system

Abstract

The system according to this embodiment aims to propose an optimal menu that takes into account the user's preferences, allergies, and health condition, and to automatically order the necessary ingredients. [Solution] The system according to the embodiment comprises a collection unit, a suggestion unit, an advice unit, and an order unit. The collection unit collects information such as the user's preferences, allergies, health condition, and ingredient inventory. The suggestion unit proposes an optimal menu based on the information collected by the collection unit. The advice unit provides real-time advice based on nutritional balance and health condition based on the menu proposed by the suggestion unit. The order unit automatically orders the necessary ingredients online based on the advice provided by the advice 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 the chatbot's character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, it has not been fully achieved to propose an optimal menu considering the user's preferences, allergies, and health conditions and automatically order the necessary ingredients, and there is room for improvement.

[0005] The system according to the embodiment aims to propose an optimal menu considering the user's preferences, allergies, and health conditions and automatically order the necessary ingredients.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, a suggestion unit, an advice unit, and an order unit. The data collection unit collects information such as the user's preferences, allergies, health status, and ingredient inventory. The suggestion unit proposes an optimal menu based on the information collected by the data collection unit. The advice unit provides real-time advice based on nutritional balance and health status, based on the menu proposed by the suggestion unit. The order unit automatically orders the necessary ingredients online based on the advice provided by the advice unit. [Effects of the Invention]

[0007] The system according to this embodiment can suggest an optimal menu that takes into account the user's preferences, allergies, and health condition, and can automatically order the necessary ingredients. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The AI ​​agent according to an embodiment of the present invention is a system that learns family preferences, allergies, health conditions (high blood sugar, high blood pressure, high cholesterol), and food inventory, and proposes the optimal menu according to each family and time of year. This AI agent provides delicious meals without waste, taking into account the user's preferences and nutritional balance. Furthermore, it has the function of providing real-time advice based on nutritional balance and health conditions, and automatically ordering necessary ingredients online. For example, the AI ​​agent checks the inventory status of ingredients using a camera attached to the refrigerator and uses them without waste. It provides balanced meals that also take allergies and nutritional management into consideration. It provides real-time advice based on nutritional balance and health conditions, and automatically orders necessary ingredients online, saving the user the trouble of shopping. In addition, by automating refrigerator inventory checks and supporting everything from menu creation to shopping list creation and shopping assistance, it can save the user time. In this way, the AI ​​agent can not only learn the user's information and propose the optimal menu, but also provide real-time advice based on nutritional balance and health conditions, and automatically order necessary ingredients online, thereby supporting the user's life.

[0029] The AI ​​agent according to this embodiment comprises a data collection unit, a suggestion unit, an advice unit, and an order unit. The data collection unit collects information such as the user's preferences, allergies, health status, and food inventory. The data collection unit analyzes, for example, information entered by the user and image data of food taken by a camera attached to the refrigerator. For example, the user inputs "dishes that the whole family likes" or "foods they are allergic to," and the data collection unit analyzes images of food taken by the refrigerator's camera to collect user information. The suggestion unit proposes an optimal menu based on the information collected by the data collection unit. For example, the suggestion unit proposes a balanced meal considering the user's preferences, allergies, and health status. The suggestion unit checks the inventory status of the refrigerator and proposes a menu that makes the most efficient use of ingredients. For example, by suggesting recipes using ingredients in the refrigerator, food waste can be reduced. The advice unit provides real-time advice based on nutritional balance and health status, based on the menu proposed by the suggestion unit. For example, when the user eats a meal, the advice unit analyzes the nutritional balance of that meal and provides advice on supplementing necessary nutrients. The advice unit can also advise the user on consuming specific nutrients based on their health condition. The ordering unit automatically orders the necessary ingredients online based on the advice provided by the advice unit. For example, if the refrigerator is running low, the ordering unit automatically orders the necessary ingredients online. This allows the AI ​​agent according to the embodiment to collect information such as the user's preferences, allergies, health condition, and ingredient inventory, propose an optimal menu, provide real-time advice, and automatically order the necessary ingredients online.

[0030] The data collection unit collects information such as user preferences, allergies, health status, and food inventory. Specifically, it analyzes information entered by the user and image data of food captured by a camera attached to the refrigerator. For example, the unit collects user information by having the user enter "dishes that the whole family likes" or "foods they are allergic to," and then analyzing images of the food captured by the refrigerator's camera. The data collection unit receives information entered by the user through a dedicated application and stores it in a database. The information entered by the user includes the types of dishes they like, foods they dislike, allergies, health status (e.g., diabetes or high blood pressure), and the frequency and quantity of meals. In addition, the camera installed in the refrigerator periodically photographs the food inside and sends the image data to the cloud. On the cloud, image recognition technology is used to identify the type and quantity of food and manage it as inventory information. Furthermore, the data collection unit also collects the user's purchase history and eating history to understand the user's preferences and eating habits. This allows the data collection unit to centrally manage detailed user information and make it available to other departments. The data collection unit updates this information in real time, ensuring that the latest data is always available, thereby enabling the proposal and advice units to function based on accurate information.

[0031] The suggestion department proposes optimal menus based on information collected by the data collection department. Specifically, it proposes balanced meals that take into account the user's preferences, allergies, and health condition. The suggestion department checks the inventory status of the refrigerator and proposes menus that make the most efficient use of ingredients. For example, by suggesting recipes that use ingredients already in the refrigerator, food waste can be reduced. The suggestion department uses AI to generate recipes that are optimal for the user's preferences and health condition. The AI ​​analyzes the data provided by the data collection department and creates nutritionally balanced menus that take into account the user's preferences and allergy information. For example, if the user has diabetes, it will prioritize suggesting low-carbohydrate recipes. It also minimizes food waste by suggesting recipes that prioritize the use of ingredients nearing their expiration date, based on the refrigerator inventory information. The suggestion department continuously improves its suggestions based on the user's meal history and feedback. The user evaluates the suggested recipes, and the suggestion department learns from the evaluation results, improving the accuracy of future suggestions. Furthermore, the suggestion department can also propose special recipes tailored to the season and events. For example, the system can suggest menus tailored to special occasions such as Christmas or birthdays, enriching the user's dining experience. This allows the suggestion department to propose menus best suited to the user's preferences and health condition, reduce food waste, and increase user satisfaction.

