system

Abstract

The system according to the embodiment aims to automate everything from managing ingredients in the refrigerator to creating recipes and ordering missing ingredients. [Solution] A system according to an embodiment includes a detection unit, an analysis unit, a generation unit, and an ordering unit. The detection unit detects ingredients in a refrigerator. The analysis unit analyzes the ingredient information detected by the detection unit. The generation unit generates a recipe based on the information analyzed by the analysis unit. The ordering unit orders missing ingredients based on the recipe generated by the generation unit.

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] With conventional technology, managing ingredients in the refrigerator, creating recipes, and ordering missing ingredients had to be done manually, which was inefficient.

[0005] The system according to the embodiment aims to automate everything from managing ingredients in the refrigerator to creating recipes and ordering missing ingredients. [Means for solving the problem]

[0006] The system according to the embodiment includes a detection unit, an analysis unit, a generation unit, and an ordering unit. The detection unit detects ingredients in the refrigerator. The analysis unit analyzes the ingredient information detected by the detection unit. The generation unit generates a recipe based on the information analyzed by the analysis unit. The ordering unit orders missing ingredients based on the recipe generated by the generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can automate everything from managing ingredients in the refrigerator to creating recipes and ordering missing 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 nonvolatile storage devices that store various programs, various parameters, etc. Examples of nonvolatile 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) A system according to an embodiment of the present invention combines a refrigerator and a generation AI to automatically detect and manage ingredients in the refrigerator using a camera. In this system, a camera installed inside the refrigerator automatically detects and manages ingredients. Next, the generation AI generates daily recipes based on information previously registered by the user and past behavioral data. Furthermore, the generation AI automatically orders any missing ingredients from an online supermarket or contacts the user. This system functions as a future home appliance that is both health- and socially friendly. For example, a camera installed inside the refrigerator automatically detects and manages ingredients. The camera takes photos of the ingredients in the refrigerator, and the generation AI performs image analysis to identify the type and quantity of ingredients. This ensures that the information about ingredients in the refrigerator is always kept up to date. Next, the generation AI generates daily recipes based on information previously registered by the user and past behavioral data. The generation AI suggests optimal recipes based on the user's past cooking habits and favorite ingredients. This allows users to easily plan their daily meals. Furthermore, the generation AI automatically orders any missing ingredients from an online supermarket or contacts the user. The generative AI identifies the ingredients needed for a recipe and either sends an order to the online supermarket or notifies the user of any missing ingredients. This allows users to easily obtain the ingredients they need. This system functions as a future home appliance that is friendly to both health and social issues. The generative AI supports a healthy diet by suggesting recipes that take into account the user's health condition and nutritional balance. It also contributes to reducing food waste by reducing the amount of ingredients wasted. This allows the system to automatically detect and manage ingredients in the refrigerator, generate recipes that take into account the user's health condition and nutritional balance, and automatically order any missing ingredients.

[0029] A refrigerator management system according to an embodiment includes a detection unit, an analysis unit, a generation unit, and an order unit. The detection unit detects ingredients in the refrigerator. For example, the detection unit photographs the ingredients using a camera installed in the refrigerator, and the generation AI analyzes the image to identify the type and quantity of ingredients. For example, the camera has high resolution and is positioned to cover the entire interior of the refrigerator. The camera periodically photographs the ingredients in the refrigerator and sends the image data to the generation AI. The generation AI uses image analysis technology to identify the type and quantity of ingredients. For example, the generation AI uses an object detection algorithm to identify ingredients in the image and identify their type and quantity. The generation AI can also classify the type of ingredients using an image classification algorithm. The analysis unit analyzes the ingredient information detected by the detection unit. For example, the analysis unit manages the status of the ingredients in the refrigerator based on the ingredient information analyzed by the generation AI. For example, the analysis unit analyzes and manages the freshness and expiration date of the ingredients. The generation unit generates recipes based on the information analyzed by the analysis unit. In the generation unit, the generation AI generates a recipe based on, for example, information previously registered by the user and past behavioral data. The generation AI proposes an optimal recipe based on the user's preferences and past cooking history. For example, the generation AI generates a recipe based on dishes the user has made in the past and their favorite ingredients. The generation AI can also propose recipes that take into account the user's health condition and nutritional balance. The ordering unit orders missing ingredients based on the recipe generated by the generation unit. For example, the ordering unit orders ingredients missing from an online supermarket that are missing from the recipe generated by the generation AI. The generation AI identifies ingredients needed for the recipe and sends an order to the online supermarket. The ordering unit can also notify the user of missing ingredients from the recipe generated by the generation AI. For example, the generation AI notifies the user of missing ingredients and prompts the user to manually order them. As a result, the refrigerator management system according to the embodiment can automatically detect and manage ingredients in the refrigerator, generate recipes that take into account the user's health condition and nutritional balance, and automatically order missing ingredients.

