System

Abstract

An object of a system according to an embodiment is to predict a meal time, automatically perform cooking, and improve the efficiency of management and procurement of food ingredients.SOLUTION: The system includes a AI pan, a refrigerator, and a generation AI. The AI pot cooks foods. The refrigerator grasps the state of the food. The generation AI proposes a menu from the state of the refrigerator, and performs a network order when foods are insufficient.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technology lacks a system that can predict meal times and automatically prepare meals, making it difficult to manage and procure ingredients.

[0005] The system according to the embodiment aims to predict meal times, automatically cook meals, and improve the efficiency of food ingredient management and procurement. [Means for solving the problem]

[0006] The system according to the embodiment includes an AI cooker, a refrigerator, and a generation AI. The AI ​​cooker cooks food. The refrigerator monitors the food status. The generation AI suggests a menu based on the refrigerator status and places an online order if food is in short supply. [Effects of the Invention]

[0007] The system according to the embodiment can predict meal times, automatically cook meals, and streamline the management and procurement of ingredients. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

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

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple 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), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

[0017] 1, a 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also 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 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0024] 2, in the data processing device 12, a specific process 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" according to the technology of the present 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 process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may 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 a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The automatic cooking system according to an embodiment of the present invention achieves automatic cooking by incorporating AI into an AI pot that cooks food and a refrigerator. This system has the ability to predict meal times based on daily smartphone usage and automatically prepare meals. Menus can be selected from suggestions based on the refrigerator's current status, and a desired menu can be served. Furthermore, if there is a shortage of food in the refrigerator, the system also has the ability to order food online and share a list of necessary foods with the smartphone to instruct shopping. This reduces the burden on users, allowing them to enjoy meals efficiently and conveniently.

[0029] An automatic cooking system according to an embodiment includes an AI cooker, a refrigerator, a smartphone, and a generation AI. The AI ​​cooker cooks food. For example, the AI ​​cooker uses a temperature control function to cook food at the appropriate temperature. The AI ​​cooker can execute a cooking program to automatically prepare specific dishes. The AI ​​cooker can monitor the status of food and adjust the cooking progress using sensor technology. The refrigerator monitors the status of food. For example, the refrigerator can detect the type and quantity of food using sensors. The refrigerator can monitor the freshness of food and identify food that has lost its freshness. The refrigerator can optimize food placement and suggest rearrangements that take into account ease of access. The smartphone predicts meal times based on daily usage. For example, the smartphone analyzes app usage time and location information to identify the user's meal times. The smartphone can also link with a calendar app to adjust meal times based on schedules. The smartphone can also analyze location information and predict meal times based on the user's movement patterns. The generation AI suggests menus based on the status of the refrigerator and places online orders if food is running low. For example, the generation AI can analyze the status of food in a refrigerator and propose an appropriate menu. The generation AI can also analyze the nutritional value of food and propose a menu that takes nutritional balance into consideration. The generation AI can also manage food allergen information and propose a menu suitable for users with allergies. As a result, the automatic cooking system according to the embodiment reduces the burden on users and allows them to enjoy meals efficiently and conveniently.

[0030] The refrigerator monitors the freshness of food in real time, and the generation AI can generate cooking plans that prioritize using foods that have lost their freshness. For example, sensors inside the refrigerator monitor the freshness of food in real time and detect foods that have lost their freshness. The generation AI uses this information to generate cooking plans that prioritize using foods that have lost their freshness. For example, it might suggest soups or stir-fries using vegetables that have lost their freshness. The refrigerator also analyzes the freshness data of the food in the refrigerator and identifies foods that have lost their freshness. The generation AI uses this data to suggest recipes that use foods that have lost their freshness. For example, it might suggest stews or grilled dishes using fish that has lost their freshness. Furthermore, if the sensor that monitors the freshness of food in the refrigerator detects foods that have lost their freshness, it sends that information to the generation AI. The generation AI uses this information to generate cooking plans that use foods that have lost their freshness and suggests them to the user. This reduces food waste and enables more efficient cooking.

[0031] The refrigerator optimizes the placement of food items, and the generative AI can suggest rearrangements that take into account ease of access. For example, sensors inside the refrigerator monitor the placement of food items and suggest optimal placement that takes into account ease of access. The generative AI uses that information to generate a plan to change the placement of food items. For example, it suggests placing frequently used foods at the front. The refrigerator also analyzes data on the placement of food items inside the refrigerator and identifies the optimal placement that takes into account ease of access. The generative AI uses that data to suggest changes to the placement of food items. For example, it suggests placing heavy foods on the bottom shelves and light foods on the top shelves. The refrigerator also monitors the placement of food items using sensors and suggests rearrangements that take into account ease of access. The generative AI uses that information to generate a plan to optimize the placement of food items and suggest it to the user. This improves the ease of access to food and enables efficient use.

[0032] The generation AI can analyze the nutritional value of food in the refrigerator and suggest menus that take nutritional balance into consideration. For example, sensors in the refrigerator analyze the nutritional value of food and send that information to the generation AI. The generation AI uses that data to suggest menus that take nutritional balance into consideration. For example, if there is a vitamin deficiency, it will suggest dishes that are rich in vitamins. The generation AI can also analyze the nutritional value data of food in the refrigerator and generate menus that take nutritional balance into consideration. The generation AI uses that data to suggest nutritionally balanced dishes. For example, if there is a protein deficiency, it will suggest dishes that use meat or fish. The generation AI can also analyze the nutritional value of food using sensors in the refrigerator and send that information to the generation AI. The generation AI uses that data to create menus that take nutritional balance into consideration and suggest them to the user. This helps provide nutritionally balanced meals and supports health.

[0033] The generation AI manages allergen information for foods in the refrigerator and can suggest menus suitable for users with allergies. For example, sensors in the refrigerator analyze food allergen information and send that information to the generation AI. The generation AI uses that data to suggest menus suitable for users with allergies. For example, it would suggest dairy-free dishes to a user with a dairy allergy. The generation AI also manages allergen information for foods in the refrigerator and generates menus based on that information. The generation AI uses that data to suggest dishes suitable for users with allergies. For example, it would suggest nut-free dishes to a user with a nut allergy. The generation AI also analyzes food allergen information using sensors in the refrigerator and sends that information to the generation AI. The generation AI uses that data to generate menus suitable for users with allergies and suggest them to the user. This provides safe meals to users with allergies.