[0032] The Advice Unit provides real-time advice based on nutritional balance and health status, using menus proposed by the Proposal Unit. Specifically, when a user eats, it analyzes the nutritional balance of that meal and provides advice on supplementing necessary nutrients. The Advice Unit can also advise on consuming specific nutrients based on the user's health status. For example, if a user is iron deficient, it will advise consuming foods rich in iron. The Advice Unit uses AI to analyze the nutritional value of proposed menus and generates advice tailored to the user's health status. Based on the user's health data provided by the Collection Unit, the AI ​​evaluates nutritional balance and calculates the necessary nutrients and intake amounts. For example, if a user has high blood pressure, it will advise limiting salt intake and recommend foods rich in potassium instead. Furthermore, the Advice Unit supports long-term improvement of nutritional balance based on the user's eating history. For example, it analyzes past eating data and, if a specific nutrient is deficient, suggests foods and recipes to supplement that nutrient. In addition, the Advice Unit provides advice that takes into account the user's lifestyle and exercise habits. For example, users with high activity levels might be offered high-calorie meals for energy replenishment, while users with low activity levels might be recommended low-calorie, nutrient-rich meals. This allows the advice department to provide appropriate advice tailored to the user's health condition and lifestyle, supporting the maintenance and improvement of their health.

[0033] The ordering department automatically orders necessary ingredients online based on advice provided by the advice department. Specifically, when the refrigerator's inventory is low, it automatically orders the necessary ingredients online. For example, the ordering department monitors refrigerator inventory information in real time and automatically places an order when the inventory falls below a certain threshold. The ordering department selects the most suitable ingredients considering the user's purchase history and preferences and places orders from reliable online stores. The ordering department uses AI to calculate the optimal ordering timing and quantity, ensuring efficient purchasing without waste. For example, it sets up regular automatic orders for ingredients that the user frequently uses to prevent stockouts. It also considers information such as whether a particular ingredient is on sale or if there are seasonal products when placing orders. Furthermore, the ordering department can suggest appropriate substitutes based on the user's preferences and allergy information. For example, if a particular ingredient is out of stock, it suggests a substitute with similar nutritional value to meet the user's needs. The ordering department also has a function to track the progress of orders and delivery status in real time and notify the user. This allows the user to always know the status of their order and use the service with peace of mind. This allows the ordering department to support users' dietary needs by saving them time and efficiently procuring the necessary ingredients.

[0034] The collection unit can check the inventory status of food items using a camera attached to the refrigerator, ensuring that food is used efficiently and without waste. For example, the collection unit can check the inventory status of food items using a camera attached to the refrigerator, ensuring that food is used efficiently and without waste. This allows the collection unit to check the inventory status of food items using a camera attached to the refrigerator, ensuring that food is used efficiently and without waste.

[0035] The suggestion department can propose a balanced meal plan that takes into account the user's preferences, allergies, and health condition. For example, the suggestion department can propose a balanced meal plan that takes into account the user's preferences, allergies, and health condition. This allows the suggestion department to propose a balanced meal plan that takes into account the user's preferences, allergies, and health condition.

[0036] The advice unit can analyze the nutritional balance of a meal when the user eats and provide advice on how to supplement necessary nutrients. For example, the advice unit can analyze the nutritional balance of a meal when the user eats and provide advice on how to supplement necessary nutrients. For example, the advice unit can analyze the nutritional balance of a meal when the user eats and provide advice on how to supplement necessary nutrients. This allows the system to analyze the nutritional balance of a meal when the user eats and provide advice on how to supplement necessary nutrients.

[0037] The advice unit can advise the user to consume specific nutrients based on their health condition. For example, the advice unit advises the user to consume specific nutrients based on their health condition. This allows the advice unit to advise the user to consume specific nutrients based on their health condition.

[0038] The ordering department can automatically order necessary ingredients online when the refrigerator's inventory is running low. For example, the ordering department can automatically order necessary ingredients online when the refrigerator's inventory is running low. This allows the ordering department to automatically order necessary ingredients online when the refrigerator's inventory is running low.

[0039] The data collection unit can analyze the user's past eating history and select the optimal information collection method. For example, the data collection unit can prioritize collecting relevant information based on the dishes the user has enjoyed eating in the past. For example, the data collection unit can collect information from the user's past eating history to help avoid allergenic ingredients. For example, the data collection unit can analyze the user's past eating history and collect information that takes nutritional balance into consideration. In this way, by analyzing the user's past eating history, the optimal information collection method can be selected.

[0040] The data collection unit can collect information while considering the freshness and expiration date of ingredients when checking the inventory status of ingredients. For example, the data collection unit can check the freshness of ingredients in the refrigerator and collect information to prioritize the use of fresh ingredients. For example, the data collection unit can collect information to prioritize the use of ingredients that are nearing their expiration date. For example, the data collection unit can collect information on the optimal storage method, taking into account the freshness and expiration date of ingredients. By collecting information while considering the freshness and expiration date of ingredients, more appropriate information can be collected.

[0041] The data collection unit can prioritize the collection of highly relevant information by considering the user's geographical location. For example, the data collection unit can collect information about groceries available at nearby supermarkets based on the user's current location. For example, the data collection unit can collect information about local specialties and seasonal ingredients based on the user's geographical location. For example, the data collection unit can collect information about online shops that offer delivery, taking the user's geographical location into consideration. By prioritizing the collection of highly relevant information while considering the user's geographical location, more appropriate information can be collected.

[0042] The data collection unit analyzes the user's social media activity during information gathering and can collect relevant information. For example, the data collection unit collects information about recipes and ingredients that the user has shared on social media. For example, the data collection unit collects information about ingredients and recipes recommended by the user's social media followers. For example, the data collection unit analyzes the content of the user's social media posts and collects information about ingredients and recipes that the user is interested in. In this way, relevant information can be collected by analyzing the user's social media activity.

[0043] The proposal department can adjust the level of detail in its proposals based on the importance of the ingredients. For example, it can propose detailed cooking methods for major ingredients. For example, it can propose simplified cooking methods for auxiliary ingredients. For example, it can make proposals that take into account cooking time and effort depending on the importance of the ingredients. By adjusting the level of detail in proposals based on the importance of the ingredients, it can make more appropriate proposals.