[0030] The detection unit can use a camera to take pictures of ingredients in the refrigerator, and the generation AI can perform image analysis. When taking pictures with a camera, for example, a high-resolution camera can be used. The camera is positioned to cover the entire interior of the refrigerator. The camera periodically takes pictures of the ingredients in the refrigerator and sends the image data to the generation AI. The generation AI uses image analysis technology to identify the type and amount of ingredients. For example, the generation AI can use an object detection algorithm to identify ingredients in the image and determine their type and amount. The generation AI can also use an image classification algorithm to classify the type of ingredients. This improves the accuracy of ingredient detection by using a camera and the generation AI. Some or all of the above-mentioned processing in the detection unit can be performed using, for example, AI, or without AI. For example, the detection unit can input image data taken with a camera to the generation AI and have the generation AI perform image analysis.

[0031] The generation unit allows the generation AI to generate recipes based on information previously registered by the user or past behavioral data. The generation unit allows the generation AI to generate recipes based on, for example, information previously registered by the user or past behavioral data. The generation AI suggests optimal recipes based on the user's preferences and past cooking history. For example, the generation AI generates recipes based on dishes the user has made in the past and their favorite ingredients. The generation AI can also suggest recipes that take into account the user's health condition and nutritional balance. For example, the generation AI suggests nutritious recipes taking into account the user's health condition and nutritional balance. This makes it possible to generate optimal recipes based on the user's preferences and behavioral data. Some or all of the above-mentioned processing in the generation unit may be performed using, or without, AI. For example, the generation unit may input the user's behavioral data into the generation AI and cause the generation AI to generate a recipe.

[0032] The ordering unit can order ingredients missing from the online supermarket for the recipe generated by the generation AI. For example, the ordering unit orders ingredients missing from the recipe generated by the generation AI from the online supermarket. The generation AI identifies the ingredients needed for the recipe and sends an order to the online supermarket. For example, the generation AI creates a list of ingredients needed for the recipe and automatically sends it to the online supermarket's ordering system. The ordering unit can also notify the user of ingredients missing from the recipe generated by the generation AI. For example, the generation AI notifies the user of missing ingredients and prompts the user to manually order them. This saves the user time and effort by automatically ordering the missing ingredients. Some or all of the above-mentioned processing in the ordering unit may be performed, for example, using AI, or may be performed without using AI. For example, the ordering unit sends an order to the online supermarket based on the recipe generated by the generation AI.

[0033] The ordering unit can notify the user of any ingredients missing from the recipe generated by the generation AI. For example, the ordering unit notifies the user of any ingredients missing from the recipe generated by the generation AI. The generation AI notifies the user of the missing ingredients and encourages the user to manually order them. For example, the generation AI sends a notification to the user's smartphone to inform them of the missing ingredients. The generation AI can also send a notification to the user's email address. This allows the user to know what ingredients they need by notifying them of the missing ingredients. Some or all of the above-mentioned processing in the ordering unit may be performed, for example, using AI, or may be performed without using AI. For example, the ordering unit notifies the user of any missing ingredients based on the recipe generated by the generation AI.

[0034] The generation unit allows the generation AI to suggest recipes based on the user's health condition or nutritional balance. For example, the generation unit allows the generation AI to suggest recipes that take into account the user's health condition and nutritional balance. The generation AI suggests nutritious recipes that take into account the user's health condition and nutritional balance. For example, the generation AI suggests recipes that contain necessary nutrients based on the user's health data. The generation AI can also suggest healthy meals based on the user's fitness data. This supports a healthy diet by suggesting recipes that take into account the user's health condition and nutritional balance. Some or all of the above-mentioned processing in the generation unit may be performed using, or without, AI. For example, the generation unit may input the user's health data into the generation AI and cause the generation AI to suggest recipes.

[0035] The detection unit can detect the freshness of ingredients and prioritize notification of ingredients whose freshness has decreased. For example, the detection unit analyzes images taken by a camera and detects changes in the color and shape of ingredients to determine freshness. For example, the camera has high resolution and is positioned to cover the entire interior of the refrigerator. The camera periodically takes photos of ingredients in the refrigerator and sends the image data to the generation AI. The generation AI uses image analysis technology to detect changes in the color and shape of ingredients and determine freshness. The detection unit can also prioritize detection of ingredients whose freshness may decrease based on the shelf life of the ingredients. For example, the generation AI identifies ingredients whose freshness may decrease based on the shelf life data of the ingredients and prioritizes notification. The detection unit can also immediately notify the user if mold or discoloration is found on the surface of the ingredients. This prioritizes notification of ingredients whose freshness has decreased, thereby reducing food waste. Some or all of the above-mentioned processing by the detection unit may be performed, for example, using AI or without AI. For example, the detection unit can input image data captured by a camera into the generation AI and have the generation AI detect freshness.

[0036] The detection unit can simultaneously detect and manage the type and expiration date of ingredients. For example, the detection unit uses a camera to read the expiration date written on the ingredient packaging and register it in a database. For example, the camera has high resolution and is positioned to cover the entire interior of the refrigerator. The camera periodically photographs the ingredients in the refrigerator and sends the image data to the generation AI. The generation AI uses image analysis technology to identify the ingredient type and expiration date and register them in a database. The detection unit can also prioritize detecting ingredients that are approaching their expiration date and notify the user. For example, the generation AI identifies ingredients that are approaching their expiration date and notifies the user. The detection unit can also automatically remove ingredients that have passed their expiration date from the list and encourage them to discard them. This allows for more efficient ingredient management by simultaneously managing expiration dates. Some or all of the above-described processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input image data captured by a camera into the generation AI and have the generation AI detect the expiration date.

