0045] The preference collection unit can collect preference information based on the user's meal times and frequency. For example, the preference collection unit can obtain the user's meal times and frequency from the device and reflect this in the collection of preference information. For example, the preference collection unit can determine the priority of preference information based on meal times and frequency. For example, the preference collection unit can also collect new preference information if meal times or frequency change. This allows the preference collection unit to collect more appropriate preference information by collecting preference information based on the user's meal times and frequency. Some or all of the above processing in the preference collection unit may be performed using AI, for example, or without AI. For example, the preference collection unit can input the user's meal times and frequency data into a generating AI and analyze the preference information.

[0046] The suggestion department can propose menus that are appropriate for the season and weather, allowing users to enjoy the seasonal atmosphere. For example, the suggestion department can propose menus using seasonal ingredients. For example, the suggestion department can propose hot or cold dishes depending on the weather. For example, the suggestion department can propose menus that are tailored to seasonal events, allowing users to enjoy the seasonal atmosphere. In this way, the suggestion department can allow users to enjoy the seasonal atmosphere by proposing menus that are appropriate for the season and weather. Some or all of the above processing in the suggestion department may be performed using AI, for example, or without AI. For example, the suggestion department can input seasonal and weather data into a generating AI and propose menus that take the seasonal atmosphere into consideration.

[0047] The suggestion unit can propose menus tailored to the user's dietary goals (e.g., weight loss, muscle building). For example, the suggestion unit can propose a low-calorie menu based on the user's weight loss goals. For example, the suggestion unit can propose a high-protein menu based on the user's muscle building goals. For example, the suggestion unit can propose a balanced menu based on the user's health maintenance goals. In this way, the suggestion unit can provide meals that meet the user's goals by proposing menus tailored to their objectives. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's dietary goal data into a generating AI and propose a menu tailored to those goals.

[0048] The suggestion unit can propose regionally specific menus, taking into account the user's cultural background and eating habits. For example, the suggestion unit can suggest regionally specific dishes based on the user's cultural background. For example, the suggestion unit can suggest menus that suit the user's usual meals, taking into account the user's eating habits. For example, the suggestion unit can also suggest menus using regionally specific ingredients to suit the user's preferences. In this way, the suggestion unit can allow users to enjoy regionally specific meals by suggesting menus that take into account their cultural background and eating habits. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's cultural background and eating habit data into a generating AI and propose regionally specific menus.

[0049] The suggestion unit can propose safe menus by taking into account the user's food allergy information. For example, the suggestion unit can retrieve the user's allergy information from a database and propose a menu that does not contain allergenic ingredients. For example, the suggestion unit can propose a menu that uses alternative ingredients to avoid allergenic ingredients. For example, the suggestion unit can propose a menu that uses safe ingredients based on the user's allergy information. In this way, the suggestion unit can propose safe menus by taking into account the user's food allergy information. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's allergy information data into a generating AI and propose a safe menu.

[0050] The plating suggestion unit can propose visually appealing plating arrangements by considering the shape and color of the ingredients. For example, the plating suggestion unit can propose a balanced arrangement by considering the shape of the ingredients. For example, the plating suggestion unit can propose a visually appealing plating arrangement by considering the color of the ingredients. For example, the plating suggestion unit can propose a visually appealing plating arrangement by combining the shape and color of the ingredients. In this way, the plating suggestion unit can propose visually appealing plating arrangements by considering the shape and color of the ingredients. Some or all of the above processing in the plating suggestion unit may be performed using AI, for example, or without AI. For example, the plating suggestion unit can input data on the shape and color of the ingredients into a generating AI and propose a visually appealing plating arrangement.

[0051] The plating suggestion unit can propose plating that enhances the dining atmosphere by considering the type and arrangement of tableware. For example, the plating suggestion unit can propose the optimal plating method according to the type of tableware. For example, the plating suggestion unit can propose plating that enhances the dining atmosphere by considering the arrangement of tableware. For example, the plating suggestion unit can propose visually appealing plating by combining the type and arrangement of tableware. In this way, the plating suggestion unit can propose plating that enhances the dining atmosphere by considering the type and arrangement of tableware. Some or all of the above processing in the plating suggestion unit may be performed using AI, for example, or without AI. For example, the plating suggestion unit can input tableware type and arrangement data into a generating AI and propose plating that enhances the dining atmosphere.

[0052] The plating suggestion unit can propose a balanced plating arrangement, taking into account the nutritional value of the ingredients. For example, the plating suggestion unit can obtain the nutritional value of the ingredients from a database and propose a balanced plating arrangement. For example, the plating suggestion unit can propose the arrangement of ingredients, taking nutritional balance into consideration. For example, if the nutritional value is unbalanced, the plating suggestion unit can also propose a balanced plating method. In this way, the plating suggestion unit can propose a balanced plating arrangement by taking into account the nutritional value of the ingredients. Some or all of the above processing in the plating suggestion unit may be performed using AI, for example, or without using AI. For example, the plating suggestion unit can input nutritional value data of the ingredients into a generating AI and propose a balanced plating arrangement.

[0053] The plating suggestion unit can suggest plating options tailored to the user's dining occasion (e.g., party, everyday meal). For example, the plating suggestion unit can suggest a lavish plating for a party. For example, it can suggest a simple and practical plating for an everyday meal. For example, it can suggest a themed plating for a special event. In this way, the plating suggestion unit can provide more appropriate plating by suggesting plating options tailored to the user's dining occasion. Some or all of the above processing in the plating suggestion unit may be performed using AI, for example, or without AI. For example, the plating suggestion unit can input dining scene data into a generating AI and suggest plating options appropriate to the scene.

[0054] The database can periodically update the information within it to maintain the latest ingredient and preference information. For example, the database can periodically check the database and update the latest ingredient information. For example, the database can periodically update user preference information to maintain the latest information. The database can also automatically update the information within it to always maintain an up-to-date state. In this way, the database can always maintain the latest information by periodically updating the information within it. Some or all of the above processes in the database may be performed using AI, for example, or not using AI. For example, the database can input ingredient and preference information into a generating AI and update it periodically.

[0055] A database can set access permissions to protect user privacy. For example, a database can set access permissions to protect user privacy. For example, a database can encrypt and securely store user privacy information. A database can also periodically review access permissions to enhance privacy protection. Thus, a database can protect user privacy by setting access permissions. Some or all of the above processes in a database may be performed using AI, for example, or not. For example, a database can input access permission setting data into a generating AI and suggest optimal settings for privacy protection.

[0056] A database can integrate its information with other devices to achieve seamless information sharing. For example, a database can integrate its information with a smartphone to achieve seamless information sharing. A database can integrate its information with a tablet to achieve seamless information sharing. A database can also integrate its information with a smartwatch to achieve seamless information sharing. In this way, a database enables seamless information sharing by integrating its information with other devices. Some or all of the above processing in a database may be performed using AI, for example, or without AI. For example, a database can input data for integration with other devices into a generating AI and propose the optimal method for achieving seamless information sharing.