[0044] The suggestion function can apply different suggestion algorithms depending on the food category when making suggestions. For example, for vegetables, the suggestion function will make suggestions that prioritize nutritional value. For meats, the suggestion function will make suggestions that prioritize cooking methods and seasonings. For seafood, the suggestion function will make suggestions that prioritize freshness and preservation methods. By applying different suggestion algorithms depending on the food category, the function can make more appropriate suggestions.

[0045] The proposal department can prioritize proposals based on the availability of ingredients. For example, the department might propose recipes that prioritize using seasonal ingredients, recipes that prioritize using ingredients nearing their expiration date, or recipes that avoid hard-to-find ingredients and use readily available ones. By prioritizing proposals based on the availability of ingredients, the department can make more appropriate suggestions.

[0046] The suggestion function can adjust the order of suggestions based on the relationships between ingredients. For example, it can suggest side dishes related to a main dish. For example, it can suggest multiple recipes using the same ingredients. For example, it can suggest a balanced menu considering combinations of ingredients. By adjusting the order of suggestions based on the relationships between ingredients, it can provide more appropriate suggestions.

[0047] The advice unit can provide optimal advice by referring to the user's past health data. For example, the advice unit can provide advice that considers nutritional balance based on the user's past health data. For example, the advice unit can provide advice to avoid foods that the user is allergic to based on the user's past health data. For example, the advice unit can provide advice to consume specific nutrients by referring to the user's past health data. In this way, by referring to the user's past health data, it can provide optimal advice.

[0048] The advice function can customize the advice given based on the user's current lifestyle. For example, if the user is busy, it will suggest recipes that can be made in a short amount of time. If the user is relaxed, it will suggest recipes that can be made over a longer period of time. If the user is participating in a specific event, it will suggest recipes suitable for that event. By customizing the advice based on the user's current lifestyle, it can provide more appropriate advice.

[0049] The advice function can provide optimal advice by taking into account the user's geographical location. For example, the advice function can provide advice using ingredients available at nearby supermarkets based on the user's current location. For example, the advice function can provide advice using local specialties or seasonal ingredients based on the user's geographical location. For example, the advice function can provide information on online shops that can deliver, taking into account the user's geographical location. By providing optimal advice while considering the user's geographical location, it is possible to provide more appropriate advice.

[0050] The advice function can analyze the user's social media activity and adjust the content of the advice accordingly. For example, the advice function can provide advice on recipes and ingredients that the user has shared on social media. For example, the advice function can provide advice on ingredients and recipes recommended by the user's social media followers. For example, the advice function can analyze the user's social media posts and provide advice on ingredients and recipes that the user is interested in. In this way, by analyzing the user's social media activity, it is possible to provide more appropriate advice.

[0051] The ordering system can select the optimal ordering method by referring to the user's past purchase history when an order is placed. For example, the ordering system can prioritize ordering related ingredients based on the ingredients the user has purchased in the past. For example, the ordering system can place orders that avoid ingredients the user is allergic to, based on the user's past purchase history. For example, the ordering system can analyze the user's past purchase history and place orders that take nutritional balance into consideration. In this way, the system can select the optimal ordering method by referring to the user's past purchase history.

[0052] The ordering system can customize orders based on the user's current lifestyle. For example, if the user is busy, the system will prioritize ordering ingredients that can be cooked quickly. If the user is relaxed, the system will prioritize ordering ingredients that can be cooked over a longer period of time. If the user is attending a specific event, the system will order ingredients suitable for that event. By customizing orders based on the user's current lifestyle, the system can make more appropriate orders.

[0053] The ordering system can select the most suitable ordering method when an order is placed, taking into account the user's geographical location. For example, the ordering system can prioritize ordering ingredients available at nearby supermarkets based on the user's current location. For example, the ordering system can prioritize ordering local specialties and seasonal ingredients based on the user's geographical location. For example, the ordering system can order from online shops that can deliver, taking into account the user's geographical location. By selecting the most suitable ordering method considering the user's geographical location, more appropriate orders can be made.

[0054] The ordering department can analyze the user's social media activity and adjust the order content at the time of ordering. For example, the ordering department can place orders based on ingredients and recipes that the user has shared on social media. For example, the ordering department can place orders based on ingredients and recipes recommended by the user's social media followers. For example, the ordering department can analyze the user's social media posts and place orders based on ingredients and recipes that the user is interested in. In this way, by analyzing the user's social media activity, more appropriate orders can be placed.

[0055] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0056] The data collection unit can analyze the user's past eating history and select the optimal information collection method. For example, the data collection unit can prioritize collecting relevant information based on the dishes the user has enjoyed eating in the past. For example, the data collection unit can collect information from the user's past eating history to help avoid allergenic ingredients. For example, the data collection unit can analyze the user's past eating history and collect information that takes nutritional balance into consideration. In this way, by analyzing the user's past eating history, the optimal information collection method can be selected.

[0057] The data collection unit can collect information while considering the freshness and expiration date of ingredients when checking the inventory status of ingredients. For example, the data collection unit can check the freshness of ingredients in the refrigerator and collect information to prioritize the use of fresh ingredients. For example, the data collection unit can collect information to prioritize the use of ingredients that are nearing their expiration date. For example, the data collection unit can collect information on the optimal storage method, taking into account the freshness and expiration date of ingredients. By collecting information while considering the freshness and expiration date of ingredients, more appropriate information can be collected.

[0058] The proposal department can adjust the level of detail in its proposals based on the importance of the ingredients. For example, it can propose detailed cooking methods for major ingredients. For example, it can propose simplified cooking methods for auxiliary ingredients. For example, it can make proposals that take into account cooking time and effort depending on the importance of the ingredients. By adjusting the level of detail in proposals based on the importance of the ingredients, it can make more appropriate proposals.

[0059] The suggestion function can apply different suggestion algorithms depending on the food category when making suggestions. For example, for vegetables, the suggestion function will make suggestions that prioritize nutritional value. For meats, the suggestion function will make suggestions that prioritize cooking methods and seasonings. For seafood, the suggestion function will make suggestions that prioritize freshness and preservation methods. By applying different suggestion algorithms depending on the food category, the function can make more appropriate suggestions.

[0060] The proposal department can prioritize proposals based on the availability of ingredients. For example, the department might propose recipes that prioritize using seasonal ingredients, recipes that prioritize using ingredients nearing their expiration date, or recipes that avoid hard-to-find ingredients and use readily available ones. By prioritizing proposals based on the availability of ingredients, the department can make more appropriate suggestions.