[0037] The detection unit detects the temperature and humidity inside the refrigerator to maintain good food preservation conditions. The detection unit, for example, detects the temperature using a temperature sensor inside the refrigerator and adjusts the temperature to an appropriate level. For example, the temperature sensor is highly accurate and monitors the temperature inside the refrigerator in real time. If the temperature inside the refrigerator falls outside a set range, the temperature sensor activates a cooling system to adjust the temperature to an appropriate level. The detection unit can also detect humidity using a humidity sensor inside the refrigerator and adjust the humidity to an appropriate level. For example, the humidity sensor is highly accurate and monitors the humidity inside the refrigerator in real time. If the humidity inside the refrigerator falls outside a set range, the humidity sensor activates a humidification or dehumidification system to adjust the humidity to an appropriate level. Furthermore, the detection unit can monitor changes in temperature and humidity in real time and notify the user if an abnormality occurs. For example, if the temperature or humidity falls outside the set range, a notification is sent to the user's smartphone to notify the abnormality. This allows the temperature and humidity inside the refrigerator to be optimized to maintain good food preservation conditions. Some or all of the above-described processing in the detection unit may be performed using, for example, AI, or may be performed without using AI. For example, the detection unit may input temperature and humidity data to the generation AI and cause the generation AI to optimize the storage conditions.

[0038] The detection unit can detect the location information of ingredients and suggest placement that makes it easy for the user to access them. The detection unit, for example, uses a camera to detect the location of ingredients and suggests placing them in a location that makes access easy. For example, the camera has high resolution and is positioned to cover the entire interior of the refrigerator. The camera periodically photographs the ingredients in the refrigerator and sends the image data to the generation AI. The generation AI uses image analysis technology to identify the location of the ingredients and suggests placing them in a location that makes access easy. The detection unit can also suggest placement in a location that makes access easy based on the frequency of use of the ingredients. For example, the generation AI suggests placing frequently used ingredients in a location that makes access easy based on the user's past usage data. Furthermore, the detection unit can also suggest the optimal storage location based on the type of ingredient. For example, the generation AI identifies the optimal storage location based on the type of ingredient and suggests it to the user. This improves user convenience by detecting the location information of ingredients and suggesting a placement that makes access easy. Some or all of the above-mentioned processing in the detection unit may be performed using, for example, AI, or may be performed without using AI. For example, the detection unit can input image data captured by a camera into the generation AI and have the generation AI detect location information.

[0039] The analysis unit can analyze the nutritional value of ingredients and suggest nutritionally balanced recipes. The analysis unit, for example, analyzes the nutritional components of ingredients and suggests well-balanced recipes. For example, the generation AI suggests nutritionally balanced recipes based on nutritional component data of ingredients. The analysis unit can also suggest recipes containing necessary nutrients based on the user's health condition. For example, the generation AI suggests recipes containing necessary nutrients based on the user's health data. The analysis unit can also suggest healthy meals based on the nutritional value of ingredients. For example, the generation AI suggests healthy meals based on the nutritional value data of ingredients. This supports a healthy diet by suggesting nutritionally balanced recipes. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input nutritional value data of ingredients to the generation AI and cause the generation AI to suggest recipes.

[0040] The analysis unit can analyze the expiration dates of ingredients and generate recipes that prioritize using ingredients with upcoming expiration dates. The analysis unit, for example, analyzes the expiration dates of ingredients and proposes recipes that prioritize using ingredients with upcoming expiration dates. For example, the generation AI proposes recipes that prioritize using ingredients with upcoming expiration dates based on ingredient expiration date data. The analysis unit can also propose recipes that reduce food waste by using ingredients with upcoming expiration dates. For example, the generation AI proposes recipes that reduce waste by using ingredients with upcoming expiration dates. Furthermore, the analysis unit can also propose recipes that reduce waste by using ingredients with upcoming expiration dates. This makes it possible to reduce food waste by prioritizing the use of ingredients with upcoming expiration dates. Some or all of the above-described processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input ingredient expiration date data into the generation AI and cause the generation AI to generate a recipe.

[0041] The analysis unit can analyze allergen information of ingredients and suggest recipes suitable for users with allergies. The analysis unit, for example, analyzes allergen information of ingredients and suggests recipes suitable for users with allergies. For example, the generation AI suggests recipes suitable for users with allergies based on the allergen information data of ingredients. The analysis unit can also suggest recipes that do not contain allergens based on the user's allergy information. For example, the generation AI suggests recipes that do not contain allergens based on the user's allergy information. The analysis unit can also suggest ingredients suitable for users with allergies based on the allergen information of ingredients. For example, the generation AI suggests ingredients suitable for users with allergies based on the allergen information data of ingredients. This supports a healthy diet by suggesting recipes suitable for users with allergies. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input allergen information data of ingredients to the generation AI and cause the generation AI to suggest recipes.