[0034] The generation AI can analyze smartphone location information and predict meal times based on the user's movement patterns. For example, the generation AI analyzes smartphone location information to identify the user's movement patterns. Based on that data, the generation AI predicts the time of day when the user is likely to eat. For example, if the user is in a specific location at the same time every day, the generation AI can suggest meals for that time of day. The generation AI also analyzes smartphone location information and learns the user's movement patterns. Based on that data, the generation AI predicts the user's meal times. For example, it predicts meal times based on the time the user frequently visits a specific restaurant. The generation AI also analyzes smartphone location information and identifies the user's movement patterns. Based on that data, the generation AI predicts the user's meal times and suggests meals at appropriate times. For example, it predicts meal times based on the time the user returns home. This makes it possible to predict meal times based on the user's movement patterns and provide meals at appropriate times.

[0035] The generation AI can work in conjunction with a smartwatch or fitness tracker to suggest meal times that take into account the user's health condition. For example, the generation AI works in conjunction with a smartwatch or fitness tracker to analyze the user's health condition. Based on that data, the generation AI suggests meal times that suit the user's health condition. For example, it suggests eating at an appropriate time after exercise. The generation AI also analyzes fitness tracker data to predict meal times that take the user's health condition into consideration. Based on that data, the generation AI suggests the healthiest time for the user to eat meals. For example, it suggests a breakfast time based on sleep data. The generation AI also works in conjunction with a smartwatch or fitness tracker to analyze the user's health condition. Based on that data, the generation AI suggests meal times that suit the user's health condition and provides meals at appropriate times. For example, it adjusts meal times based on heart rate and calorie consumption. This makes it possible to suggest meal times that suit the user's health condition and provide healthy meals.

[0036] The generative AI can work in conjunction with smart home devices to suggest meal times that take into account the schedules of all family members. For example, the generative AI can work in conjunction with smart home devices to analyze the schedules of all family members. Based on that data, the generative AI can suggest times when the whole family can eat together. For example, it can suggest eating at a time when all family members are together. The generative AI can also analyze data from smart home devices to predict meal times that take into account the schedules of all family members. Based on that data, the generative AI can suggest times when the whole family can eat together. For example, it can suggest eating at a time when all family members' schedules do not overlap. The generative AI can also work in conjunction with smart home devices to analyze the schedules of all family members. Based on that data, the generative AI can suggest times when the whole family can eat together and serve the meal at an appropriate time. For example, it can suggest eating at a time when all family members are relaxed. This allows the generative AI to suggest meal times that fit the schedules of all family members, allowing the whole family to enjoy meals together.

[0037] The generation AI can analyze past meal history and suggest menus that reflect the user's preferences and eating habits. For example, the generation AI analyzes past meal history to identify the user's preferences and eating habits. Based on that data, the generation AI suggests dishes that the user likes. For example, it suggests menus based on dishes the user eats frequently. The generation AI also analyzes meal history data to generate menus that reflect the user's eating habits. Based on that data, the generation AI suggests dishes that the user likes. For example, it suggests dishes that use the user's favorite ingredients. The generation AI also analyzes past meal history to identify the user's preferences and eating habits. Based on that data, the generation AI suggests dishes that the user likes and provides an appropriate menu. For example, if the user likes a particular dish, it suggests that dish. This suggests menus based on the user's preferences and eating habits, improving satisfaction.

[0038] The generation AI can propose menus that reflect the season and weather, and provide dishes that reflect the season. For example, the generation AI analyzes seasonal and weather data and proposes menus based on that information. The generation AI then proposes dishes that reflect the season based on that data. For example, it proposes cold dishes in summer and hot dishes in winter. The generation AI also generates menus that reflect the season and weather, and proposes them to the user. The generation AI then proposes dishes that reflect the season based on that data. For example, it proposes dishes using seasonal vegetables in spring and dishes using mushrooms in autumn. The generation AI also analyzes seasonal and weather data, and proposes menus based on that information. The generation AI then proposes dishes that reflect the season based on that data, and provides an appropriate menu. For example, it proposes hot soup on a rainy day and salad on a sunny day. This allows the user to enjoy dishes that reflect the season and weather, allowing them to enjoy the feeling of the season.

[0039] The generation AI can propose menus that take into account the ratings and reviews of other users and provide popular dishes. For example, the generation AI analyzes the ratings and reviews of other users and proposes menus based on that information. The generation AI proposes popular dishes based on that data. For example, it prioritizes proposing highly rated dishes. The generation AI also analyzes the reviews of other users and generates menus based on that information. The generation AI proposes popular dishes based on that data. For example, it proposes dishes with a large number of reviews. The generation AI also analyzes the ratings and reviews of other users and proposes menus based on that information. The generation AI proposes popular dishes based on that data and provides appropriate menus. For example, it proposes dishes that are highly rated by the user. This provides popular dishes that take into account the ratings and reviews of other users, improving satisfaction.

[0040] The generation AI can propose menus that take into account information about the origins and producers of ingredients, promoting local production and consumption. For example, the generation AI can analyze information about the origins and producers of ingredients and propose menus based on that information. The generation AI can then propose dishes that promote local production and consumption based on that data. For example, it can propose dishes that use local ingredients. The generation AI can also analyze information about the origins and producers of ingredients and generate menus based on that information. The generation AI can then propose dishes that promote local production and consumption based on that data. For example, it can propose dishes that use local agricultural products. The generation AI can also analyze information about the origins and producers of ingredients and propose menus based on that information. The generation AI can then propose dishes that promote local production and consumption based on that data, providing an appropriate menu. For example, it can propose dishes that use fish caught by local fishermen. This can promote local production and consumption and support the local economy.

[0041] When ordering online, the generation AI can suggest the optimal delivery time and deliver food that fits the user's schedule. For example, when ordering online, the generation AI analyzes the user's schedule and suggests the optimal delivery time. Based on that data, the generation AI suggests delivery at a time when it is convenient for the user to receive the food. For example, it suggests delivery when the user is at home. The generation AI also analyzes the user's schedule and suggests the optimal delivery time based on that information. Based on that data, the generation AI suggests delivery at a time when it is convenient for the user to receive the food. For example, it suggests delivery when the user is home from work. The generation AI also analyzes the user's schedule and suggests the optimal delivery time when ordering online. Based on that data, the generation AI suggests delivery at a time when it is convenient for the user to receive the food, and delivers food at the appropriate time. For example, it suggests delivery when the user is at home on a holiday. This enables delivery to fit the user's schedule and improves convenience.