[0061] The suggestion function can adjust the order of suggestions based on the relationships between ingredients. For example, it can suggest side dishes related to a main dish. For example, it can suggest multiple recipes using the same ingredients. For example, it can suggest a balanced menu considering combinations of ingredients. By adjusting the order of suggestions based on the relationships between ingredients, it can provide more appropriate suggestions.

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

[0063] Step 1: The data collection unit collects information such as the user's preferences, allergies, health status, and food inventory. For example, it collects user information by analyzing information entered by the user or by analyzing image data of food items taken by a camera attached to the refrigerator. Step 2: The suggestion department proposes the optimal menu based on the information collected by the data collection department. For example, it proposes a balanced meal considering the user's preferences, allergies, and health condition, and proposes a menu that makes the most efficient use of ingredients by checking the inventory status of the refrigerator. Step 3: The advice unit provides real-time advice based on nutritional balance and health status, using the menu proposed by the suggestion unit. For example, when a user eats a meal, the unit analyzes the nutritional balance of that meal and provides advice on supplementing necessary nutrients. Step 4: The ordering department automatically orders the necessary ingredients online based on the advice provided by the advice department. For example, if the refrigerator is running low on ingredients, it will automatically order the necessary ingredients online.

[0064] (Example of form 2) The AI ​​agent according to an embodiment of the present invention is a system that learns family preferences, allergies, health conditions (high blood sugar, high blood pressure, high cholesterol), and food inventory, and proposes the optimal menu according to each family and time of year. This AI agent provides delicious meals without waste, taking into account the user's preferences and nutritional balance. Furthermore, it has the function of providing real-time advice based on nutritional balance and health conditions, and automatically ordering necessary ingredients online. For example, the AI ​​agent checks the inventory status of ingredients using a camera attached to the refrigerator and uses them without waste. It provides balanced meals that also take allergies and nutritional management into consideration. It provides real-time advice based on nutritional balance and health conditions, and automatically orders necessary ingredients online, saving the user the trouble of shopping. In addition, by automating refrigerator inventory checks and supporting everything from menu creation to shopping list creation and shopping assistance, it can save the user time. In this way, the AI ​​agent can not only learn the user's information and propose the optimal menu, but also provide real-time advice based on nutritional balance and health conditions, and automatically order necessary ingredients online, thereby supporting the user's life.

[0065] The AI ​​agent according to this embodiment comprises a data collection unit, a suggestion unit, an advice unit, and an order unit. The data collection unit collects information such as the user's preferences, allergies, health status, and food inventory. The data collection unit analyzes, for example, information entered by the user and image data of food taken by a camera attached to the refrigerator. For example, the user inputs "dishes that the whole family likes" or "foods they are allergic to," and the data collection unit analyzes images of food taken by the refrigerator's camera to collect user information. The suggestion unit proposes an optimal menu based on the information collected by the data collection unit. For example, the suggestion unit proposes a balanced meal considering the user's preferences, allergies, and health status. The suggestion unit checks the inventory status of the refrigerator and proposes a menu that makes the most efficient use of ingredients. For example, by suggesting recipes using ingredients in the refrigerator, food waste can be reduced. The advice unit provides real-time advice based on nutritional balance and health status, based on the menu proposed by the suggestion unit. For example, when the user eats a meal, the advice unit analyzes the nutritional balance of that meal and provides advice on supplementing necessary nutrients. The advice unit can also advise the user on consuming specific nutrients based on their health condition. The ordering unit automatically orders the necessary ingredients online based on the advice provided by the advice unit. For example, if the refrigerator is running low, the ordering unit automatically orders the necessary ingredients online. This allows the AI ​​agent according to the embodiment to collect information such as the user's preferences, allergies, health condition, and ingredient inventory, propose an optimal menu, provide real-time advice, and automatically order the necessary ingredients online.

[0066] The data collection unit collects information such as user preferences, allergies, health status, and food inventory. Specifically, it analyzes information entered by the user and image data of food captured by a camera attached to the refrigerator. For example, the unit collects user information by having the user enter "dishes that the whole family likes" or "foods they are allergic to," and then analyzing images of the food captured by the refrigerator's camera. The data collection unit receives information entered by the user through a dedicated application and stores it in a database. The information entered by the user includes the types of dishes they like, foods they dislike, allergies, health status (e.g., diabetes or high blood pressure), and the frequency and quantity of meals. In addition, the camera installed in the refrigerator periodically photographs the food inside and sends the image data to the cloud. On the cloud, image recognition technology is used to identify the type and quantity of food and manage it as inventory information. Furthermore, the data collection unit also collects the user's purchase history and eating history to understand the user's preferences and eating habits. This allows the data collection unit to centrally manage detailed user information and make it available to other departments. The data collection unit updates this information in real time, ensuring that the latest data is always available, thereby enabling the proposal and advice units to function based on accurate information.

[0067] The suggestion department proposes optimal menus based on information collected by the data collection department. Specifically, it proposes balanced meals that take into account the user's preferences, allergies, and health condition. The suggestion department checks the inventory status of the refrigerator and proposes menus that make the most efficient use of ingredients. For example, by suggesting recipes that use ingredients already in the refrigerator, food waste can be reduced. The suggestion department uses AI to generate recipes that are optimal for the user's preferences and health condition. The AI ​​analyzes the data provided by the data collection department and creates nutritionally balanced menus that take into account the user's preferences and allergy information. For example, if the user has diabetes, it will prioritize suggesting low-carbohydrate recipes. It also minimizes food waste by suggesting recipes that prioritize the use of ingredients nearing their expiration date, based on the refrigerator inventory information. The suggestion department continuously improves its suggestions based on the user's meal history and feedback. The user evaluates the suggested recipes, and the suggestion department learns from the evaluation results, improving the accuracy of future suggestions. Furthermore, the suggestion department can also propose special recipes tailored to the season and events. For example, the system can suggest menus tailored to special occasions such as Christmas or birthdays, enriching the user's dining experience. This allows the suggestion department to propose menus best suited to the user's preferences and health condition, reduce food waste, and increase user satisfaction.