[0042] The analysis unit can analyze information on the origin of ingredients and propose recipes that promote local production and consumption. The analysis unit, for example, analyzes information on the origin of ingredients and proposes recipes using local ingredients. For example, the generation AI proposes recipes using local ingredients based on the origin information data of ingredients. The analysis unit can also propose recipes that prioritize using local ingredients to promote local production and consumption. For example, the generation AI proposes recipes that prioritize using local ingredients. Furthermore, the analysis unit can also propose healthy recipes using local ingredients based on the origin information data of ingredients. For example, the generation AI proposes healthy recipes using local ingredients based on the origin information data of ingredients. This contributes to revitalizing the local economy by proposing recipes that promote local production and consumption. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input origin information data of ingredients to the generation AI and cause the generation AI to propose recipes.

[0043] The generation unit can analyze the user's past cooking history and generate recipes that match the user's preferences. The generation unit, for example, analyzes the user's past cooking history and suggests recipes that match the user's preferences. For example, the generation AI suggests recipes that match the user's preferences based on the user's past cooking history data. The generation unit can also suggest similar recipes based on dishes the user has made in the past. For example, the generation AI suggests similar recipes based on data on dishes the user has made in the past. The generation unit can also analyze the user's preferences and suggest new recipes. For example, the generation AI suggests new recipes based on the user's preference data. This improves meal satisfaction by providing recipes that match the user's preferences. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's cooking history data into the generation AI and have the generation AI suggest recipes.

[0044] The generation unit can generate recipes according to the season and weather. The generation unit, for example, suggests recipes using ingredients according to the season. For example, the generation AI suggests recipes according to the season based on seasonal ingredient data. The generation unit can also suggest recipes using ingredients according to the weather. For example, the generation AI suggests recipes according to the weather based on weather data. The generation unit can also suggest healthy recipes according to the season and weather. For example, the generation AI suggests healthy recipes based on the season and weather data. This improves the user's satisfaction with their meal by providing recipes according to the season and weather. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input season and weather data into the generation AI and cause the generation AI to suggest recipes.

[0045] The generation unit can generate recipes according to the user's dietary restrictions and diet goals. The generation unit, for example, suggests recipes according to the user's dietary restrictions. For example, the generation AI suggests recipes according to the user's dietary restrictions based on the user's dietary restriction data. The generation unit can also suggest recipes according to the user's diet goals. For example, the generation AI suggests recipes according to the user's diet goals based on the user's diet goal data. The generation unit can also suggest recipes according to the user's health condition. For example, the generation AI suggests recipes according to the user's health data. This supports a healthy diet by providing recipes according to the user's dietary restrictions and diet goals. Some or all of the above-mentioned processing in the generation unit may be performed using, or without, AI. For example, the generation unit can input the user's dietary restriction data into the generation AI and have the generation AI suggest recipes.

[0046] The generation unit can generate recipes that take into account the preferences of family members or housemates. The generation unit, for example, suggests recipes that match the preferences of family members or housemates. For example, the generation AI suggests recipes that match the preferences based on preference data of family members or housemates. The generation unit can also suggest recipes that match the preferences based on the family members or housemates' past cooking history. For example, the generation AI suggests recipes that match the preferences based on the family members or housemates' past cooking history data. The generation unit can also suggest recipes that match the health status of family members or housemates. For example, the generation AI suggests recipes that match the health status based on the family members or housemates' health data. This improves meal satisfaction by providing recipes that match the preferences of family members or housemates. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input preference data of family members or housemates into the generation AI and cause the generation AI to suggest recipes.

[0047] The ordering unit can place an order at the optimal timing, taking into account price fluctuations of ingredients. The ordering unit, for example, places an order when the price of an ingredient drops. For example, the generation AI places an order when the price drops based on ingredient price data. The ordering unit can also place an order before the price of an ingredient rises. For example, the generation AI places an order before the price rises based on ingredient price data. Furthermore, the ordering unit can monitor ingredient price fluctuations in real time and place an order at the optimal timing. For example, the generation AI places an order at the optimal timing based on ingredient price fluctuation data. This allows for costs to be reduced by taking ingredient price fluctuations into account when placing an order. Some or all of the above-described processing in the ordering unit may be performed using, for example, AI, or may be performed without using AI. For example, the ordering unit can input ingredient price data into the generation AI and have the generation AI determine the timing of the order.

[0048] The ordering unit can check the inventory status of ingredients in real time and automatically replenish necessary ingredients. The ordering unit, for example, checks the inventory status of ingredients in a refrigerator in real time and automatically orders necessary ingredients. For example, the generation AI automatically orders necessary ingredients based on inventory data of ingredients in the refrigerator. The ordering unit can also immediately place an order and replenish ingredients when ingredients are low in stock. For example, the generation AI immediately places an order when ingredients are low based on ingredient inventory data. The ordering unit can also monitor the inventory status of ingredients and automatically place an order when necessary. For example, the generation AI automatically places an order when necessary based on ingredient inventory data. This makes it possible to check the inventory status of ingredients in real time and automatically replenish ingredients, thereby preventing shortages of ingredients. Some or all of the above-mentioned processing in the ordering unit may be performed using AI, for example, or may be performed without using AI. For example, the ordering unit can input ingredient inventory data into the generation AI and have the generation AI determine the timing of replenishment.