[0042] Generative AI can automatically apply discounts and coupon information when placing an online order, reducing costs. For example, generative AI can build a system that automatically applies discounts and coupon information when placing an online order. Based on that data, generative AI can apply the most appropriate discounts and coupons, reducing costs. For example, it can automatically apply discount coupons for specific products. Generative AI can also analyze discount and coupon information and automatically apply it when placing an online order based on that information. Based on that data, generative AI can apply the most appropriate discounts and coupons, reducing costs. For example, it can apply discounts when purchasing multiple products together. Generative AI can also build a system that automatically applies discounts and coupon information when placing an online order. Based on that data, generative AI can apply the most appropriate discounts and coupons, reducing costs. For example, it can apply discounts when ordering during a specific time period. This allows discounts and coupon information to be applied automatically, reducing costs.

[0043] Generative AI can suggest eco-friendly options when ordering online, promoting environmentally conscious shopping. For example, generative AI could build a system that suggests eco-friendly options when ordering online. Based on that data, generative AI would suggest environmentally conscious products. For example, it could suggest products that use recyclable packaging. Generative AI could also analyze eco-friendly options and use that information to make suggestions when ordering online. Based on that data, generative AI would suggest environmentally conscious products. For example, it could suggest locally produced foods. Generative AI could also build a system that suggests eco-friendly options when ordering online. Based on that data, generative AI would suggest environmentally conscious products and provide them to users. For example, it could suggest organic foods and fair trade products. This would promote environmentally conscious shopping and support sustainable consumption.

[0044] Generative AI can suggest options to purchase directly from local producers when placing an online order, thereby supporting the local economy. For example, generative AI builds a system that suggests options to purchase directly from local producers when placing an online order. Based on that data, generative AI suggests products from local producers. For example, it might suggest vegetables produced by local farmers. Generative AI also analyzes options to purchase directly from local producers and makes suggestions based on that information when placing an online order. Based on that data, generative AI suggests products that support the local economy. For example, it might suggest fish caught by local fishermen. Based on that data, generative AI builds a system that suggests options to purchase directly from local producers when placing an online order. Based on that data, generative AI suggests products from local producers and provides them to the user. For example, it might suggest dairy products produced by a local farm. In this way, purchasing directly from local producers supports the local economy.

[0045] The generation AI can add a function to the shopping list that prioritizes items based on purchase frequency and expiration date. For example, the generation AI adds a function to the shopping list that prioritizes items based on purchase frequency and expiration date. The generation AI then generates a prioritized shopping list based on that data. For example, foods with an approaching expiration date are displayed first on the list. The generation AI also analyzes purchase frequency and expiration date, and prioritizes the shopping list based on that information. The generation AI then generates a prioritized shopping list based on that data. For example, foods that are purchased frequently are displayed at the top of the list. The generation AI also adds a function to the shopping list that prioritizes items based on purchase frequency and expiration date. The generation AI then generates a prioritized shopping list based on that data and provides it to the user. For example, foods with an approaching expiration date are displayed at the top of the list. This enables efficient shopping by prioritizing items based on purchase frequency and expiration date.

[0046] The generative AI can add suggested substitutes to the shopping list, providing appropriate options even when an item is out of stock. For example, the generative AI builds a function to add suggested substitutes to the shopping list. Based on that data, the generative AI suggests appropriate substitutes even when an item is out of stock. For example, if a specific brand of milk is out of stock, it will suggest other brands of milk. The generative AI also analyzes out-of-stock information and suggests substitutes based on that information. The generative AI also builds a function to add suggested substitutes to the shopping list. Based on that data, the generative AI suggests appropriate substitutes even when an item is out of stock, and provides them to the user. For example, if a specific meat is out of stock, it will suggest other meats that can be cooked in the same way. This allows appropriate substitutes to be suggested even when an item is out of stock, improving shopping convenience.

[0047] The generative AI can link recipe information to a shopping list and suggest dishes using purchased foods. For example, the generative AI adds a function to link recipe information to a shopping list. The generative AI then uses that data to suggest dishes using purchased foods. For example, it suggests recipes using foods added to the list. The generative AI also analyzes recipe information and links it to a shopping list based on that information. The generative AI then uses that data to suggest dishes using purchased foods. For example, it suggests recipes using foods added to the list. The generative AI also adds a function to link recipe information to a shopping list and builds a system that suggests dishes using purchased foods. The generative AI then uses that data to suggest dishes using purchased foods and provide them to the user. For example, it suggests recipes using foods added to the list. This makes it possible to suggest dishes using purchased foods and use ingredients efficiently.

[0048] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0049] Generative AI can not only predict a user's meal times, but also suggest meal times that take into account the user's health condition. For example, by linking with a smartwatch or fitness tracker and analyzing the user's exercise and sleep data, it can suggest optimal meal times. It can suggest the appropriate time to eat after exercise or a dinner time to improve sleep quality. It can also adjust meal times to supplement specific nutrients based on the user's health condition. This can support the user's health and provide more effective meal timings.

[0050] Generative AI can not only monitor the freshness of food in the refrigerator, but also propose cooking plans that take food expiration dates into consideration. For example, it can propose recipes that prioritize the use of foods with an approaching expiration date, thereby reducing food waste. In addition, by increasing the variety of dishes using foods with an approaching expiration date, users can enjoy meals without getting bored. It can also suggest ways to make large quantities of dishes using foods with an approaching expiration date at once and freeze them. This reduces food waste and enables efficient use of ingredients.

[0051] The generative AI not only optimizes the placement of food in the refrigerator, but can also suggest changes to the placement that take into account the user's height and dominant hand. For example, placing frequently used foods in a location that is easy for the user to access improves convenience. It is also possible to suggest placing food at a height that makes it easy to access based on the user's height. Furthermore, adjusting the placement of food in the refrigerator to suit the user's dominant hand can create a refrigerator that is easier to use. This improves user convenience and enables efficient food access.

[0052] Generative AI can analyze the nutritional value of food in the refrigerator and suggest menus that take nutritional balance into consideration, as well as suggest meal plans tailored to the user's health goals. For example, it can suggest low-calorie dishes to a user on a diet, and high-protein dishes to a user looking to build muscle. It can also suggest dishes to supplement a specific nutrient if the user is lacking in that nutrient. Furthermore, it can support health by suggesting meal plans that actively incorporate specific ingredients based on the user's health condition. This allows for the provision of meal plans tailored to the user's health goals, enabling effective health management.

[0053] The generation AI manages allergen information for food in the refrigerator and not only suggests menus suitable for users with allergies, but also suggests alternative ingredients that do not contain allergens. For example, it can suggest dishes that use alternative ingredients that do not contain dairy products to users with dairy allergies. It can also suggest dishes that use alternative ingredients that do not contain nuts to users with nut allergies. Furthermore, by increasing the variety of dishes that use alternative ingredients that do not contain allergens, users can enjoy their meals without getting bored. This makes it possible to provide safe and diverse meals to users with allergies.