[0068] The Advice Unit provides real-time advice based on nutritional balance and health status, using menus proposed by the Proposal Unit. Specifically, when a user eats, it analyzes the nutritional balance of that meal and provides advice on supplementing necessary nutrients. The Advice Unit can also advise on consuming specific nutrients based on the user's health status. For example, if a user is iron deficient, it will advise consuming foods rich in iron. The Advice Unit uses AI to analyze the nutritional value of proposed menus and generates advice tailored to the user's health status. Based on the user's health data provided by the Collection Unit, the AI ​​evaluates nutritional balance and calculates the necessary nutrients and intake amounts. For example, if a user has high blood pressure, it will advise limiting salt intake and recommend foods rich in potassium instead. Furthermore, the Advice Unit supports long-term improvement of nutritional balance based on the user's eating history. For example, it analyzes past eating data and, if a specific nutrient is deficient, suggests foods and recipes to supplement that nutrient. In addition, the Advice Unit provides advice that takes into account the user's lifestyle and exercise habits. For example, users with high activity levels might be offered high-calorie meals for energy replenishment, while users with low activity levels might be recommended low-calorie, nutrient-rich meals. This allows the advice department to provide appropriate advice tailored to the user's health condition and lifestyle, supporting the maintenance and improvement of their health.

[0069] The ordering department automatically orders necessary ingredients online based on advice provided by the advice department. Specifically, when the refrigerator's inventory is low, it automatically orders the necessary ingredients online. For example, the ordering department monitors refrigerator inventory information in real time and automatically places an order when the inventory falls below a certain threshold. The ordering department selects the most suitable ingredients considering the user's purchase history and preferences and places orders from reliable online stores. The ordering department uses AI to calculate the optimal ordering timing and quantity, ensuring efficient purchasing without waste. For example, it sets up regular automatic orders for ingredients that the user frequently uses to prevent stockouts. It also considers information such as whether a particular ingredient is on sale or if there are seasonal products when placing orders. Furthermore, the ordering department can suggest appropriate substitutes based on the user's preferences and allergy information. For example, if a particular ingredient is out of stock, it suggests a substitute with similar nutritional value to meet the user's needs. The ordering department also has a function to track the progress of orders and delivery status in real time and notify the user. This allows the user to always know the status of their order and use the service with peace of mind. This allows the ordering department to support users' dietary needs by saving them time and efficiently procuring the necessary ingredients.

[0070] The collection unit can check the inventory status of food items using a camera attached to the refrigerator, ensuring that food is used efficiently and without waste. For example, the collection unit can check the inventory status of food items using a camera attached to the refrigerator, ensuring that food is used efficiently and without waste. This allows the collection unit to check the inventory status of food items using a camera attached to the refrigerator, ensuring that food is used efficiently and without waste.

[0071] The suggestion department can propose a balanced meal plan that takes into account the user's preferences, allergies, and health condition. For example, the suggestion department can propose a balanced meal plan that takes into account the user's preferences, allergies, and health condition. This allows the suggestion department to propose a balanced meal plan that takes into account the user's preferences, allergies, and health condition.

[0072] The advice unit can analyze the nutritional balance of a meal when the user eats and provide advice on how to supplement necessary nutrients. For example, the advice unit can analyze the nutritional balance of a meal when the user eats and provide advice on how to supplement necessary nutrients. For example, the advice unit can analyze the nutritional balance of a meal when the user eats and provide advice on how to supplement necessary nutrients. This allows the system to analyze the nutritional balance of a meal when the user eats and provide advice on how to supplement necessary nutrients.

[0073] The advice unit can advise the user to consume specific nutrients based on their health condition. For example, the advice unit advises the user to consume specific nutrients based on their health condition. This allows the advice unit to advise the user to consume specific nutrients based on their health condition.

[0074] The ordering department can automatically order necessary ingredients online when the refrigerator's inventory is running low. For example, the ordering department can automatically order necessary ingredients online when the refrigerator's inventory is running low. This allows the ordering department to automatically order necessary ingredients online when the refrigerator's inventory is running low.

[0075] The data collection unit can estimate the user's emotions and adjust the timing of information collection based on the estimated emotions. For example, if the user is stressed, the data collection unit will collect information during a relaxed time. If the user is busy, the data collection unit will adjust to complete the information collection in a short amount of time. If the user is relaxed, the data collection unit will take more time to collect detailed information. By adjusting the timing of information collection based on the user's emotions, information can be collected at a more appropriate time. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0076] The data collection unit can analyze the user's past eating history and select the optimal information collection method. For example, the data collection unit can prioritize collecting relevant information based on the dishes the user has enjoyed eating in the past. For example, the data collection unit can collect information from the user's past eating history to help avoid allergenic ingredients. For example, the data collection unit can analyze the user's past eating history and collect information that takes nutritional balance into consideration. In this way, by analyzing the user's past eating history, the optimal information collection method can be selected.

[0077] The data collection unit can collect information while considering the freshness and expiration date of ingredients when checking the inventory status of ingredients. For example, the data collection unit can check the freshness of ingredients in the refrigerator and collect information to prioritize the use of fresh ingredients. For example, the data collection unit can collect information to prioritize the use of ingredients that are nearing their expiration date. For example, the data collection unit can collect information on the optimal storage method, taking into account the freshness and expiration date of ingredients. By collecting information while considering the freshness and expiration date of ingredients, more appropriate information can be collected.

[0078] The data collection unit can estimate the user's emotions and determine the priority of information to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit will prioritize collecting information about foods with relaxing effects. For example, if the user is tired, the data collection unit will prioritize collecting information about highly nutritious foods. For example, if the user is energetic, the data collection unit will prioritize collecting information about new recipes and ingredients. By prioritizing the information to collect based on the user's emotions, more appropriate information can be collected. 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.

[0079] The data collection unit can prioritize the collection of highly relevant information by considering the user's geographical location. For example, the data collection unit can collect information about groceries available at nearby supermarkets based on the user's current location. For example, the data collection unit can collect information about local specialties and seasonal ingredients based on the user's geographical location. For example, the data collection unit can collect information about online shops that offer delivery, taking the user's geographical location into consideration. By prioritizing the collection of highly relevant information while considering the user's geographical location, more appropriate information can be collected.