[0049] The ordering unit can analyze the user's purchase history and prioritize ordering ingredients that the user has purchased in the past. The ordering unit, for example, analyzes the user's past purchase history and prioritizes ordering ingredients that the user has purchased frequently. For example, the generation AI prioritizes ordering ingredients that the user has purchased frequently based on the user's past purchase history data. The ordering unit can also suggest and order similar ingredients based on ingredients the user has purchased in the past. For example, the generation AI suggests and orders similar ingredients based on the user's past purchase history data. The ordering unit can also prioritize ordering ingredients that match the user's preferences based on the user's purchase history. For example, the generation AI prioritizes ordering ingredients that match the user's preferences based on the user's purchase history data. In this way, by ordering ingredients based on the user's purchase history, ingredients that match the user's preferences can be provided. Some or all of the above-described processing in the ordering unit may be performed using, for example, AI, or may be performed without using AI. For example, the ordering unit can input the user's purchase history data into the generation AI and have the generation AI determine the ingredients to order.

[0050] The ordering unit can place an order that suits the user's schedule, taking into account the delivery time of the ingredients. The ordering unit, for example, selects the optimal delivery time based on the user's schedule and places an order. For example, the generation AI selects the optimal delivery time based on the user's schedule data and places an order. The ordering unit can also place an order for ingredients that avoids time periods when the user is not at home. For example, the generation AI places an order based on the user's schedule data, avoiding time periods when the user is not at home. The ordering unit can also adjust the delivery time of ingredients to suit the user's schedule and place an order. For example, the generation AI adjusts the delivery time based on the user's schedule data and places an order. This allows ingredients to be ordered according to the user's schedule, making it easier to receive the ingredients. Some or all of the above-described processing in the ordering unit may be performed using AI, or may be performed without using AI. For example, the ordering unit can input the user's schedule data into the generation AI and have the generation AI determine the delivery time.

[0051] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0052] The analysis unit can estimate the user's food preferences and suggest ingredient combinations based on the estimated preferences. For example, if the user prefers a particular ingredient, the analysis unit can suggest recipes centered around that ingredient. Also, if the user prefers a particular cooking style, the analysis unit can suggest ingredient combinations that suit that style. Furthermore, the analysis unit can suggest new ingredient combinations based on dishes the user has enjoyed in the past. This improves meal satisfaction by suggesting ingredient combinations that suit the user's preferences. Some or all of the above-mentioned processing by the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's preference data into a generation AI and have the generation AI suggest ingredient combinations.

[0053] The detection unit can detect the frequency of ingredient use and place frequently used ingredients in locations that make them easy to access in order to optimize the placement of ingredients in the refrigerator. For example, the generation AI can identify frequently used ingredients based on the user's past usage data and suggest placing them in locations that make them easy to access. The detection unit can also suggest optimal storage locations based on the type of ingredient. For example, the generation AI can identify optimal storage locations based on the type of ingredient and suggest them to the user. Furthermore, the detection unit can take into account the freshness of the ingredients and prioritize placing ingredients that are likely to lose freshness in locations that make them easy to access. This optimizes the placement of ingredients, improving user convenience. Some or all of the above-mentioned processing in the detection unit may be performed using AI, for example, or may be performed without using AI. For example, the detection unit can input ingredient usage data into the generation AI and cause the generation AI to optimize the placement.

[0054] The analysis unit can analyze the nutritional value of ingredients and suggest nutritionally balanced recipes that suit the user's health condition. For example, the generation AI can suggest recipes that suit the user's health condition based on the nutritional data of ingredients. The analysis unit can also suggest recipes that contain necessary nutrients based on the user's health data. For example, the generation AI can suggest recipes that contain necessary nutrients based on the user's health data. The analysis unit can also suggest healthy meals based on the nutritional value of ingredients. For example, the generation AI can suggest healthy meals based on the nutritional value data of ingredients. This supports a healthy diet by suggesting nutritionally balanced recipes. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input nutritional value data of ingredients into the generation AI and cause the generation AI to suggest recipes.

[0055] The detection unit can detect the location information of ingredients in the refrigerator and suggest an arrangement that makes it easy for the user to take them out. For example, the detection unit can detect the location of ingredients using a camera and suggest placing ingredients in a location that makes them easy to take out. It can also suggest placing ingredients in a location that makes them easy to take out based on how often the ingredients are used. It can also suggest the optimal storage location depending on the type of ingredient. This improves user convenience by detecting the location information of ingredients and suggesting an arrangement that makes them easy to take out. Some or all of the above-mentioned processing in the detection unit may be performed using AI, for example, or may be performed without using AI. For example, the detection unit can input image data captured by a camera to the generation AI and have the generation AI detect the location information.

[0056] The analysis unit can analyze allergen information of ingredients and suggest recipes suitable for users with allergies. For example, the generation AI suggests recipes suitable for users with allergies based on the allergen information data of ingredients. The analysis unit can also suggest recipes that do not contain allergens based on the user's allergy information. For example, the generation AI suggests recipes that do not contain allergens based on the user's allergy information. Furthermore, the analysis unit can suggest ingredients suitable for users with allergies based on the allergen information of ingredients. This supports a healthy diet by suggesting recipes suitable for users with allergies. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input allergen information data of ingredients to the generation AI and cause the generation AI to suggest recipes.