[0054] The generative AI not only analyzes the smartphone's location information and predicts meal times based on the user's movement patterns, but also makes meal suggestions based on the user's destination. For example, if a user frequently visits a particular restaurant, it can suggest meals based on that restaurant's menu. Also, if the user is traveling, it can suggest local cuisine from the destination. Furthermore, by analyzing the user's movement patterns and adjusting meal times based on that data, the system can ensure that the user eats at appropriate times even while on the move. This makes it possible to make meal suggestions based on the user's movement patterns, providing more convenient and appropriate meals.

[0055] The processing flow of the first embodiment will be briefly explained below.

[0056] Step 1: The AI ​​Cooker cooks the food. For example, the AI ​​Cooker uses temperature control to cook food at the right temperature and executes cooking programs to automatically create specific dishes. It can also use sensor technology to monitor the condition of the food and adjust the cooking progress. Step 2: The refrigerator monitors the food situation. For example, it uses sensors to detect the type and quantity of food, monitors the freshness of the food, and identifies foods that have lost their freshness. It can also optimize the placement of food and suggest rearranging it to make it easier to access. Step 3: The smartphone predicts meal times based on daily usage. For example, the smartphone can analyze app usage time and location information to identify the user's meal times. It can also link with a calendar app to adjust meal times based on schedules. It can also analyze location information and predict meal times based on the user's movement patterns. Step 4: The generation AI proposes a menu based on the refrigerator's status, and places an online order if food is in short supply. For example, the generation AI can analyze the status of food in the refrigerator and propose an appropriate menu. It can also analyze the nutritional value of food and propose a menu that takes nutritional balance into consideration. It can also manage food allergen information and propose a menu suitable for users with allergies.

[0057] (Example 2) The automatic cooking system according to an embodiment of the present invention achieves automatic cooking by incorporating AI into an AI pot that cooks food and a refrigerator. This system has the ability to predict meal times based on daily smartphone usage and automatically prepare meals. Menus can be selected from suggestions based on the refrigerator's current status, and a desired menu can be served. Furthermore, if there is a shortage of food in the refrigerator, the system also has the ability to order food online and share a list of necessary foods with the smartphone to instruct shopping. This reduces the burden on users, allowing them to enjoy meals efficiently and conveniently.

[0058] An automatic cooking system according to an embodiment includes an AI cooker, a refrigerator, a smartphone, and a generation AI. The AI ​​cooker cooks food. For example, the AI ​​cooker uses a temperature control function to cook food at the appropriate temperature. The AI ​​cooker can execute a cooking program to automatically prepare specific dishes. The AI ​​cooker can monitor the status of food and adjust the cooking progress using sensor technology. The refrigerator monitors the status of food. For example, the refrigerator can detect the type and quantity of food using sensors. The refrigerator can monitor the freshness of food and identify food that has lost its freshness. The refrigerator can optimize food placement and suggest rearrangements that take into account ease of access. The smartphone predicts meal times based on daily usage. For example, the smartphone analyzes app usage time and location information to identify the user's meal times. The smartphone can also link with a calendar app to adjust meal times based on schedules. The smartphone can also analyze location information and predict meal times based on the user's movement patterns. The generation AI suggests menus based on the status of the refrigerator and places online orders if food is running low. For example, the generation AI can analyze the status of food in a refrigerator and propose an appropriate menu. The generation AI can also analyze the nutritional value of food and propose a menu that takes nutritional balance into consideration. The generation AI can also manage food allergen information and propose a menu suitable for users with allergies. As a result, the automatic cooking system according to the embodiment reduces the burden on users and allows them to enjoy meals efficiently and conveniently.

[0059] The refrigerator monitors the freshness of food in real time, and the generation AI can generate cooking plans that prioritize using foods that have lost their freshness. For example, sensors inside the refrigerator monitor the freshness of food in real time and detect foods that have lost their freshness. The generation AI uses this information to generate cooking plans that prioritize using foods that have lost their freshness. For example, it might suggest soups or stir-fries using vegetables that have lost their freshness. The refrigerator also analyzes the freshness data of the food in the refrigerator and identifies foods that have lost their freshness. The generation AI uses this data to suggest recipes that use foods that have lost their freshness. For example, it might suggest stews or grilled dishes using fish that has lost their freshness. Furthermore, if the sensor that monitors the freshness of food in the refrigerator detects foods that have lost their freshness, it sends that information to the generation AI. The generation AI uses this information to generate cooking plans that use foods that have lost their freshness and suggests them to the user. This reduces food waste and enables more efficient cooking.

[0060] The refrigerator optimizes the placement of food items, and the generative AI can suggest rearrangements that take into account ease of access. For example, sensors inside the refrigerator monitor the placement of food items and suggest optimal placement that takes into account ease of access. The generative AI uses that information to generate a plan to change the placement of food items. For example, it suggests placing frequently used foods at the front. The refrigerator also analyzes data on the placement of food items inside the refrigerator and identifies the optimal placement that takes into account ease of access. The generative AI uses that data to suggest changes to the placement of food items. For example, it suggests placing heavy foods on the bottom shelves and light foods on the top shelves. The refrigerator also monitors the placement of food items using sensors and suggests rearrangements that take into account ease of access. The generative AI uses that information to generate a plan to optimize the placement of food items and suggest it to the user. This improves the ease of access to food and enables efficient use.

[0061] The generation AI can use the emotion estimation function to suggest dishes that match the user's mood and select cooking methods that match the mood. For example, the generation AI can use the emotion estimation function to analyze the user's mood in real time and suggest dishes based on that information. For example, if the user is in the mood to relax, the generation AI will suggest dishes that are easy to make. The generation AI can also analyze the user's mood and select cooking methods that match that mood. For example, if the user is in the mood to cheer up, the generation AI will suggest spicy dishes. The generation AI can also use the emotion estimation function to analyze the user's mood and generate cooking plans based on that information. For example, if the user is tired, the generation AI will suggest dishes that can be made in a short amount of time. This allows the generation AI to provide dishes that match the user's mood and increase satisfaction.

[0062] The generation AI can analyze the nutritional value of food in the refrigerator and suggest menus that take nutritional balance into consideration. For example, sensors in the refrigerator analyze the nutritional value of food and send that information to the generation AI. The generation AI uses that data to suggest menus that take nutritional balance into consideration. For example, if there is a vitamin deficiency, it will suggest dishes that are rich in vitamins. The generation AI can also analyze the nutritional value data of food in the refrigerator and generate menus that take nutritional balance into consideration. The generation AI uses that data to suggest nutritionally balanced dishes. For example, if there is a protein deficiency, it will suggest dishes that use meat or fish. The generation AI can also analyze the nutritional value of food using sensors in the refrigerator and send that information to the generation AI. The generation AI uses that data to create menus that take nutritional balance into consideration and suggest them to the user. This helps provide nutritionally balanced meals and supports health.