[0057] A database can ensure data security by regularly backing up its database. For example, a database can regularly back up its database to ensure data security. For example, a database can encrypt and securely store backup data. A database can also regularly review backup data to maintain an up-to-date state. Thus, a database can ensure data security by regularly backing up its database. Some or all of the above processes in a database may be performed using AI, for example, or not. For example, a database can input backup data into a generating AI to suggest the optimal backup method.

[0058] The database can periodically update the information within it to maintain the latest ingredient and preference information. For example, the database can periodically check the database and update the latest ingredient information. For example, the database can periodically update user preference information to maintain the latest information. The database can also automatically update the information within it to always maintain an up-to-date state. In this way, the database can always maintain the latest information by periodically updating the information within it. Some or all of the above processes in the database may be performed using AI, for example, or not using AI. For example, the database can input ingredient and preference information into a generating AI and update it periodically.

[0059] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0060] The collection unit can monitor the freshness of ingredients in real time and suggest using ingredients that have lost their freshness as a priority. The collection unit periodically checks the freshness of ingredients using, for example, cameras and sensors inside the refrigerator. If the collection unit detects ingredients that have lost their freshness, it can notify the user and suggest using them as a priority. The collection unit can also automatically list ingredients that have lost their freshness and reflect this in menu suggestions. This allows the collection unit to reduce food waste by prioritizing the use of ingredients that have lost their freshness. Some or all of the above processing in the collection unit may be performed using, for example, AI, or not using AI. For example, the collection unit can input image data captured by a camera inside the refrigerator into a generating AI to analyze the freshness of the ingredients.

[0061] The suggestion department can propose menus that are appropriate for the season and weather, allowing users to enjoy the seasonal atmosphere. For example, the suggestion department can propose menus using seasonal ingredients. For example, the suggestion department can propose hot or cold dishes depending on the weather. For example, the suggestion department can propose menus that are tailored to seasonal events, allowing users to enjoy the seasonal atmosphere. In this way, the suggestion department can allow users to enjoy the seasonal atmosphere by proposing menus that are appropriate for the season and weather. Some or all of the above processing in the suggestion department may be performed using AI, for example, or without AI. For example, the suggestion department can input seasonal and weather data into a generating AI and propose menus that take the seasonal atmosphere into consideration.

[0062] The collection unit can automatically recognize the expiration dates of ingredients and prioritize suggesting ingredients that are nearing their expiration date. For example, the collection unit can read the expiration date printed on the ingredient packaging using a camera and store it in a database. For example, if the collection unit detects ingredients that are nearing their expiration date, it can notify the user and suggest using them preferentially. The collection unit can also automatically list ingredients that are nearing their expiration date and reflect this in menu suggestions. This allows the collection unit to reduce food waste by prioritizing the use of ingredients that are nearing their expiration date. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can read the expiration date printed on the ingredient packaging using a camera, input it into a generating AI, and analyze the expiration date.

[0063] The suggestion unit can propose menus tailored to the user's dietary goals (e.g., weight loss, muscle building). For example, the suggestion unit can propose a low-calorie menu based on the user's weight loss goals. For example, the suggestion unit can propose a high-protein menu based on the user's muscle building goals. For example, the suggestion unit can propose a balanced menu based on the user's health maintenance goals. In this way, the suggestion unit can provide meals that meet the user's goals by proposing menus tailored to their objectives. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's dietary goal data into a generating AI and propose a menu tailored to those goals.

[0064] The preference collection unit can also collect preference information of the user's family and roommates and propose a menu that satisfies everyone. For example, the preference collection unit can collect preference information of the user's family and roommates through the app. For example, the preference collection unit can store the preference information of family and roommates in a database and reflect it in menu suggestions. For example, the preference collection unit can integrate and analyze preference information in order to propose a menu that satisfies everyone. In this way, the preference collection unit can propose a menu that satisfies everyone by collecting preference information of family and roommates. Some or all of the above processing in the preference collection unit may be performed using AI, for example, or without AI. For example, the preference collection unit can input the preference information of family and roommates into a generating AI and propose a menu that satisfies everyone.

[0065] The suggestion unit can propose regionally specific menus, taking into account the user's cultural background and eating habits. For example, the suggestion unit can suggest regionally specific dishes based on the user's cultural background. For example, the suggestion unit can suggest menus that suit the user's usual meals, taking into account the user's eating habits. For example, the suggestion unit can also suggest menus using regionally specific ingredients to suit the user's preferences. In this way, the suggestion unit can allow users to enjoy regionally specific meals by suggesting menus that take into account their cultural background and eating habits. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's cultural background and eating habit data into a generating AI and propose regionally specific menus.

[0066] The plating suggestion unit can suggest plating options tailored to the user's dining occasion (e.g., party, everyday meal). For example, the plating suggestion unit can suggest a lavish plating for a party. For example, it can suggest a simple and practical plating for an everyday meal. For example, it can suggest a themed plating for a special event. In this way, the plating suggestion unit can provide more appropriate plating by suggesting plating options tailored to the user's dining occasion. Some or all of the above processing in the plating suggestion unit may be performed using AI, for example, or without AI. For example, the plating suggestion unit can input dining scene data into a generating AI and suggest plating options appropriate to the scene.

[0067] The following briefly describes the processing flow for example form 1.

[0068] Step 1: The collection unit collects information about the food items inside the refrigerator. The collection unit collects information about the types and quantities of food items, for example, using cameras or sensors installed inside the refrigerator. For example, the collection unit can collect information about vegetables, meat, condiments, etc., inside the refrigerator. The collection unit can also periodically update the information to understand the inventory status of food items inside the refrigerator. Step 2: The preference collection unit collects user preference information. The preference collection unit collects user preference information, for example, through the app. For example, the preference collection unit can collect information such as the types of ingredients and dishes that the user likes, and allergy information. The preference collection unit can also periodically update the user preference information. Step 3: The suggestion unit proposes the optimal menu based on the information collected by the data collection unit and the preference collection unit. The suggestion unit proposes the optimal menu using algorithms that consider, for example, ingredient combinations and nutritional balance. For example, the suggestion unit can suggest dishes that match the user's preferences, such as stir-fries using vegetables and meat from the refrigerator, or stews using fish. The suggestion unit can also propose menus that take nutritional balance into consideration. Step 4: The plating suggestion department proposes plating methods based on the menu proposed by the suggestion department. The plating suggestion department proposes plating methods that take into account, for example, the colors and arrangement. For example, the plating suggestion department can propose plating methods that take into account the arrangement for beautifully plating the food and the colors of the ingredients. The plating suggestion department can also propose plating methods that are visually appealing.