[0057] The ordering unit can place an order at the optimal timing, taking into account fluctuations in the price of ingredients. For example, it can place an order when the price of ingredients drops. It can also place an order before the price of ingredients rises. It can also monitor price fluctuations of ingredients in real time and place an order at the optimal timing. This allows costs to be reduced by taking price fluctuations of ingredients into account when placing an order. Some or all of the above-mentioned processing in the ordering unit may be performed using, for example, AI, or may be performed without using AI. For example, the ordering unit can input price data of ingredients into the generation AI and have the generation AI decide the timing of the order.

[0058] The processing flow of the first embodiment will be briefly explained below.

[0059] Step 1: The detection unit detects ingredients in the refrigerator. For example, a camera installed inside the refrigerator takes a picture of the ingredients, and the generation AI analyzes the image to identify the type and quantity of ingredients. The camera has high resolution and is positioned to cover the entire interior of the refrigerator, taking pictures periodically. The generation AI uses object detection algorithms and image classification algorithms to identify the ingredients in the image and determine their type and quantity. Step 2: The analysis unit analyzes the food information detected by the detection unit. For example, the state of food in the refrigerator is managed based on the food information analyzed by the generation AI. The analysis unit analyzes and manages the freshness and expiration date of the food. Step 3: The generation unit generates recipes based on the information analyzed by the analysis unit. For example, the generation AI generates recipes based on information previously registered by the user and past behavioral data. The generation AI suggests optimal recipes based on the user's preferences and past cooking history. It can also suggest recipes that take into account the user's health condition and nutritional balance. Step 4: The ordering unit orders missing ingredients based on the recipe generated by the generation unit. For example, the generation AI orders missing ingredients from an online supermarket. The generation AI identifies the ingredients needed for the recipe and sends an order to the online supermarket. It can also notify the user of missing ingredients and prompt the user to order them manually.

[0060] (Example 2) A system according to an embodiment of the present invention combines a refrigerator and a generation AI to automatically detect and manage ingredients in the refrigerator using a camera. In this system, a camera installed inside the refrigerator automatically detects and manages ingredients. Next, the generation AI generates daily recipes based on information previously registered by the user and past behavioral data. Furthermore, the generation AI automatically orders any missing ingredients from an online supermarket or contacts the user. This system functions as a future home appliance that is both health- and socially friendly. For example, a camera installed inside the refrigerator automatically detects and manages ingredients. The camera takes photos of the ingredients in the refrigerator, and the generation AI performs image analysis to identify the type and quantity of ingredients. This ensures that the information about ingredients in the refrigerator is always kept up to date. Next, the generation AI generates daily recipes based on information previously registered by the user and past behavioral data. The generation AI suggests optimal recipes based on the user's past cooking habits and favorite ingredients. This allows users to easily plan their daily meals. Furthermore, the generation AI automatically orders any missing ingredients from an online supermarket or contacts the user. The generative AI identifies the ingredients needed for a recipe and either sends an order to the online supermarket or notifies the user of any missing ingredients. This allows users to easily obtain the ingredients they need. This system functions as a future home appliance that is friendly to both health and social issues. The generative AI supports a healthy diet by suggesting recipes that take into account the user's health condition and nutritional balance. It also contributes to reducing food waste by reducing the amount of ingredients wasted. This allows the system to automatically detect and manage ingredients in the refrigerator, generate recipes that take into account the user's health condition and nutritional balance, and automatically order any missing ingredients.

[0061] A refrigerator management system according to an embodiment includes a detection unit, an analysis unit, a generation unit, and an order unit. The detection unit detects ingredients in the refrigerator. For example, the detection unit photographs the ingredients using a camera installed in the refrigerator, and the generation AI analyzes the image to identify the type and quantity of ingredients. For example, the camera has high resolution and is positioned to cover the entire interior of the refrigerator. The camera periodically photographs the ingredients in the refrigerator and sends the image data to the generation AI. The generation AI uses image analysis technology to identify the type and quantity of ingredients. For example, the generation AI uses an object detection algorithm to identify ingredients in the image and identify their type and quantity. The generation AI can also classify the type of ingredients using an image classification algorithm. The analysis unit analyzes the ingredient information detected by the detection unit. For example, the analysis unit manages the status of the ingredients in the refrigerator based on the ingredient information analyzed by the generation AI. For example, the analysis unit analyzes and manages the freshness and expiration date of the ingredients. The generation unit generates recipes based on the information analyzed by the analysis unit. In the generation unit, the generation AI generates a recipe based on, for example, information previously registered by the user and past behavioral data. The generation AI proposes an optimal recipe based on the user's preferences and past cooking history. For example, the generation AI generates a recipe based on dishes the user has made in the past and their favorite ingredients. The generation AI can also propose recipes that take into account the user's health condition and nutritional balance. The ordering unit orders missing ingredients based on the recipe generated by the generation unit. For example, the ordering unit orders ingredients missing from an online supermarket that are missing from the recipe generated by the generation AI. The generation AI identifies ingredients needed for the recipe and sends an order to the online supermarket. The ordering unit can also notify the user of missing ingredients from the recipe generated by the generation AI. For example, the generation AI notifies the user of missing ingredients and prompts the user to manually order them. As a result, the refrigerator management system according to the embodiment can automatically detect and manage ingredients in the refrigerator, generate recipes that take into account the user's health condition and nutritional balance, and automatically order missing ingredients.