[0063] The generation AI manages allergen information for foods in the refrigerator and can suggest menus suitable for users with allergies. For example, sensors in the refrigerator analyze food allergen information and send that information to the generation AI. The generation AI uses that data to suggest menus suitable for users with allergies. For example, it would suggest dairy-free dishes to a user with a dairy allergy. The generation AI also manages allergen information for foods in the refrigerator and generates menus based on that information. The generation AI uses that data to suggest dishes suitable for users with allergies. For example, it would suggest nut-free dishes to a user with a nut allergy. The generation AI also analyzes food allergen information using sensors in the refrigerator and sends that information to the generation AI. The generation AI uses that data to generate menus suitable for users with allergies and suggest them to the user. This provides safe meals to users with allergies.

[0064] The generation AI can use the emotion estimation function to suggest menus that take into account the preferences and allergy information of all family members. For example, the generation AI can use the emotion estimation function to analyze the preferences and allergy information of all family members and suggest menus based on that information. For example, it can suggest dishes that all family members like. The generation AI can also analyze the preferences and allergy information of all family members and generate menus based on that information. The generation AI can then use that data to suggest dishes that the whole family can enjoy. For example, if there is a family member with an allergy, it can suggest dishes that do not contain that allergen. The generation AI can also use the emotion estimation function to analyze the preferences and allergy information of all family members and generate menus based on that information. For example, it can suggest dishes that will relax the whole family. This allows for meals that the whole family can enjoy.

[0065] The generation AI can analyze smartphone location information and predict meal times based on the user's movement patterns. For example, the generation AI analyzes smartphone location information to identify the user's movement patterns. Based on that data, the generation AI predicts the time of day when the user is likely to eat. For example, if the user is in a specific location at the same time every day, the generation AI can suggest meals for that time of day. The generation AI also analyzes smartphone location information and learns the user's movement patterns. Based on that data, the generation AI predicts the user's meal times. For example, it predicts meal times based on the time the user frequently visits a specific restaurant. The generation AI also analyzes smartphone location information and identifies the user's movement patterns. Based on that data, the generation AI predicts the user's meal times and suggests meals at appropriate times. For example, it predicts meal times based on the time the user returns home. This makes it possible to predict meal times based on the user's movement patterns and provide meals at appropriate times.

[0066] The generation AI can use the emotion estimation function to analyze the user's stress level and suggest meal times during times when the user is able to relax. For example, the generation AI uses the emotion estimation function to analyze the user's stress level in real time. Based on that data, the generation AI suggests meals during times when the user is able to relax. For example, suggesting meals that avoid times when stress is high. The generation AI also analyzes the user's stress level and adjusts meal times based on that data. Based on that data, the generation AI suggests meals during times when the user is able to relax. For example, suggesting relaxing dishes during times when stress is low. The generation AI also uses the emotion estimation function to analyze the user's stress level and adjust meal times based on that data. Based on that data, the generation AI suggests meals during times when the user is able to relax and provides the meals at appropriate times. This makes it possible to adjust meal times according to the user's stress level and provide relaxing meals.

[0067] The generation AI can work in conjunction with a smartwatch or fitness tracker to suggest meal times that take into account the user's health condition. For example, the generation AI works in conjunction with a smartwatch or fitness tracker to analyze the user's health condition. Based on that data, the generation AI suggests meal times that suit the user's health condition. For example, it suggests eating at an appropriate time after exercise. The generation AI also analyzes fitness tracker data to predict meal times that take the user's health condition into consideration. Based on that data, the generation AI suggests the healthiest time for the user to eat meals. For example, it suggests a breakfast time based on sleep data. The generation AI also works in conjunction with a smartwatch or fitness tracker to analyze the user's health condition. Based on that data, the generation AI suggests meal times that suit the user's health condition and provides meals at appropriate times. For example, it adjusts meal times based on heart rate and calorie consumption. This makes it possible to suggest meal times that suit the user's health condition and provide healthy meals.

[0068] The generative AI can work in conjunction with smart home devices to suggest meal times that take into account the schedules of all family members. For example, the generative AI can work in conjunction with smart home devices to analyze the schedules of all family members. Based on that data, the generative AI can suggest times when the whole family can eat together. For example, it can suggest eating at a time when all family members are together. The generative AI can also analyze data from smart home devices to predict meal times that take into account the schedules of all family members. Based on that data, the generative AI can suggest times when the whole family can eat together. For example, it can suggest eating at a time when all family members' schedules do not overlap. The generative AI can also work in conjunction with smart home devices to analyze the schedules of all family members. Based on that data, the generative AI can suggest times when the whole family can eat together and serve the meal at an appropriate time. For example, it can suggest eating at a time when all family members are relaxed. This allows the generative AI to suggest meal times that fit the schedules of all family members, allowing the whole family to enjoy meals together.

[0069] The generation AI can use the emotion estimation function to suggest meal times that match the user's mood and adjust the timing of meals according to the mood. For example, the generation AI can use the emotion estimation function to analyze the user's mood in real time and suggest meal times based on that information. For example, if the user feels like relaxing, the generation AI will suggest eating at a time when the user can relax. The generation AI also analyzes the user's mood and adjusts the timing of meals according to that mood. Based on that data, the generation AI will suggest eating at a time when the user can be most relaxed. For example, if the user is feeling stressed, the generation AI will suggest eating at a time when the stress is low. The generation AI also uses the emotion estimation function to analyze the user's mood and adjust the timing of meals based on that information. Based on that data, the generation AI will suggest eating at a time when the user can relax and provide meals at an appropriate time. For example, if the user is tired, the generation AI will suggest eating at a time when the user can relax. This adjusts the timing of meals according to the user's mood and improves satisfaction.

[0070] The generation AI can analyze past meal history and suggest menus that reflect the user's preferences and eating habits. For example, the generation AI analyzes past meal history to identify the user's preferences and eating habits. Based on that data, the generation AI suggests dishes that the user likes. For example, it suggests menus based on dishes the user eats frequently. The generation AI also analyzes meal history data to generate menus that reflect the user's eating habits. Based on that data, the generation AI suggests dishes that the user likes. For example, it suggests dishes that use the user's favorite ingredients. The generation AI also analyzes past meal history to identify the user's preferences and eating habits. Based on that data, the generation AI suggests dishes that the user likes and provides an appropriate menu. For example, if the user likes a particular dish, it suggests that dish. This suggests menus based on the user's preferences and eating habits, improving satisfaction.