[0069] (Example of form 2) An AI service according to an embodiment of the present invention is a system that proposes menus and plating methods that take into account the stock in the refrigerator and the user's preferences. This system collects information on ingredients in the refrigerator and information on the user's preferences. Based on this information, the AI ​​proposes an optimal menu. Furthermore, it also proposes plating methods based on the proposed menu. This mechanism allows the user to use up the ingredients in the refrigerator without waste and enjoy meals that suit their preferences. For example, when collecting information on ingredients in the refrigerator, cameras and sensors installed inside the refrigerator are used to grasp the types and quantities of ingredients. For example, information on vegetables, meat, and seasonings inside the refrigerator is collected. This allows the system to grasp the inventory status of ingredients in the refrigerator. Next, information on the user's preferences is collected. For example, the system collects information on the types of ingredients and dishes the user likes, allergy information, etc. This allows the system to propose menus that suit the user's preferences. Based on the collected information on ingredients in the refrigerator and the user's preferences, the AI ​​proposes an optimal menu. For example, it proposes dishes that suit the user's preferences, such as stir-fries using vegetables and meat in the refrigerator, or stews using fish. This allows the user to use up the ingredients in the refrigerator without waste. Furthermore, based on the suggested menu, the system also suggests plating methods. For example, it suggests arrangements for beautifully plating dishes and plating methods that take into account the colors of the ingredients. This allows users to enjoy meals that are visually appealing as well. This system allows users to use up all the ingredients in their refrigerator without waste and enjoy meals that suit their preferences. In addition, plating methods are suggested, so users can enjoy meals that are visually appealing. For example, if the system suggests a stir-fry using vegetables and meat from the refrigerator, it will suggest plating methods that balance the vegetables and meat and take color into consideration. This allows users to enjoy meals that are not only delicious but also visually appealing. In this way, the AI ​​service can use up all the ingredients in the refrigerator without waste and provide meals that suit the user's preferences.

[0070] The AI ​​service according to this embodiment comprises a collection unit, a preference collection unit, a suggestion unit, and a serving suggestion unit. The collection unit collects information about ingredients in the refrigerator. The collection unit collects information about the types and quantities of ingredients using, for example, cameras and sensors installed inside the refrigerator. For example, the collection unit can collect information about vegetables, meat, seasonings, etc., inside the refrigerator. The collection unit can also periodically update the information to understand the inventory status of ingredients inside the refrigerator. The preference collection unit collects information about the user's preferences. The preference collection unit collects information about the user's preferences through, for example, an app. For example, the preference collection unit can collect information about the types of ingredients and dishes that the user likes, allergy information, etc. The preference collection unit can also periodically update the user's preference information. The suggestion unit suggests an optimal menu based on the information collected by the collection unit and the preference collection unit. The suggestion unit suggests an optimal menu using, for example, an algorithm that considers ingredient combinations and nutritional balance. For example, the suggestion unit can suggest dishes that suit the user's preferences, such as stir-fries using vegetables and meat inside the refrigerator, or stews using fish. Furthermore, the suggestion unit can also propose menus that take nutritional balance into consideration. The plating suggestion unit proposes plating methods based on the menu proposed by the suggestion unit. For example, the plating suggestion unit can propose plating methods that take into consideration color and arrangement. For example, the plating suggestion unit can propose plating methods that take into consideration the arrangement for beautifully plating the food and the colors of the ingredients. The plating suggestion unit can also propose plating methods that are visually appealing. As a result, the AI ​​service according to the embodiment can use up the ingredients in the refrigerator without waste and provide meals that suit the user's preferences.

[0071] The data collection unit collects information about food items inside the refrigerator. For example, it uses cameras and sensors installed inside the refrigerator to collect information on the type and quantity of food items. Specifically, cameras periodically photograph shelves and drawers inside the refrigerator, and use image recognition technology to identify the type and quantity of food items. Sensors, such as weight sensors and RFID tags, provide real-time information on food inventory. For example, weight sensors are installed on each shelf and drawer to measure the weight of food items and determine inventory levels. RFID tags are attached to food items, and a reader inside the refrigerator reads the tags to identify the type of food item and its expiration date. This allows the data collection unit to accurately and efficiently collect information about food items inside the refrigerator and understand inventory levels in real time. Furthermore, the data collection unit can transmit the collected data to a cloud server and share it with other departments. This allows the data collection unit to centrally manage information about food items inside the refrigerator and collaborate with other systems and departments as needed. For example, the collected data can be made accessible to the proposal department and the food presentation proposal department. Additionally, the data collection unit can adjust the frequency and accuracy of data collection to provide flexible responses to specific situations and conditions. This allows the data collection unit to collect data efficiently and effectively, improving the overall performance of the system.

[0072] The Preference Collection Department collects user preference information. For example, it collects user preference information through the app. Specifically, it collects data such as food and dish preferences, allergy information, and meal frequency and timing that users input into the app. The app has a function to analyze preference trends based on dishes and ratings previously selected by the user. For example, if a user frequently selects a particular dish, it determines that the user likes that dish and reflects this in their preference information. The app also has a function that allows users to upload photos of their meals, and image recognition technology can be used to identify the type of dish and ingredients, adding them to the preference information. Furthermore, the Preference Collection Department can periodically update user preference information. For example, if a user tries a new dish or their preferences change, the department collects this information through the app and updates the preference information. This allows the Preference Collection Department to always know the latest user preference information and provide it to the Suggestion Department and the Plating Suggestion Department. The Preference Collection Department can store the collected preference information on a cloud server and share it with other departments. This allows the preference collection unit to centrally manage user preference information and collaborate with other systems and departments as needed. For example, the collected preference information can be made accessible to the proposal department. Furthermore, the preference collection unit can adjust the frequency and accuracy of data collection, enabling flexible responses to specific situations and conditions. As a result, the preference collection unit can collect data efficiently and effectively, improving the overall system performance.

[0073] The suggestion department proposes the optimal menu based on information collected by the data collection department and the preference collection department. For example, the suggestion department proposes the optimal menu using algorithms that consider ingredient combinations and nutritional balance. Specifically, it analyzes the collected ingredient information and preference information using AI to generate a menu that matches the user's preferences and nutritional balance. For example, it can suggest dishes that match the user's preferences, such as stir-fries using vegetables and meat in the refrigerator, or stews using fish. The AI ​​considers the nutritional value, calories, and cooking methods of ingredients to generate a balanced menu. In addition, the suggestion department can propose new menus based on the user's eating history and changes in preferences, while referring to previously suggested menus. Furthermore, the suggestion department can also propose menus at the appropriate time, considering the user's meal frequency and timing. For example, it can suggest a menu suitable for breakfast, based on the time the user eats breakfast. The suggestion department can also propose menus that cater to situations where the user wants to use up a specific ingredient or consume a specific nutrient. In this way, the suggestion department can propose the optimal menu that takes into account the user's preferences and nutritional balance, supporting the user's eating habits. The proposal department can save the generated menus to a cloud server and share them with other departments. This allows the proposal department to centrally manage the generated menus and collaborate with other systems and departments as needed. For example, the generated menus can be made accessible to the plating proposal department. Furthermore, the proposal department can adjust the frequency and accuracy of data analysis to respond flexibly to specific situations and conditions. This enables the proposal department to propose menus efficiently and effectively, improving the overall performance of the system.