[0062] The detection unit can use a camera to take pictures of ingredients in the refrigerator, and the generation AI can perform image analysis. When taking pictures with a camera, for example, a high-resolution camera can be used. The camera is positioned to cover the entire interior of the refrigerator. The camera periodically takes pictures of the ingredients in the refrigerator and sends the image data to the generation AI. The generation AI uses image analysis technology to identify the type and amount of ingredients. For example, the generation AI can use an object detection algorithm to identify ingredients in the image and determine their type and amount. The generation AI can also use an image classification algorithm to classify the type of ingredients. This improves the accuracy of ingredient detection by using a camera and the generation AI. Some or all of the above-mentioned processing in the detection unit can be performed using, for example, AI, or without AI. For example, the detection unit can input image data taken with a camera to the generation AI and have the generation AI perform image analysis.

[0063] The generation unit allows the generation AI to generate recipes based on information previously registered by the user or past behavioral data. The generation unit allows the generation AI to generate recipes based on, for example, information previously registered by the user or past behavioral data. The generation AI suggests optimal recipes based on the user's preferences and past cooking history. For example, the generation AI generates recipes based on dishes the user has made in the past and their favorite ingredients. The generation AI can also suggest recipes that take into account the user's health condition and nutritional balance. For example, the generation AI suggests nutritious recipes taking into account the user's health condition and nutritional balance. This makes it possible to generate optimal recipes based on the user's preferences and behavioral data. Some or all of the above-mentioned processing in the generation unit may be performed using, or without, AI. For example, the generation unit may input the user's behavioral data into the generation AI and cause the generation AI to generate a recipe.

[0064] The ordering unit can order ingredients missing from the online supermarket for the recipe generated by the generation AI. For example, the ordering unit orders ingredients missing from the recipe generated by the generation AI from the online supermarket. The generation AI identifies the ingredients needed for the recipe and sends an order to the online supermarket. For example, the generation AI creates a list of ingredients needed for the recipe and automatically sends it to the online supermarket's ordering system. The ordering unit can also notify the user of ingredients missing from the recipe generated by the generation AI. For example, the generation AI notifies the user of missing ingredients and prompts the user to manually order them. This saves the user time and effort by automatically ordering the missing ingredients. Some or all of the above-mentioned processing in the ordering unit may be performed, for example, using AI, or may be performed without using AI. For example, the ordering unit sends an order to the online supermarket based on the recipe generated by the generation AI.

[0065] The ordering unit can notify the user of any ingredients missing from the recipe generated by the generation AI. For example, the ordering unit notifies the user of any ingredients missing from the recipe generated by the generation AI. The generation AI notifies the user of the missing ingredients and encourages the user to manually order them. For example, the generation AI sends a notification to the user's smartphone to inform them of the missing ingredients. The generation AI can also send a notification to the user's email address. This allows the user to know what ingredients they need by notifying them of the missing ingredients. Some or all of the above-mentioned processing in the ordering unit may be performed, for example, using AI, or may be performed without using AI. For example, the ordering unit notifies the user of any missing ingredients based on the recipe generated by the generation AI.

[0066] The generation unit allows the generation AI to suggest recipes based on the user's health condition or nutritional balance. For example, the generation unit allows the generation AI to suggest recipes that take into account the user's health condition and nutritional balance. The generation AI suggests nutritious recipes that take into account the user's health condition and nutritional balance. For example, the generation AI suggests recipes that contain necessary nutrients based on the user's health data. The generation AI can also suggest healthy meals based on the user's fitness data. This supports a healthy diet by suggesting recipes that take into account the user's health condition and nutritional balance. Some or all of the above-mentioned processing in the generation unit may be performed using, or without, AI. For example, the generation unit may input the user's health data into the generation AI and cause the generation AI to suggest recipes.

[0067] The detection unit can estimate the user's emotions and adjust the ingredient detection frequency based on the estimated user emotions. For example, the detection unit can estimate the user's emotions and adjust the ingredient detection frequency based on the estimated user emotions. For example, if the user is stressed, the detection unit can increase the ingredient detection frequency to always provide the latest information. Also, if the user is relaxed, the detection unit can decrease the ingredient detection frequency and reduce the frequency of notifications. Furthermore, if the user is in a hurry, the detection unit can prioritize detection of only important ingredients and provide information quickly. This allows for more appropriate information to be provided by adjusting the ingredient detection frequency according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the detection unit can be performed using AI, or without AI. For example, the detection unit can input the user's emotion data into the generation AI and have the generation AI perform emotion estimation.