[0080] The data collection unit analyzes the user's social media activity during information gathering and can collect relevant information. For example, the data collection unit collects information about recipes and ingredients that the user has shared on social media. For example, the data collection unit collects information about ingredients and recipes recommended by the user's social media followers. For example, the data collection unit analyzes the content of the user's social media posts and collects information about ingredients and recipes that the user is interested in. In this way, relevant information can be collected by analyzing the user's social media activity.

[0081] The suggestion function can estimate the user's emotions and adjust the way suggestions are presented based on those emotions. For example, if the user is stressed, the suggestion function might suggest a recipe using ingredients that have a relaxing effect. If the user is tired, for example, the suggestion function might suggest an easy-to-make, nutritious recipe. If the user is energetic, for example, the suggestion function might suggest a new, challenging recipe. By adjusting the way suggestions are presented based on the user's emotions, more appropriate suggestions can be made. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0082] The proposal department can adjust the level of detail in its proposals based on the importance of the ingredients. For example, it can propose detailed cooking methods for major ingredients. For example, it can propose simplified cooking methods for auxiliary ingredients. For example, it can make proposals that take into account cooking time and effort depending on the importance of the ingredients. By adjusting the level of detail in proposals based on the importance of the ingredients, it can make more appropriate proposals.

[0083] The suggestion function can apply different suggestion algorithms depending on the food category when making suggestions. For example, for vegetables, the suggestion function will make suggestions that prioritize nutritional value. For meats, the suggestion function will make suggestions that prioritize cooking methods and seasonings. For seafood, the suggestion function will make suggestions that prioritize freshness and preservation methods. By applying different suggestion algorithms depending on the food category, the function can make more appropriate suggestions.

[0084] The suggestion function can estimate the user's emotions and adjust the length of the suggestions based on those emotions. For example, if the user is in a hurry, the suggestion function will provide short, concise suggestions. If the user is relaxed, the suggestion function will provide longer suggestions with detailed explanations. If the user is excited, the suggestion function will provide visually stimulating suggestions. By adjusting the length of suggestions based on the user's emotions, more appropriate suggestions can be provided. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0085] The proposal department can prioritize proposals based on the availability of ingredients. For example, the department might propose recipes that prioritize using seasonal ingredients, recipes that prioritize using ingredients nearing their expiration date, or recipes that avoid hard-to-find ingredients and use readily available ones. By prioritizing proposals based on the availability of ingredients, the department can make more appropriate suggestions.

[0086] The suggestion function can adjust the order of suggestions based on the relationships between ingredients. For example, it can suggest side dishes related to a main dish. For example, it can suggest multiple recipes using the same ingredients. For example, it can suggest a balanced menu considering combinations of ingredients. By adjusting the order of suggestions based on the relationships between ingredients, it can provide more appropriate suggestions.

[0087] The advice unit can estimate the user's emotions and adjust the way it expresses advice based on those emotions. For example, if the user is stressed, the advice unit can provide advice using ingredients that have a relaxing effect. If the user is tired, the advice unit can provide advice on easy-to-prepare, nutritious meals. If the user is feeling energetic, the advice unit can provide advice on new and challenging activities. By adjusting the way advice is expressed based on the user's emotions, more appropriate advice can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0088] The advice unit can provide optimal advice by referring to the user's past health data. For example, the advice unit can provide advice that considers nutritional balance based on the user's past health data. For example, the advice unit can provide advice to avoid foods that the user is allergic to based on the user's past health data. For example, the advice unit can provide advice to consume specific nutrients by referring to the user's past health data. In this way, by referring to the user's past health data, it can provide optimal advice.

[0089] The advice function can customize the advice given based on the user's current lifestyle. For example, if the user is busy, it will suggest recipes that can be made in a short amount of time. If the user is relaxed, it will suggest recipes that can be made over a longer period of time. If the user is participating in a specific event, it will suggest recipes suitable for that event. By customizing the advice based on the user's current lifestyle, it can provide more appropriate advice.

[0090] The advice unit can estimate the user's emotions and prioritize advice based on those emotions. For example, if the user is stressed, the advice unit will prioritize providing relaxing advice. If the user is tired, the advice unit will prioritize providing nutritious advice. If the user is energetic, the advice unit will prioritize providing new and challenging advice. By prioritizing advice based on the user's emotions, more appropriate advice can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0091] The advice function can provide optimal advice by taking into account the user's geographical location. For example, the advice function can provide advice using ingredients available at nearby supermarkets based on the user's current location. For example, the advice function can provide advice using local specialties or seasonal ingredients based on the user's geographical location. For example, the advice function can provide information on online shops that can deliver, taking into account the user's geographical location. By providing optimal advice while considering the user's geographical location, it is possible to provide more appropriate advice.

[0092] The advice function can analyze the user's social media activity and adjust the content of the advice accordingly. For example, the advice function can provide advice on recipes and ingredients that the user has shared on social media. For example, the advice function can provide advice on ingredients and recipes recommended by the user's social media followers. For example, the advice function can analyze the user's social media posts and provide advice on ingredients and recipes that the user is interested in. In this way, by analyzing the user's social media activity, it is possible to provide more appropriate advice.

[0093] The ordering system can estimate the user's emotions and adjust the timing of the order based on those emotions. For example, if the user is stressed, the ordering system will place the order during a relaxed time. If the user is busy, the ordering system will adjust the order to be completed quickly. If the user is relaxed, the ordering system will take time to review the detailed order. By adjusting the timing of the order based on the user's emotions, orders can be placed at a more appropriate time. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0094] The ordering system can select the optimal ordering method by referring to the user's past purchase history when an order is placed. For example, the ordering system can prioritize ordering related ingredients based on the ingredients the user has purchased in the past. For example, the ordering system can place orders that avoid ingredients the user is allergic to, based on the user's past purchase history. For example, the ordering system can analyze the user's past purchase history and place orders that take nutritional balance into consideration. In this way, the system can select the optimal ordering method by referring to the user's past purchase history.

[0095] The ordering system can customize orders based on the user's current lifestyle. For example, if the user is busy, the system will prioritize ordering ingredients that can be cooked quickly. If the user is relaxed, the system will prioritize ordering ingredients that can be cooked over a longer period of time. If the user is attending a specific event, the system will order ingredients suitable for that event. By customizing orders based on the user's current lifestyle, the system can make more appropriate orders.