[0074] The plating suggestion department proposes plating methods based on the menu proposed by the proposal department. For example, the plating suggestion department proposes plating methods that take into account color and arrangement. Specifically, it uses AI to analyze the characteristics of the ingredients and dishes in the proposed menu and generates visually appealing plating methods. For example, it considers the color and shape of the dishes and proposes the arrangement of ingredients and the order of plating. The AI ​​can learn from past plating data and food photos to generate beautiful plating patterns. The plating suggestion department can also propose plating methods that suit the user's preferences and dining occasion. For example, it can propose elaborate plating for special events and parties, or simple and beautiful plating for everyday meals. Furthermore, the plating suggestion department can also provide users with procedures and tips for actually plating the food. For example, it can provide specific advice on the order in which to plate the ingredients and how to arrange them. In this way, the plating suggestion department can support users in beautifully plating their food and enhance the enjoyment of meals. The plating suggestion department can save the generated plating methods to a cloud server and share them with other departments. This allows the plating suggestion department to centrally manage the generated plating methods and collaborate with other systems and departments as needed. For example, the generated plating methods can be made accessible to the suggestion department. Furthermore, the plating suggestion department can adjust the frequency and accuracy of data analysis to enable flexible responses to specific situations and conditions. As a result, the plating suggestion department can propose plating methods efficiently and effectively, improving the overall performance of the system.

[0075] The collection unit can collect information on the type and quantity of food items using cameras and sensors installed inside the refrigerator. For example, the collection unit can collect information on the type and quantity of food items as image data using cameras installed inside the refrigerator. The collection unit can also collect information on the weight and quantity of food items as data using sensors installed inside the refrigerator. For example, the collection unit can input image data captured by cameras inside the refrigerator into a generating AI to analyze the type and quantity of food items. This allows the collection unit to accurately collect information on food items inside the refrigerator. Some or all of the above-described processes in the collection unit may be performed using AI, or without AI. For example, the collection unit can input image data captured by cameras inside the refrigerator into a generating AI to analyze the type and quantity of food items.

[0076] The preference collection unit can collect user preference information through an application. For example, the preference collection unit can collect user preference information using a smartphone application. For example, the preference collection unit can collect information such as the types of ingredients and dishes that users like, and allergy information, in the form of a questionnaire. For example, the preference collection unit can store user preference information in a database and update it periodically. For example, the preference collection unit can input user preference information into a generating AI and perform analysis of the preference information. This allows the preference collection unit to efficiently collect user preference information. Some or all of the above processing in the preference collection unit may be performed using AI, for example, or without using AI. For example, the preference collection unit can input user preference information into a generating AI and perform analysis of the preference information.

[0077] The suggestion unit can propose the optimal menu using an algorithm that considers ingredient combinations and nutritional balance. The suggestion unit proposes the optimal menu based on information collected by the data collection unit and the preference collection unit, for example. The suggestion unit can propose dishes that suit the user's preferences, such as stir-fries using vegetables and meat in the refrigerator, or stews using fish, using an algorithm that considers ingredient combinations and nutritional balance. The suggestion unit can also propose menus that consider nutritional balance, for example. The suggestion unit can analyze the collected information and propose the optimal menu using a generation AI, for example. This allows the suggestion unit to propose the optimal menu that considers nutritional balance. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the collected information into a generation AI and propose the optimal menu.

[0078] The plating suggestion unit can propose plating methods that take into account color and arrangement. For example, based on the menu proposed by the suggestion unit, the plating suggestion unit proposes arrangements for beautifully plating dishes and plating methods that take into account the color of the ingredients. For example, the plating suggestion unit can propose visually appealing plating methods using color theory. For example, the plating suggestion unit can analyze the collected information using generative AI and propose the optimal plating method. In this way, the plating suggestion unit can propose visually appealing plating methods. Some or all of the above processing in the plating suggestion unit may be performed using AI, for example, or without AI. For example, the plating suggestion unit can input the collected information into generative AI and propose the optimal plating method.

[0079] The data collection unit can estimate the user's emotions and adjust the timing of food ingredient information collection based on the estimated user emotions. For example, the data collection unit can capture the user's facial expressions with a camera and estimate the emotions using an emotion estimation algorithm. The data collection unit can also record the user's voice and estimate the emotions using voice analysis technology. For example, the data collection unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate the emotions using an emotion estimation algorithm. This allows the data collection unit to adjust the collection timing according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0080] The collection unit can monitor the freshness of ingredients in real time and suggest using ingredients that have lost their freshness as a priority. The collection unit periodically checks the freshness of ingredients using, for example, cameras and sensors inside the refrigerator. If the collection unit detects ingredients that have lost their freshness, it can notify the user and suggest using them as a priority. The collection unit can also automatically list ingredients that have lost their freshness and reflect this in menu suggestions. This allows the collection unit to reduce food waste by prioritizing the use of ingredients that have lost their freshness. Some or all of the above processing in the collection unit may be performed using, for example, AI, or not using AI. For example, the collection unit can input image data captured by a camera inside the refrigerator into a generating AI to analyze the freshness of the ingredients.

[0081] The collection unit can automatically recognize the expiration dates of ingredients and prioritize suggesting ingredients that are nearing their expiration date. For example, the collection unit can read the expiration date printed on the ingredient packaging using a camera and store it in a database. For example, if the collection unit detects ingredients that are nearing their expiration date, it can notify the user and suggest using them preferentially. The collection unit can also automatically list ingredients that are nearing their expiration date and reflect this in menu suggestions. This allows the collection unit to reduce food waste by prioritizing the use of ingredients that are nearing their expiration date. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can read the expiration date printed on the ingredient packaging using a camera, input it into a generating AI, and analyze the expiration date.

[0082] The data collection unit can estimate the user's emotions and determine the priority of food information to collect based on the estimated user emotions. For example, the data collection unit can capture the user's facial expressions with a camera and estimate the emotions using an emotion estimation algorithm. The data collection unit can also record the user's voice and estimate the emotions using voice analysis technology. For example, the data collection unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate the emotions using an emotion estimation algorithm. This allows the data collection unit to determine the priority of food information to collect according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0083] The collection unit can optimize the placement of food items in the refrigerator, enabling efficient food collection. For example, the collection unit optimizes the placement of food items in the refrigerator based on the type of food item and its frequency of use. For example, the collection unit can notify the user when changing the placement of food items and suggest an efficient collection method. For example, the collection unit can periodically review the placement of food items to maintain the optimal placement. In this way, the collection unit can efficiently collect food items by optimizing the placement of food items in the refrigerator. Some or all of the above processes in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input data on the placement of food items in the refrigerator into a generating AI and suggest an optimal placement.

[0084] The collection unit can automatically calculate the nutritional value of ingredients and collect them while considering nutritional balance. For example, the collection unit can obtain the nutritional value of ingredients from a database and calculate it automatically. For example, the collection unit can determine the priority of ingredients to collect while considering nutritional balance. For example, if the nutritional balance is skewed, the collection unit can notify the user and suggest a balanced collection method. This enables the collection unit to collect ingredients while considering nutritional balance. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input the nutritional value data of ingredients into a generating AI and analyze the nutritional balance.