[0071] The generation AI can propose menus that reflect the season and weather, and provide dishes that reflect the season. For example, the generation AI analyzes seasonal and weather data and proposes menus based on that information. The generation AI then proposes dishes that reflect the season based on that data. For example, it proposes cold dishes in summer and hot dishes in winter. The generation AI also generates menus that reflect the season and weather, and proposes them to the user. The generation AI then proposes dishes that reflect the season based on that data. For example, it proposes dishes using seasonal vegetables in spring and dishes using mushrooms in autumn. The generation AI also analyzes seasonal and weather data, and proposes menus based on that information. The generation AI then proposes dishes that reflect the season based on that data, and provides an appropriate menu. For example, it proposes hot soup on a rainy day and salad on a sunny day. This allows the user to enjoy dishes that reflect the season and weather, allowing them to enjoy the feeling of the season.

[0072] The generation AI can use the emotion estimation function to suggest menus that match the user's mood and select dishes that match the mood. For example, the generation AI can use the emotion estimation function to analyze the user's mood in real time and suggest menus based on that information. For example, if the user is in the mood to relax, it will suggest relaxing dishes. The generation AI also analyzes the user's mood and selects dishes that match that mood. Based on that data, the generation AI suggests dishes that will most relax the user. For example, if the user is feeling stressed, it will suggest dishes that will reduce stress. The generation AI also uses the emotion estimation function to analyze the user's mood and suggest menus based on that information. Based on that data, the generation AI suggests dishes that will relax the user and provides an appropriate menu. For example, if the user is tired, it will suggest relaxing dishes. This suggests menus that match the user's mood and improves satisfaction.

[0073] The generation AI can propose menus that take into account the ratings and reviews of other users and provide popular dishes. For example, the generation AI analyzes the ratings and reviews of other users and proposes menus based on that information. The generation AI proposes popular dishes based on that data. For example, it prioritizes proposing highly rated dishes. The generation AI also analyzes the reviews of other users and generates menus based on that information. The generation AI proposes popular dishes based on that data. For example, it proposes dishes with a large number of reviews. The generation AI also analyzes the ratings and reviews of other users and proposes menus based on that information. The generation AI proposes popular dishes based on that data and provides appropriate menus. For example, it proposes dishes that are highly rated by the user. This provides popular dishes that take into account the ratings and reviews of other users, improving satisfaction.

[0074] The generation AI can propose menus that take into account information about the origins and producers of ingredients, promoting local production and consumption. For example, the generation AI can analyze information about the origins and producers of ingredients and propose menus based on that information. The generation AI can then propose dishes that promote local production and consumption based on that data. For example, it can propose dishes that use local ingredients. The generation AI can also analyze information about the origins and producers of ingredients and generate menus based on that information. The generation AI can then propose dishes that promote local production and consumption based on that data. For example, it can propose dishes that use local agricultural products. The generation AI can also analyze information about the origins and producers of ingredients and propose menus based on that information. The generation AI can then propose dishes that promote local production and consumption based on that data, providing an appropriate menu. For example, it can propose dishes that use fish caught by local fishermen. This can promote local production and consumption and support the local economy.

[0075] When ordering online, the generation AI can suggest the optimal delivery time and deliver food that fits the user's schedule. For example, when ordering online, the generation AI analyzes the user's schedule and suggests the optimal delivery time. Based on that data, the generation AI suggests delivery at a time when it is convenient for the user to receive the food. For example, it suggests delivery when the user is at home. The generation AI also analyzes the user's schedule and suggests the optimal delivery time based on that information. Based on that data, the generation AI suggests delivery at a time when it is convenient for the user to receive the food. For example, it suggests delivery when the user is home from work. The generation AI also analyzes the user's schedule and suggests the optimal delivery time when ordering online. Based on that data, the generation AI suggests delivery at a time when it is convenient for the user to receive the food, and delivers food at the appropriate time. For example, it suggests delivery when the user is at home on a holiday. This enables delivery to fit the user's schedule and improves convenience.

[0076] Generative AI can automatically apply discounts and coupon information when placing an online order, reducing costs. For example, generative AI can build a system that automatically applies discounts and coupon information when placing an online order. Based on that data, generative AI can apply the most appropriate discounts and coupons, reducing costs. For example, it can automatically apply discount coupons for specific products. Generative AI can also analyze discount and coupon information and automatically apply it when placing an online order based on that information. Based on that data, generative AI can apply the most appropriate discounts and coupons, reducing costs. For example, it can apply discounts when purchasing multiple products together. Generative AI can also build a system that automatically applies discounts and coupon information when placing an online order. Based on that data, generative AI can apply the most appropriate discounts and coupons, reducing costs. For example, it can apply discounts when ordering during a specific time period. This allows discounts and coupon information to be applied automatically, reducing costs.

[0077] The generation AI can use the emotion estimation function to suggest foods that match the user's mood and select ingredients that match the mood. For example, the generation AI can use the emotion estimation function to analyze the user's mood in real time and suggest foods based on that information. For example, if the user is in the mood to relax, it will suggest foods that will help them relax. The generation AI can also analyze the user's mood and select ingredients that match that mood. Based on that data, the generation AI can suggest foods that will help the user relax the most. For example, if the user is feeling stressed, it will suggest foods that will help relieve stress. The generation AI can also use the emotion estimation function to analyze the user's mood and suggest foods based on that information. Based on that data, the generation AI can suggest foods that will help the user relax and provide appropriate ingredients. For example, if the user is tired, it will suggest foods that will help them relax. This allows the generation AI to suggest foods that match the user's mood and improve satisfaction.

[0078] Generative AI can suggest eco-friendly options when ordering online, promoting environmentally conscious shopping. For example, generative AI could build a system that suggests eco-friendly options when ordering online. Based on that data, generative AI would suggest environmentally conscious products. For example, it could suggest products that use recyclable packaging. Generative AI could also analyze eco-friendly options and use that information to make suggestions when ordering online. Based on that data, generative AI would suggest environmentally conscious products. For example, it could suggest locally produced foods. Generative AI could also build a system that suggests eco-friendly options when ordering online. Based on that data, generative AI would suggest environmentally conscious products and provide them to users. For example, it could suggest organic foods and fair trade products. This would promote environmentally conscious shopping and support sustainable consumption.