[0096] The ordering system can estimate the user's emotions and prioritize orders based on those emotions. For example, if the user is stressed, the system will prioritize ordering ingredients with relaxing effects. If the user is tired, the system will prioritize ordering ingredients with high nutritional value. If the user is energetic, the system will prioritize ordering new and challenging ingredients. By prioritizing orders based on the user's emotions, the system can make more appropriate orders. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0097] The ordering system can select the most suitable ordering method when an order is placed, taking into account the user's geographical location. For example, the ordering system can prioritize ordering ingredients available at nearby supermarkets based on the user's current location. For example, the ordering system can prioritize ordering local specialties and seasonal ingredients based on the user's geographical location. For example, the ordering system can order from online shops that can deliver, taking into account the user's geographical location. By selecting the most suitable ordering method considering the user's geographical location, more appropriate orders can be made.

[0098] The ordering department can analyze the user's social media activity and adjust the order content at the time of ordering. For example, the ordering department can place orders based on ingredients and recipes that the user has shared on social media. For example, the ordering department can place orders based on ingredients and recipes recommended by the user's social media followers. For example, the ordering department can analyze the user's social media posts and place orders based on ingredients and recipes that the user is interested in. In this way, by analyzing the user's social media activity, more appropriate orders can be placed.

[0099] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0100] The data collection unit can estimate the user's emotions and adjust the timing of information collection based on the estimated emotions. For example, if the user is stressed, the data collection unit will collect information during a relaxed time. If the user is busy, the data collection unit will adjust to complete the information collection in a short amount of time. If the user is relaxed, the data collection unit will take more time to collect detailed information. By adjusting the timing of information collection based on the user's emotions, information can be collected at a more appropriate time. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0101] The data collection unit can analyze the user's past eating history and select the optimal information collection method. For example, the data collection unit can prioritize collecting relevant information based on the dishes the user has enjoyed eating in the past. For example, the data collection unit can collect information from the user's past eating history to help avoid allergenic ingredients. For example, the data collection unit can analyze the user's past eating history and collect information that takes nutritional balance into consideration. In this way, by analyzing the user's past eating history, the optimal information collection method can be selected.

[0102] The data collection unit can collect information while considering the freshness and expiration date of ingredients when checking the inventory status of ingredients. For example, the data collection unit can check the freshness of ingredients in the refrigerator and collect information to prioritize the use of fresh ingredients. For example, the data collection unit can collect information to prioritize the use of ingredients that are nearing their expiration date. For example, the data collection unit can collect information on the optimal storage method, taking into account the freshness and expiration date of ingredients. By collecting information while considering the freshness and expiration date of ingredients, more appropriate information can be collected.

[0103] The suggestion function can estimate the user's emotions and adjust the way suggestions are presented based on those emotions. For example, if the user is stressed, the suggestion function might suggest a recipe using ingredients that have a relaxing effect. If the user is tired, for example, the suggestion function might suggest an easy-to-make, nutritious recipe. If the user is energetic, for example, the suggestion function might suggest a new, challenging recipe. By adjusting the way suggestions are presented based on the user's emotions, more appropriate suggestions can be made. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0104] The proposal department can adjust the level of detail in its proposals based on the importance of the ingredients. For example, it can propose detailed cooking methods for major ingredients. For example, it can propose simplified cooking methods for auxiliary ingredients. For example, it can make proposals that take into account cooking time and effort depending on the importance of the ingredients. By adjusting the level of detail in proposals based on the importance of the ingredients, it can make more appropriate proposals.

[0105] The suggestion function can apply different suggestion algorithms depending on the food category when making suggestions. For example, for vegetables, the suggestion function will make suggestions that prioritize nutritional value. For meats, the suggestion function will make suggestions that prioritize cooking methods and seasonings. For seafood, the suggestion function will make suggestions that prioritize freshness and preservation methods. By applying different suggestion algorithms depending on the food category, the function can make more appropriate suggestions.

[0106] The suggestion function can estimate the user's emotions and adjust the length of the suggestions based on those emotions. For example, if the user is in a hurry, the suggestion function will provide short, concise suggestions. If the user is relaxed, the suggestion function will provide longer suggestions with detailed explanations. If the user is excited, the suggestion function will provide visually stimulating suggestions. By adjusting the length of suggestions based on the user's emotions, more appropriate suggestions can be provided. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0107] The proposal department can prioritize proposals based on the availability of ingredients. For example, the department might propose recipes that prioritize using seasonal ingredients, recipes that prioritize using ingredients nearing their expiration date, or recipes that avoid hard-to-find ingredients and use readily available ones. By prioritizing proposals based on the availability of ingredients, the department can make more appropriate suggestions.

[0108] The suggestion function can adjust the order of suggestions based on the relationships between ingredients. For example, it can suggest side dishes related to a main dish. For example, it can suggest multiple recipes using the same ingredients. For example, it can suggest a balanced menu considering combinations of ingredients. By adjusting the order of suggestions based on the relationships between ingredients, it can provide more appropriate suggestions.

[0109] The advice unit can estimate the user's emotions and adjust the way it expresses advice based on those emotions. For example, if the user is stressed, the advice unit can provide advice using ingredients that have a relaxing effect. If the user is tired, the advice unit can provide advice on easy-to-prepare, nutritious meals. If the user is feeling energetic, the advice unit can provide advice on new and challenging activities. By adjusting the way advice is expressed based on the user's emotions, more appropriate advice can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0110] The following briefly describes the processing flow for example form 2.

[0111] Step 1: The data collection unit collects information such as the user's preferences, allergies, health status, and food inventory. For example, it collects user information by analyzing information entered by the user or by analyzing image data of food items taken by a camera attached to the refrigerator. Step 2: The suggestion department proposes the optimal menu based on the information collected by the data collection department. For example, it proposes a balanced meal considering the user's preferences, allergies, and health condition, and proposes a menu that makes the most efficient use of ingredients by checking the inventory status of the refrigerator. Step 3: The advice unit provides real-time advice based on nutritional balance and health status, using the menu proposed by the suggestion unit. For example, when a user eats a meal, the unit analyzes the nutritional balance of that meal and provides advice on supplementing necessary nutrients. Step 4: The ordering department automatically orders the necessary ingredients online based on the advice provided by the advice department. For example, if the refrigerator is running low on ingredients, it will automatically order the necessary ingredients online.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] Each of the multiple elements described above, including the data collection unit, proposal unit, advice unit, and ordering unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit collects user information using the camera 42 and microphone 38B of the smart device 14 and analyzes it using the control unit 46A. The proposal unit is implemented in the specific processing unit 290 of the data processing unit 12 and proposes an optimal menu based on the collected information. The advice unit is implemented in the specific processing unit 290 of the data processing unit 12 and provides real-time advice based on nutritional balance and health status. The ordering unit is implemented in the control unit 46A of the smart device 14 and automatically orders the necessary ingredients online. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0116] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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).