[0085] The preference collection unit can estimate the user's emotions and adjust the method of collecting preference information based on the estimated user emotions. For example, the preference collection unit can capture the user's facial expressions with a camera and estimate the emotions using an emotion estimation algorithm. The preference collection unit can also record the user's voice and estimate the emotions using voice analysis technology. The preference collection unit can also collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate the emotions using an emotion estimation algorithm. This allows the preference collection unit to adjust the method of collecting preference information according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the preference collection unit may be performed using AI, for example, or without AI. For example, the preference collection unit can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0086] The preference collection unit can analyze the user's past eating history, detect changes in preferences, and update the information it collects. For example, the preference collection unit can retrieve the user's past eating history from a database and analyze changes in preferences. For example, if the preference collection unit detects a change in preferences, it can automatically update the information it collects. For example, the preference collection unit can also collect new preference information based on changes in preferences. In this way, the preference collection unit can always collect the latest preference information by updating the information it collects in accordance with changes in the user's preferences. Some or all of the above processing in the preference collection unit may be performed using AI, for example, or without AI. For example, the preference collection unit can input the user's past eating history data into a generating AI and analyze changes in preferences.

[0087] The preference collection unit can monitor the user's health status and collect preference information corresponding to that health status. For example, the preference collection unit can acquire and monitor the user's health status from the device. For example, the preference collection unit can adjust the method of collecting preference information according to the health status. For example, the preference collection unit can also collect new preference information if the health status changes. As a result, the preference collection unit can suggest healthier menus by collecting preference information according to the user's health status. Some or all of the above processing in the preference collection unit may be performed using AI, for example, or without AI. For example, the preference collection unit can input the user's health data into a generating AI and analyze preference information according to the health status.

[0088] The preference collection unit can estimate the user's emotions and determine the priority of preference information based on the estimated user emotions. For example, the preference collection unit can capture the user's facial expressions with a camera and estimate the emotions using an emotion estimation algorithm. The preference collection unit can also record the user's voice and estimate the emotions using voice analysis technology. The preference collection unit can also collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate the emotions using an emotion estimation algorithm. This allows the preference collection unit to determine the priority of preference information according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the preference collection unit may be performed using AI, for example, or without AI. For example, the preference collection unit can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0089] The preference collection unit can also collect preference information of the user's family and roommates and propose a menu that satisfies everyone. For example, the preference collection unit can collect preference information of the user's family and roommates through the app. For example, the preference collection unit can store the preference information of family and roommates in a database and reflect it in menu suggestions. For example, the preference collection unit can integrate and analyze preference information in order to propose a menu that satisfies everyone. In this way, the preference collection unit can propose a menu that satisfies everyone by collecting preference information of family and roommates. Some or all of the above processing in the preference collection unit may be performed using AI, for example, or without AI. For example, the preference collection unit can input the preference information of family and roommates into a generating AI and propose a menu that satisfies everyone.

[0090] The preference collection unit can collect preference information based on the user's meal times and frequency. For example, the preference collection unit can obtain the user's meal times and frequency from the device and reflect this in the collection of preference information. For example, the preference collection unit can determine the priority of preference information based on meal times and frequency. For example, the preference collection unit can also collect new preference information if meal times or frequency change. This allows the preference collection unit to collect more appropriate preference information by collecting preference information based on the user's meal times and frequency. Some or all of the above processing in the preference collection unit may be performed using AI, for example, or without AI. For example, the preference collection unit can input the user's meal times and frequency data into a generating AI and analyze the preference information.

[0091] The suggestion unit can estimate the user's emotions and adjust the menu suggestion method based on the estimated user emotions. For example, the suggestion unit can capture the user's facial expressions with a camera and estimate the emotions using an emotion estimation algorithm. The suggestion unit can also record the user's voice and estimate the emotions using voice analysis technology. The suggestion unit can also collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate the emotions using an emotion estimation algorithm. This allows the suggestion unit to adjust the menu suggestion method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0092] The suggestion department can propose menus that are appropriate for the season and weather, allowing users to enjoy the seasonal atmosphere. For example, the suggestion department can propose menus using seasonal ingredients. For example, the suggestion department can propose hot or cold dishes depending on the weather. For example, the suggestion department can propose menus that are tailored to seasonal events, allowing users to enjoy the seasonal atmosphere. In this way, the suggestion department can allow users to enjoy the seasonal atmosphere by proposing menus that are appropriate for the season and weather. Some or all of the above processing in the suggestion department may be performed using AI, for example, or without AI. For example, the suggestion department can input seasonal and weather data into a generating AI and propose menus that take the seasonal atmosphere into consideration.

[0093] The suggestion unit can propose menus tailored to the user's dietary goals (e.g., weight loss, muscle building). For example, the suggestion unit can propose a low-calorie menu based on the user's weight loss goals. For example, the suggestion unit can propose a high-protein menu based on the user's muscle building goals. For example, the suggestion unit can propose a balanced menu based on the user's health maintenance goals. In this way, the suggestion unit can provide meals that meet the user's goals by proposing menus tailored to their objectives. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's dietary goal data into a generating AI and propose a menu tailored to those goals.

[0094] The proposed system can estimate the user's emotions and determine the priority of the menu based on the estimated emotions. For example, the proposed system can capture the user's facial expressions with a camera and estimate the emotions using an emotion estimation algorithm. The proposed system can also record the user's voice and estimate the emotions using voice analysis technology. The proposed system can also collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate the emotions using an emotion estimation algorithm. This allows the proposed system to determine the priority of the menu according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the proposed system may be performed using AI, or not using AI. For example, the proposed system can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0095] The suggestion unit can propose regionally specific menus, taking into account the user's cultural background and eating habits. For example, the suggestion unit can suggest regionally specific dishes based on the user's cultural background. For example, the suggestion unit can suggest menus that suit the user's usual meals, taking into account the user's eating habits. For example, the suggestion unit can also suggest menus using regionally specific ingredients to suit the user's preferences. In this way, the suggestion unit can allow users to enjoy regionally specific meals by suggesting menus that take into account their cultural background and eating habits. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's cultural background and eating habit data into a generating AI and propose regionally specific menus.

[0096] The suggestion unit can propose safe menus by taking into account the user's food allergy information. For example, the suggestion unit can retrieve the user's allergy information from a database and propose a menu that does not contain allergenic ingredients. For example, the suggestion unit can propose a menu that uses alternative ingredients to avoid allergenic ingredients. For example, the suggestion unit can propose a menu that uses safe ingredients based on the user's allergy information. In this way, the suggestion unit can propose safe menus by taking into account the user's food allergy information. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's allergy information data into a generating AI and propose a safe menu.