[0079] Generative AI can suggest options to purchase directly from local producers when placing an online order, thereby supporting the local economy. For example, generative AI builds a system that suggests options to purchase directly from local producers when placing an online order. Based on that data, generative AI suggests products from local producers. For example, it might suggest vegetables produced by local farmers. Generative AI also analyzes options to purchase directly from local producers and makes suggestions based on that information when placing an online order. Based on that data, generative AI suggests products that support the local economy. For example, it might suggest fish caught by local fishermen. Based on that data, generative AI builds a system that suggests options to purchase directly from local producers when placing an online order. Based on that data, generative AI suggests products from local producers and provides them to the user. For example, it might suggest dairy products produced by a local farm. In this way, purchasing directly from local producers supports the local economy.

[0080] The generation AI can use the emotion estimation function to suggest foods that take into account the preferences and allergy information of all family members. For example, the generation AI can use the emotion estimation function to analyze the preferences and allergy information of all family members and suggest foods based on that information. For example, it can suggest foods that all family members like. The generation AI can also analyze the preferences and allergy information of all family members and generate foods based on that information. The generation AI can use that data to suggest foods that the whole family can enjoy. For example, if there is a family member with an allergy, it can suggest foods that do not contain that allergen. The generation AI can also use the emotion estimation function to analyze the preferences and allergy information of all family members and generate foods based on that information. For example, it can suggest foods that will relax the whole family. This allows for the provision of foods that the whole family can enjoy.

[0081] The generation AI can add a function to the shopping list that prioritizes items based on purchase frequency and expiration date. For example, the generation AI adds a function to the shopping list that prioritizes items based on purchase frequency and expiration date. The generation AI then generates a prioritized shopping list based on that data. For example, foods with an approaching expiration date are displayed first on the list. The generation AI also analyzes purchase frequency and expiration date, and prioritizes the shopping list based on that information. The generation AI then generates a prioritized shopping list based on that data. For example, foods that are purchased frequently are displayed at the top of the list. The generation AI also adds a function to the shopping list that prioritizes items based on purchase frequency and expiration date. The generation AI then generates a prioritized shopping list based on that data and provides it to the user. For example, foods with an approaching expiration date are displayed at the top of the list. This enables efficient shopping by prioritizing items based on purchase frequency and expiration date.

[0082] The generative AI can add suggested substitutes to the shopping list, providing appropriate options even when an item is out of stock. For example, the generative AI builds a function to add suggested substitutes to the shopping list. Based on that data, the generative AI suggests appropriate substitutes even when an item is out of stock. For example, if a specific brand of milk is out of stock, it will suggest other brands of milk. The generative AI also analyzes out-of-stock information and suggests substitutes based on that information. The generative AI also builds a function to add suggested substitutes to the shopping list. Based on that data, the generative AI suggests appropriate substitutes even when an item is out of stock, and provides them to the user. For example, if a specific meat is out of stock, it will suggest other meats that can be cooked in the same way. This allows appropriate substitutes to be suggested even when an item is out of stock, improving shopping convenience.

[0083] The generation AI uses the emotion estimation function to generate a shopping list tailored to the user's mood and suggest foods that match the mood. For example, the generation AI uses the emotion estimation function to analyze the user's mood in real time and generate a shopping list based on that information. For example, if the user feels like relaxing, it adds relaxing foods to the list. The generation AI also analyzes the user's mood and selects foods that match that mood. Based on that data, the generation AI adds foods that help the user relax to the list. For example, if the user is feeling stressed, it adds foods that relieve stress to the list. The generation AI also uses the emotion estimation function to analyze the user's mood and generate a shopping list based on that information. Based on that data, the generation AI adds foods that help the user relax to the list and provides an appropriate shopping list. For example, if the user is tired, it adds relaxing foods to the list. This generates a shopping list that matches the user's mood and improves satisfaction.

[0084] The generative AI can link recipe information to a shopping list and suggest dishes using purchased foods. For example, the generative AI adds a function to link recipe information to a shopping list. The generative AI then uses that data to suggest dishes using purchased foods. For example, it suggests recipes using foods added to the list. The generative AI also analyzes recipe information and links it to a shopping list based on that information. The generative AI then uses that data to suggest dishes using purchased foods. For example, it suggests recipes using foods added to the list. The generative AI also adds a function to link recipe information to a shopping list and builds a system that suggests dishes using purchased foods. The generative AI then uses that data to suggest dishes using purchased foods and provide them to the user. For example, it suggests recipes using foods added to the list. This makes it possible to suggest dishes using purchased foods and use ingredients efficiently.

[0085] The generation AI can use the emotion estimation function to generate a shopping list that takes into account the preferences and allergy information of all family members. For example, the generation AI uses the emotion estimation function to analyze the preferences and allergy information of all family members and generate a shopping list based on that information. For example, it adds foods that all family members like to the list. The generation AI also analyzes the preferences and allergy information of all family members and generates a shopping list based on that information. The generation AI uses that data to add foods that all family members can enjoy to the list. For example, if there is a family member with an allergy, it adds foods that do not contain that allergen to the list. The generation AI also uses the emotion estimation function to analyze the preferences and allergy information of all family members and generates a shopping list based on that information. For example, it adds foods that make the whole family feel relaxed to the list. In this way, a shopping list is generated that provides foods that the whole family can enjoy.

[0086] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0087] Generative AI can not only predict a user's meal times, but also suggest meal times that take into account the user's health condition. For example, by linking with a smartwatch or fitness tracker and analyzing the user's exercise and sleep data, it can suggest optimal meal times. It can suggest the appropriate time to eat after exercise or a dinner time to improve sleep quality. It can also adjust meal times to supplement specific nutrients based on the user's health condition. This can support the user's health and provide more effective meal timings.

[0088] Generative AI can not only monitor the freshness of food in the refrigerator, but also propose cooking plans that take food expiration dates into consideration. For example, it can propose recipes that prioritize the use of foods with an approaching expiration date, thereby reducing food waste. In addition, by increasing the variety of dishes using foods with an approaching expiration date, users can enjoy meals without getting bored. It can also suggest ways to make large quantities of dishes using foods with an approaching expiration date at once and freeze them. This reduces food waste and enables efficient use of ingredients.

[0089] The generative AI not only optimizes the placement of food in the refrigerator, but can also suggest changes to the placement that take into account the user's height and dominant hand. For example, placing frequently used foods in a location that is easy for the user to access improves convenience. It is also possible to suggest placing food at a height that makes it easy to access based on the user's height. Furthermore, adjusting the placement of food in the refrigerator to suit the user's dominant hand can create a refrigerator that is easier to use. This improves user convenience and enables efficient food access.