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.).

[0128] 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.

[0129] 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.

[0130] 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.

[0131] Each of the multiple elements described above, including the data collection unit, proposal unit, advice unit, and ordering unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit collects user information using the camera 42 and microphone 238 of the smart glasses 214 and analyzes it using the control unit 46A. The proposal unit is implemented in the specific processing unit 290 of the data processing unit 12 and proposes an optimal menu based on the collected information. The advice unit is implemented in the specific processing unit 290 of the data processing unit 12 and provides real-time advice based on nutritional balance and health status. The ordering unit is implemented in the control unit 46A of the smart glasses 214 and automatically orders the necessary ingredients online. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0132] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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).

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.).

[0144] 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.

[0145] 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.

[0146] 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.

[0147] Each of the multiple elements described above, including the data collection unit, proposal unit, advice unit, and ordering unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit collects user information using the camera 42 and microphone 238 of the headset terminal 314 and analyzes it using the control unit 46A. The proposal unit is implemented in the specific processing unit 290 of the data processing unit 12 and proposes an optimal menu based on the collected information. The advice unit is implemented in the specific processing unit 290 of the data processing unit 12 and provides real-time advice based on nutritional balance and health status. The ordering unit is implemented in the control unit 46A of the headset terminal 314 and automatically orders the necessary ingredients online. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0148] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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).

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.).

[0161] 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.

[0162] 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.

[0163] 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.

[0164] Each of the multiple elements described above, including the data collection unit, proposal unit, advice unit, and ordering unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the data collection unit collects user information using the camera 42 and microphone 238 of the robot 414 and analyzes it with the control unit 46A. The proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and proposes an optimal menu based on the collected information. The advice unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and provides advice in real time based on nutritional balance and health status. The ordering unit is implemented by, for example, the control unit 46A of the robot 414 and automatically orders the necessary ingredients online. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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."

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] (Note 1) A data collection unit that collects information such as user preferences, allergies, health status, and ingredient inventory, A proposal unit that proposes the optimal menu based on the information collected by the aforementioned collection unit, An advice unit provides real-time advice based on nutritional balance and health status, based on the menu proposed by the aforementioned proposal unit. The system includes an ordering unit that automatically orders the necessary ingredients online based on the advice provided by the aforementioned advice unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is Use an external camera attached to the refrigerator to check the inventory status of food items and use them efficiently without waste. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned proposal section is, We suggest balanced meals that take into account the user's preferences, allergies, and health condition. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned advice section, When a user eats, the system analyzes the nutritional balance of that meal and provides advice on how to supplement necessary nutrients. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned advice section, Based on the user's health condition, advise them to consume specific nutrients. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned ordering section is, When the refrigerator is running low on ingredients, the system automatically orders the necessary groceries online. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is It estimates the user's emotions and adjusts the timing of information collection based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is Analyze the user's past meal history and select the optimal method for collecting information. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When checking the inventory status of ingredients, information should be collected while considering the freshness and expiration date of the ingredients. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is It estimates the user's emotions and prioritizes the information to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting information, the system prioritizes collecting highly relevant information by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is When gathering information, we analyze users' social media activity and collect relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned proposal section is, When making a proposal, adjust the level of detail based on the importance of the ingredients. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned proposal section is, When making suggestions, different suggestion algorithms are applied depending on the category of ingredients. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned proposal section is, It estimates the user's emotions and adjusts the length of the suggestion based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned proposal section is, When making proposals, prioritize them based on when the ingredients are available. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned proposal section is, When making suggestions, adjust the order of suggestions based on the relationships between the ingredients. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned advice section, It estimates the user's emotions and adjusts the way advice is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned advice section, When providing advice, we refer to the user's past health data to provide the most suitable advice. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned advice section, When providing advice, the advice is customized based on the user's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned advice section, It estimates the user's emotions and prioritizes advice based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned advice section, When providing advice, we take the user's geographical location into consideration to provide the most appropriate advice. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned advice section, When providing advice, we analyze the user's social media activity and adjust the content of the advice accordingly. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned ordering section is, It estimates the user's emotions and adjusts the timing of orders based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned ordering section is, When an order is placed, the system selects the most suitable ordering method by referring to the user's past purchase history. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned ordering section is, When placing an order, the order contents are customized based on the user's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned ordering section is, It estimates the user's emotions and determines order priorities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned ordering section is, When an order is placed, the system selects the optimal ordering method by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned ordering section is, When an order is placed, we analyze the user's social media activity and adjust the order accordingly. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0184] 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 data collection unit that collects information such as user preferences, allergies, health status, and ingredient inventory, A proposal unit that proposes the optimal menu based on the information collected by the aforementioned collection unit, An advice unit provides real-time advice based on nutritional balance and health status, based on the menu proposed by the aforementioned proposal unit. The system includes an ordering unit that automatically orders the necessary ingredients online based on the advice provided by the aforementioned advice unit. A system characterized by the following features.

2. The aforementioned collection unit is Use an external camera attached to the refrigerator to check the inventory status of food items and use them efficiently without waste. The system according to feature 1.

3. The aforementioned proposal section is, We suggest balanced meals that take into account the user's preferences, allergies, and health condition. The system according to feature 1.

4. The aforementioned advice section, When a user eats, the system analyzes the nutritional balance of that meal and provides advice on how to supplement necessary nutrients. The system according to feature 1.

5. The aforementioned advice section, Based on the user's health condition, advise them to consume specific nutrients. The system according to feature 1.

6. The aforementioned ordering section is, When the refrigerator is running low on ingredients, the system automatically orders the necessary groceries online. The system according to feature 1.

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

8. The aforementioned collection unit is Analyze the user's past meal history and select the optimal method for collecting information. The system according to feature 1.

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