[0097] The plating suggestion unit can estimate the user's emotions and adjust the plating method based on the estimated emotions. For example, the plating suggestion unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. The plating suggestion unit can also record the user's voice and estimate the emotion using voice analysis technology. The plating suggestion unit can also collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate the emotion using an emotion estimation algorithm. This allows the plating suggestion unit to adjust the plating method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the plating suggestion unit may be performed using AI, or not using AI. For example, the plating suggestion unit can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0098] The plating suggestion unit can propose visually appealing plating arrangements by considering the shape and color of the ingredients. For example, the plating suggestion unit can propose a balanced arrangement by considering the shape of the ingredients. For example, the plating suggestion unit can propose a visually appealing plating arrangement by considering the color of the ingredients. For example, the plating suggestion unit can propose a visually appealing plating arrangement by combining the shape and color of the ingredients. In this way, the plating suggestion unit can propose visually appealing plating arrangements by considering the shape and color of the ingredients. Some or all of the above processing in the plating suggestion unit may be performed using AI, for example, or without AI. For example, the plating suggestion unit can input data on the shape and color of the ingredients into a generating AI and propose a visually appealing plating arrangement.

[0099] The plating suggestion unit can propose plating that enhances the dining atmosphere by considering the type and arrangement of tableware. For example, the plating suggestion unit can propose the optimal plating method according to the type of tableware. For example, the plating suggestion unit can propose plating that enhances the dining atmosphere by considering the arrangement of tableware. For example, the plating suggestion unit can propose visually appealing plating by combining the type and arrangement of tableware. In this way, the plating suggestion unit can propose plating that enhances the dining atmosphere by considering the type and arrangement of tableware. Some or all of the above processing in the plating suggestion unit may be performed using AI, for example, or without AI. For example, the plating suggestion unit can input tableware type and arrangement data into a generating AI and propose plating that enhances the dining atmosphere.

[0100] The plating suggestion unit can estimate the user's emotions and determine plating priorities based on the estimated emotions. For example, the plating suggestion unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The plating suggestion unit can also record the user's voice and estimate their emotions using voice analysis technology. The plating suggestion unit can also collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. This allows the plating suggestion unit to determine plating priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the plating suggestion unit may be performed using AI or not using AI. For example, the plating suggestion unit can input user facial data into a generative AI and have the generative AI perform emotion estimation.

[0101] The plating suggestion unit can propose a balanced plating arrangement, taking into account the nutritional value of the ingredients. For example, the plating suggestion unit can obtain the nutritional value of the ingredients from a database and propose a balanced plating arrangement. For example, the plating suggestion unit can propose the arrangement of ingredients, taking nutritional balance into consideration. For example, if the nutritional value is unbalanced, the plating suggestion unit can also propose a balanced plating method. In this way, the plating suggestion unit can propose a balanced plating arrangement by taking into account the nutritional value of the ingredients. Some or all of the above processing in the plating suggestion unit may be performed using AI, for example, or without using AI. For example, the plating suggestion unit can input nutritional value data of the ingredients into a generating AI and propose a balanced plating arrangement.

[0102] The plating suggestion unit can suggest plating options tailored to the user's dining occasion (e.g., party, everyday meal). For example, the plating suggestion unit can suggest a lavish plating for a party. For example, it can suggest a simple and practical plating for an everyday meal. For example, it can suggest a themed plating for a special event. In this way, the plating suggestion unit can provide more appropriate plating by suggesting plating options tailored to the user's dining occasion. Some or all of the above processing in the plating suggestion unit may be performed using AI, for example, or without AI. For example, the plating suggestion unit can input dining scene data into a generating AI and suggest plating options appropriate to the scene.

[0103] The database can estimate the user's emotions and adjust how data is stored based on the estimated emotions. For example, the database can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The database can also record the user's voice and estimate their emotions using voice analysis technology. The database can also collect the user's biometric data (heart rate or skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. This allows the database to adjust how data is stored according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the database may be performed using AI or not. For example, the database can input user facial data into a generative AI and have the generative AI perform emotion estimation.

[0104] The database can periodically update the information within it to maintain the latest ingredient and preference information. For example, the database can periodically check the database and update the latest ingredient information. For example, the database can periodically update user preference information to maintain the latest information. The database can also automatically update the information within it to always maintain an up-to-date state. In this way, the database can always maintain the latest information by periodically updating the information within it. Some or all of the above processes in the database may be performed using AI, for example, or not using AI. For example, the database can input ingredient and preference information into a generating AI and update it periodically.

[0105] A database can set access permissions to protect user privacy. For example, a database can set access permissions to protect user privacy. For example, a database can encrypt and securely store user privacy information. A database can also periodically review access permissions to enhance privacy protection. Thus, a database can protect user privacy by setting access permissions. Some or all of the above processes in a database may be performed using AI, for example, or not. For example, a database can input access permission setting data into a generating AI and suggest optimal settings for privacy protection.

[0106] The database can estimate a user's emotions and prioritize data based on those estimated emotions. For example, the database might capture a user's facial expression with a camera and estimate their emotions using an emotion estimation algorithm. Alternatively, it could record a user's voice and estimate their emotions using voice analysis technology. It could also collect a user's biometric data (such as heart rate or skin electrical activity) using sensors and estimate their emotions using an emotion estimation algorithm. This allows the database to prioritize data according to the user's emotions. 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. Some or all of the above processing in the database may be performed using AI or not. For example, the database could input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0107] A database can integrate its information with other devices to achieve seamless information sharing. For example, a database can integrate its information with a smartphone to achieve seamless information sharing. A database can integrate its information with a tablet to achieve seamless information sharing. A database can also integrate its information with a smartwatch to achieve seamless information sharing. In this way, a database enables seamless information sharing by integrating its information with other devices. Some or all of the above processing in a database may be performed using AI, for example, or without AI. For example, a database can input data for integration with other devices into a generating AI and propose the optimal method for achieving seamless information sharing.

[0108] A database can ensure data security by regularly backing up its database. For example, a database can regularly back up its database to ensure data security. For example, a database can encrypt and securely store backup data. A database can also regularly review backup data to maintain an up-to-date state. Thus, a database can ensure data security by regularly backing up its database. Some or all of the above processes in a database may be performed using AI, for example, or not. For example, a database can input backup data into a generating AI to suggest the optimal backup method.

[0109] The database can periodically update the information within it to maintain the latest ingredient and preference information. For example, the database can periodically check the database and update the latest ingredient information. For example, the database can periodically update user preference information to maintain the latest information. The database can also automatically update the information within it to always maintain an up-to-date state. In this way, the database can always maintain the latest information by periodically updating the information within it. Some or all of the above processes in the database may be performed using AI, for example, or not using AI. For example, the database can input ingredient and preference information into a generating AI and update it periodically.

[0110] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0111] The suggestion unit can estimate the user's emotions and adjust the menu suggestion method based on the estimated user emotions. For example, the suggestion unit can capture the user's facial expressions with a camera and estimate the emotions using an emotion estimation algorithm. The suggestion unit can also record the user's voice and estimate the emotions using voice analysis technology. The suggestion unit can also collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate the emotions using an emotion estimation algorithm. This allows the suggestion unit to adjust the menu suggestion method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0112] The collection unit can monitor the freshness of ingredients in real time and suggest using ingredients that have lost their freshness as a priority. The collection unit periodically checks the freshness of ingredients using, for example, cameras and sensors inside the refrigerator. If the collection unit detects ingredients that have lost their freshness, it can notify the user and suggest using them as a priority. The collection unit can also automatically list ingredients that have lost their freshness and reflect this in menu suggestions. This allows the collection unit to reduce food waste by prioritizing the use of ingredients that have lost their freshness. Some or all of the above processing in the collection unit may be performed using, for example, AI, or not using AI. For example, the collection unit can input image data captured by a camera inside the refrigerator into a generating AI to analyze the freshness of the ingredients.