[0090] Using its emotion estimation function, the generative AI can not only suggest dishes that match the user's mood, but can also analyze the user's stress level and suggest meal times that are convenient for relaxation. For example, by suggesting meals that avoid high-stress times, the user can enjoy their meal in a relaxing environment. It can also suggest dishes that use ingredients that have a relaxing effect based on the user's stress level. Furthermore, by analyzing the user's stress level and adjusting meal times based on that data, a more effective relaxation effect can be achieved. This makes it possible to reduce the user's stress and provide a relaxing meal.

[0091] Generative AI can analyze the nutritional value of food in the refrigerator and suggest menus that take nutritional balance into consideration, as well as suggest meal plans tailored to the user's health goals. For example, it can suggest low-calorie dishes to a user on a diet, and high-protein dishes to a user looking to build muscle. It can also suggest dishes to supplement a specific nutrient if the user is lacking in that nutrient. Furthermore, it can support health by suggesting meal plans that actively incorporate specific ingredients based on the user's health condition. This allows for the provision of meal plans tailored to the user's health goals, enabling effective health management.

[0092] The generation AI manages allergen information for food in the refrigerator and not only suggests menus suitable for users with allergies, but also suggests alternative ingredients that do not contain allergens. For example, it can suggest dishes that use alternative ingredients that do not contain dairy products to users with dairy allergies. It can also suggest dishes that use alternative ingredients that do not contain nuts to users with nut allergies. Furthermore, by increasing the variety of dishes that use alternative ingredients that do not contain allergens, users can enjoy their meals without getting bored. This makes it possible to provide safe and diverse meals to users with allergies.

[0093] Using its emotion estimation function, the generative AI can not only propose menus that take into account the preferences and allergy information of all family members, but also suggest meal times that suit the mood of each family member. For example, by suggesting meals at times when everyone is relaxed, the whole family can enjoy a meal together in a relaxed environment. It can also suggest dishes that use ingredients that have a relaxing effect depending on the mood of each family member. Furthermore, by analyzing the moods of each family member and adjusting meal times based on that data, it is possible to achieve a more effective relaxation effect. This makes it possible to provide meals that the whole family can relax at.

[0094] The generative AI not only analyzes the smartphone's location information and predicts meal times based on the user's movement patterns, but also makes meal suggestions based on the user's destination. For example, if a user frequently visits a particular restaurant, it can suggest meals based on that restaurant's menu. Also, if the user is traveling, it can suggest local cuisine from the destination. Furthermore, by analyzing the user's movement patterns and adjusting meal times based on that data, the system can ensure that the user eats at appropriate times even while on the move. This makes it possible to make meal suggestions based on the user's movement patterns, providing more convenient and appropriate meals.

[0095] Using its emotion estimation function, the generation AI can not only suggest mealtimes that match the user's mood, but also select ingredients that match the user's mood. For example, if the user is feeling like relaxing, it can suggest dishes that use ingredients that have a relaxing effect. Also, if the user is feeling like energizing, it can suggest dishes that use ingredients that will replenish energy. Furthermore, by analyzing the user's mood and selecting ingredients based on that data, it is possible to provide a more effective meal. This allows for the selection of ingredients that match the user's mood, thereby increasing satisfaction.

[0096] Using its emotion estimation function, the generative AI can analyze the user's stress level and suggest meal times that are convenient for relaxation. For example, it can suggest meals that avoid high-stress times. It can also suggest dishes that use ingredients with a relaxing effect based on the user's stress level. Furthermore, by analyzing the user's stress level and adjusting meal times based on that data, it is possible to achieve a more effective relaxation effect. This makes it possible to provide a meal that reduces the user's stress and allows them to relax.

[0097] The processing flow of the second embodiment will be briefly explained below.

[0098] Step 1: The AI ​​Cooker cooks the food. For example, the AI ​​Cooker uses temperature control to cook food at the right temperature and executes cooking programs to automatically create specific dishes. It can also use sensor technology to monitor the condition of the food and adjust the cooking progress. Step 2: The refrigerator monitors the food situation. For example, it uses sensors to detect the type and quantity of food, monitors the freshness of the food, and identifies foods that have lost their freshness. It can also optimize the placement of food and suggest rearranging it to make it easier to access. Step 3: The smartphone predicts meal times based on daily usage. For example, the smartphone can analyze app usage time and location information to identify the user's meal times. It can also link with a calendar app to adjust meal times based on schedules. It can also analyze location information and predict meal times based on the user's movement patterns. Step 4: The generation AI proposes a menu based on the refrigerator's status, and places an online order if food is in short supply. For example, the generation AI can analyze the status of food in the refrigerator and propose an appropriate menu. It can also analyze the nutritional value of food and propose a menu that takes nutritional balance into consideration. It can also manage food allergen information and propose a menu suitable for users with allergies.

[0099] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the 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.

[0100] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0101] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, 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.

[0102] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0103] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0104] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0105] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0106] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0107] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0108] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0109] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0110] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0111] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0112] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0113] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0114] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0115] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0116] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0117] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0118] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0119] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0120] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0121] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0122] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0123] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0124] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0125] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0126] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0127] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0128] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0129] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0130] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0131] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0132] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0133] 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.

[0134] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0135] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0136] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to 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 imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0138] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0139] The control 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0140] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0141] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0142] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0143] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0144] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0145] 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 control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0146] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0147] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0148] The emotion identification model 59 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 an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0149] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0150] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0151] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0152] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0153] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0154] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0155] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0156] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0157] 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.

[0158] It is not necessary to store all 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 all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0159] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0160] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with 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). Also, the hardware resource that executes the specific process may be a single processor.

[0161] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0162] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0163] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0164] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0165] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0166] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. Equipped with AI hotpot and refrigerator, The AI ​​pot cooks food, The refrigerator grasps the status of food, Predict meal times based on daily smartphone usage, The AI ​​then suggests a menu based on the refrigerator's contents and places an online order if food is in short supply. A system characterized by:

2. The refrigerator is monitoring the freshness of said food in real time; The generated AI is Generate a cooking plan that prioritizes the use of the food whose freshness has decreased.

2. The system of claim 1.

3. The refrigerator is optimizing the placement of the food; The generated AI is Propose a layout change that takes into account the ease of access.

2. The system of claim 1.

4. The generated AI is It suggests dishes that match the user's mood, Choose a cooking method that suits your mood 2. The system of claim 1.

5. The generated AI is Analyzing the nutritional value of the food in the refrigerator; Propose the above menu taking into consideration nutritional balance 2. The system of claim 1.

6. The generated AI is managing allergen information of the food in the refrigerator; Suggesting suitable meals for users with allergies 2. The system of claim 1.

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