[0113] The preference collection unit can estimate the user's emotions and adjust the method of collecting preference information based on the estimated user emotions. For example, the preference collection unit can capture the user's facial expressions with a camera and estimate the emotions using an emotion estimation algorithm. The preference collection unit can also record the user's voice and estimate the emotions using voice analysis technology. The preference collection unit can also collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate the emotions using an emotion estimation algorithm. This allows the preference collection unit to adjust the method of collecting preference information according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the preference collection unit may be performed using AI, for example, or without AI. For example, the preference collection unit can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0114] The suggestion department can propose menus that are appropriate for the season and weather, allowing users to enjoy the seasonal atmosphere. For example, the suggestion department can propose menus using seasonal ingredients. For example, the suggestion department can propose hot or cold dishes depending on the weather. For example, the suggestion department can propose menus that are tailored to seasonal events, allowing users to enjoy the seasonal atmosphere. In this way, the suggestion department can allow users to enjoy the seasonal atmosphere by proposing menus that are appropriate for the season and weather. Some or all of the above processing in the suggestion department may be performed using AI, for example, or without AI. For example, the suggestion department can input seasonal and weather data into a generating AI and propose menus that take the seasonal atmosphere into consideration.

[0115] The collection unit can automatically recognize the expiration dates of ingredients and prioritize suggesting ingredients that are nearing their expiration date. For example, the collection unit can read the expiration date printed on the ingredient packaging using a camera and store it in a database. For example, if the collection unit detects ingredients that are nearing their expiration date, it can notify the user and suggest using them preferentially. The collection unit can also automatically list ingredients that are nearing their expiration date and reflect this in menu suggestions. This allows the collection unit to reduce food waste by prioritizing the use of ingredients that are nearing their expiration date. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can read the expiration date printed on the ingredient packaging using a camera, input it into a generating AI, and analyze the expiration date.

[0116] The suggestion unit can propose menus tailored to the user's dietary goals (e.g., weight loss, muscle building). For example, the suggestion unit can propose a low-calorie menu based on the user's weight loss goals. For example, the suggestion unit can propose a high-protein menu based on the user's muscle building goals. For example, the suggestion unit can propose a balanced menu based on the user's health maintenance goals. In this way, the suggestion unit can provide meals that meet the user's goals by proposing menus tailored to their objectives. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's dietary goal data into a generating AI and propose a menu tailored to those goals.

[0117] The plating suggestion unit can estimate the user's emotions and adjust the plating method based on the estimated emotions. For example, the plating suggestion unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. The plating suggestion unit can also record the user's voice and estimate the emotion using voice analysis technology. The plating suggestion unit can also collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate the emotion using an emotion estimation algorithm. This allows the plating suggestion unit to adjust the plating method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the plating suggestion unit may be performed using AI, or not using AI. For example, the plating suggestion unit can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0118] The preference collection unit can also collect preference information of the user's family and roommates and propose a menu that satisfies everyone. For example, the preference collection unit can collect preference information of the user's family and roommates through the app. For example, the preference collection unit can store the preference information of family and roommates in a database and reflect it in menu suggestions. For example, the preference collection unit can integrate and analyze preference information in order to propose a menu that satisfies everyone. In this way, the preference collection unit can propose a menu that satisfies everyone by collecting preference information of family and roommates. Some or all of the above processing in the preference collection unit may be performed using AI, for example, or without AI. For example, the preference collection unit can input the preference information of family and roommates into a generating AI and propose a menu that satisfies everyone.

[0119] The suggestion unit can propose regionally specific menus, taking into account the user's cultural background and eating habits. For example, the suggestion unit can suggest regionally specific dishes based on the user's cultural background. For example, the suggestion unit can suggest menus that suit the user's usual meals, taking into account the user's eating habits. For example, the suggestion unit can also suggest menus using regionally specific ingredients to suit the user's preferences. In this way, the suggestion unit can allow users to enjoy regionally specific meals by suggesting menus that take into account their cultural background and eating habits. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's cultural background and eating habit data into a generating AI and propose regionally specific menus.

[0120] The plating suggestion unit can suggest plating options tailored to the user's dining occasion (e.g., party, everyday meal). For example, the plating suggestion unit can suggest a lavish plating for a party. For example, it can suggest a simple and practical plating for an everyday meal. For example, it can suggest a themed plating for a special event. In this way, the plating suggestion unit can provide more appropriate plating by suggesting plating options tailored to the user's dining occasion. Some or all of the above processing in the plating suggestion unit may be performed using AI, for example, or without AI. For example, the plating suggestion unit can input dining scene data into a generating AI and suggest plating options appropriate to the scene.

[0121] The following briefly describes the processing flow for example form 2.

[0122] Step 1: The collection unit collects information about the food items inside the refrigerator. The collection unit collects information about the types and quantities of food items, for example, using cameras or sensors installed inside the refrigerator. For example, the collection unit can collect information about vegetables, meat, condiments, etc., inside the refrigerator. The collection unit can also periodically update the information to understand the inventory status of food items inside the refrigerator. Step 2: The preference collection unit collects user preference information. The preference collection unit collects user preference information, for example, through the app. For example, the preference collection unit can collect information such as the types of ingredients and dishes that the user likes, and allergy information. The preference collection unit can also periodically update the user preference information. Step 3: The suggestion unit proposes the optimal menu based on the information collected by the data collection unit and the preference collection unit. The suggestion unit proposes the optimal menu using algorithms that consider, for example, ingredient combinations and nutritional balance. For example, the suggestion unit can suggest dishes that match the user's preferences, such as stir-fries using vegetables and meat from the refrigerator, or stews using fish. The suggestion unit can also propose menus that take nutritional balance into consideration. Step 4: The plating suggestion department proposes plating methods based on the menu proposed by the suggestion department. The plating suggestion department proposes plating methods that take into account, for example, the colors and arrangement. For example, the plating suggestion department can propose plating methods that take into account the arrangement for beautifully plating the food and the colors of the ingredients. The plating suggestion department can also propose plating methods that are visually appealing.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] Each of the multiple elements described above, including the collection unit, preference collection unit, suggestion unit, and plating suggestion unit, is implemented, for example, by at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects information about ingredients in the refrigerator using the camera 42 and sensors of the smart device 14, and this information is analyzed by the identification processing unit 290 of the data processing unit 12. The preference collection unit collects user preference information through an app on the smart device 14, and this information is analyzed by the identification processing unit 290 of the data processing unit 12. The suggestion unit proposes an optimal menu using the identification processing unit 290 of the data processing unit 12, and the plating suggestion unit proposes a plating method using the control unit 46A of the smart device 14. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0127] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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).

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] Each of the multiple elements described above, including the collection unit, preference collection unit, suggestion unit, and plating suggestion unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit collects information on ingredients in the refrigerator using the camera 42 and sensors of the smart glasses 214, and this information is analyzed by the identification processing unit 290 of the data processing unit 12. The preference collection unit collects user preference information through the app on the smart glasses 214, and this information is analyzed by the identification processing unit 290 of the data processing unit 12. The suggestion unit proposes an optimal menu using the identification processing unit 290 of the data processing unit 12, and the plating suggestion unit proposes a plating method using the control unit 46A of the smart glasses 214. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0143] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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).

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.).

[0155] 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.

[0156] 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.

[0157] 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.

[0158] Each of the multiple elements described above, including the collection unit, preference collection unit, suggestion unit, and plating suggestion unit, is implemented, for example, by at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects information on ingredients in the refrigerator using the camera 42 and sensors of the headset terminal 314, and this information is analyzed by the identification processing unit 290 of the data processing unit 12. The preference collection unit collects user preference information through the application on the headset terminal 314, and this information is analyzed by the identification processing unit 290 of the data processing unit 12. The suggestion unit proposes an optimal menu using the identification processing unit 290 of the data processing unit 12, and the plating suggestion unit proposes a plating method using the control unit 46A of the headset terminal 314. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0159] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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).

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.).

[0172] 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.

[0173] 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.

[0174] 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.

[0175] Each of the multiple elements described above, including the collection unit, preference collection unit, suggestion unit, and plating suggestion 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 on ingredients in the refrigerator using the camera 42 and sensors of the robot 414, and this information is analyzed by the identification processing unit 290 of the data processing unit 12. The preference collection unit collects user preference information through the robot 414's app, and this information is analyzed by the identification processing unit 290 of the data processing unit 12. The suggestion unit proposes an optimal menu using the identification processing unit 290 of the data processing unit 12, and the plating suggestion unit proposes a plating method using the control unit 46A of the robot 414. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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."

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] (Note 1) A collection unit that collects information on the ingredients inside the refrigerator, A preference collection unit that collects user preference information, A suggestion unit that proposes the optimal menu based on the information collected by the collection unit and the preference collection unit, The system includes a plating suggestion unit that proposes a plating method based on the menu proposed by the aforementioned suggestion unit. A system characterized by the following features. (Note 2) It has a database for storing and managing the collected information. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned collection unit is Cameras and sensors installed inside the refrigerator are used to collect information on the type and quantity of food items. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned preference collection unit is, Collect user preference information through the app. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned proposal section is, We propose the optimal menu using an algorithm that considers the combination of ingredients and nutritional balance. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned plating suggestion section is We propose a plating method that takes color and arrangement into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is The system estimates the user's emotions and adjusts the timing of ingredient information collection based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is The system monitors the freshness of ingredients in real time and suggests prioritizing the use of ingredients that have lost their freshness. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is It automatically recognizes the expiration dates of ingredients and prioritizes suggesting ingredients that are nearing their expiration date. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is The system estimates the user's emotions and prioritizes the food information to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is Optimize the placement of ingredients in the refrigerator to allow for more efficient retrieval of food. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is The system automatically calculates the nutritional value of ingredients and collects them while considering nutritional balance. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned preference collection unit is, It estimates the user's emotions and adjusts the method of collecting preference information based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned preference collection unit is, We analyze the user's past eating history, detect changes in preferences, and update the collected information. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned preference collection unit is, The system monitors the user's health status and collects preference information tailored to that health status. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned preference collection unit is, It estimates the user's emotions and prioritizes preference information based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned preference collection unit is, The system also collects information on the preferences of the user's family and roommates, and proposes menus that will satisfy everyone. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned preference collection unit is, We collect preference information based on the user's meal times and frequency. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned proposal section is, The system estimates the user's emotions and adjusts the menu suggestion method based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned proposal section is, We propose menus that are appropriate for the season and weather, allowing customers to enjoy the feeling of the season. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned proposal section is, We propose menus tailored to the user's dietary needs. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned proposal section is, It estimates the user's emotions and determines the priority of the menu based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned proposal section is, We propose regionally specific menus, taking into account the user's cultural background and eating habits. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned proposal section is, We propose safe menus that take into account the user's food allergy information. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned plating suggestion section is The system estimates the user's emotions and adjusts the plating method based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned plating suggestion section is We propose visually appealing plating, taking into account the shape and color of the ingredients. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned plating suggestion section is We propose plating arrangements that enhance the dining experience, taking into account the type and placement of tableware. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned plating suggestion section is The system estimates the user's emotions and determines the priority of the food presentation based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned plating suggestion section is We propose a balanced plating arrangement that takes into account the nutritional value of the ingredients. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned plating suggestion section is We propose plating ideas tailored to the user's dining experience. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned database is We estimate the user's emotions and adjust how data is stored based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 32) The aforementioned database is The database is regularly updated to maintain the latest information on ingredients and preferences. The system described in Appendix 2, characterized by the features described herein. (Note 33) The aforementioned database is Set database access permissions to protect user privacy. The system described in Appendix 2, characterized by the features described herein. (Note 34) The aforementioned database is It estimates user sentiment and prioritizes data based on the estimated user sentiment. The system described in Appendix 2, characterized by the features described herein. (Note 35) The aforementioned database is By linking information within the database with other devices, seamless information sharing can be achieved. The system described in Appendix 2, characterized by the features described herein. (Note 36) The aforementioned database is Regularly back up your database to ensure data security. The system described in Appendix 2, characterized by the features described herein. [Explanation of Symbols]

[0195] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A collection unit that collects information on the ingredients inside the refrigerator, A preference collection unit that collects user preference information, A suggestion unit that proposes the optimal menu based on the information collected by the collection unit and the preference collection unit, The system includes a plating suggestion unit that proposes a plating method based on the menu proposed by the aforementioned suggestion unit. A system characterized by the following features.

2. It has a database for storing and managing the collected information. The system according to feature 1.

3. The aforementioned collection unit is Cameras and sensors installed inside the refrigerator are used to collect information on the type and quantity of food items. The system according to feature 1.

4. The aforementioned preference collection unit is, Collect user preference information through the app. The system according to feature 1.

5. The aforementioned proposal section is, We propose the optimal menu using an algorithm that considers the combination of ingredients and nutritional balance. The system according to feature 1.

6. The aforementioned plating suggestion section is We propose a plating method that takes color and arrangement into consideration. The system according to feature 1.

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

8. The aforementioned collection unit is The system monitors the freshness of ingredients in real time and suggests prioritizing the use of ingredients that have lost their freshness. The system according to feature 1.

9. The aforementioned collection unit is It automatically recognizes the expiration dates of ingredients and prioritizes suggesting ingredients that are nearing their expiration date. The system according to feature 1.

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

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