[0068] The detection unit can detect the freshness of ingredients and prioritize notification of ingredients whose freshness has decreased. For example, the detection unit analyzes images taken by a camera and detects changes in the color and shape of ingredients to determine freshness. For example, the camera has high resolution and is positioned to cover the entire interior of the refrigerator. The camera periodically takes photos of ingredients in the refrigerator and sends the image data to the generation AI. The generation AI uses image analysis technology to detect changes in the color and shape of ingredients and determine freshness. The detection unit can also prioritize detection of ingredients whose freshness may decrease based on the shelf life of the ingredients. For example, the generation AI identifies ingredients whose freshness may decrease based on the shelf life data of the ingredients and prioritizes notification. The detection unit can also immediately notify the user if mold or discoloration is found on the surface of the ingredients. This prioritizes notification of ingredients whose freshness has decreased, thereby reducing food waste. Some or all of the above-mentioned processing by the detection unit may be performed, for example, using AI or without AI. For example, the detection unit can input image data captured by a camera into the generation AI and have the generation AI detect freshness.

[0069] The detection unit can simultaneously detect and manage the type and expiration date of ingredients. For example, the detection unit uses a camera to read the expiration date written on the ingredient packaging and register it in a database. For example, the camera has high resolution and is positioned to cover the entire interior of the refrigerator. The camera periodically photographs the ingredients in the refrigerator and sends the image data to the generation AI. The generation AI uses image analysis technology to identify the ingredient type and expiration date and register them in a database. The detection unit can also prioritize detecting ingredients that are approaching their expiration date and notify the user. For example, the generation AI identifies ingredients that are approaching their expiration date and notifies the user. The detection unit can also automatically remove ingredients that have passed their expiration date from the list and encourage them to discard them. This allows for more efficient ingredient management by simultaneously managing expiration dates. Some or all of the above-described processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input image data captured by a camera into the generation AI and have the generation AI detect the expiration date.

[0070] The detection unit can estimate the user's emotions and prioritize the ingredients to be detected based on the estimated user's emotions. For example, the detection unit estimates the user's emotions and prioritizes the ingredients to be detected based on the estimated user's emotions. For example, if the user is feeling stressed, it can prioritize detecting ingredients that have a relaxing effect. Furthermore, if the user is relaxed, it can prioritize detecting healthy ingredients. Furthermore, if the user is in a hurry, it can prioritize detecting ingredients that are easy to prepare. This prioritizes ingredients according to the user's emotions, allowing more appropriate ingredients to be provided. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the detection unit may be performed using an AI, or may be performed without an AI. For example, the detection unit can input the user's emotion data into the generation AI and cause the generation AI to perform emotion estimation.

[0071] The detection unit detects the temperature and humidity inside the refrigerator to maintain good food preservation conditions. The detection unit, for example, detects the temperature using a temperature sensor inside the refrigerator and adjusts the temperature to an appropriate level. For example, the temperature sensor is highly accurate and monitors the temperature inside the refrigerator in real time. If the temperature inside the refrigerator falls outside a set range, the temperature sensor activates a cooling system to adjust the temperature to an appropriate level. The detection unit can also detect humidity using a humidity sensor inside the refrigerator and adjust the humidity to an appropriate level. For example, the humidity sensor is highly accurate and monitors the humidity inside the refrigerator in real time. If the humidity inside the refrigerator falls outside a set range, the humidity sensor activates a humidification or dehumidification system to adjust the humidity to an appropriate level. Furthermore, the detection unit can monitor changes in temperature and humidity in real time and notify the user if an abnormality occurs. For example, if the temperature or humidity falls outside the set range, a notification is sent to the user's smartphone to notify the abnormality. This allows the temperature and humidity inside the refrigerator to be optimized to maintain good food preservation conditions. Some or all of the above-described processing in the detection unit may be performed using, for example, AI, or may be performed without using AI. For example, the detection unit may input temperature and humidity data to the generation AI and cause the generation AI to optimize the storage conditions.

[0072] The detection unit can detect the location information of ingredients and suggest placement that makes it easy for the user to access them. The detection unit, for example, uses a camera to detect the location of ingredients and suggests placing them in a location that makes access easy. For example, the camera has high resolution and is positioned to cover the entire interior of the refrigerator. The camera periodically photographs the ingredients in the refrigerator and sends the image data to the generation AI. The generation AI uses image analysis technology to identify the location of the ingredients and suggests placing them in a location that makes access easy. The detection unit can also suggest placement in a location that makes access easy based on the frequency of use of the ingredients. For example, the generation AI suggests placing frequently used ingredients in a location that makes access easy based on the user's past usage data. Furthermore, the detection unit can also suggest the optimal storage location based on the type of ingredient. For example, the generation AI identifies the optimal storage location based on the type of ingredient and suggests it to the user. This improves user convenience by detecting the location information of ingredients and suggesting a placement that makes access easy. Some or all of the above-mentioned processing in the detection unit may be performed using, for example, AI, or may be performed without using AI. For example, the detection unit can input image data captured by a camera into the generation AI and have the generation AI detect location information.

[0073] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, the analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is nervous, a simple, highly visible display method can be provided. If the user is relaxed, a display method including detailed information can be provided. Furthermore, if the user is in a hurry, a display method that focuses on the main points can be provided. This allows for more appropriate information to be provided by adjusting the display method of the analysis results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the display method.

[0074] The analysis unit can analyze the nutritional value of ingredients and suggest nutritionally balanced recipes. The analysis unit, for example, analyzes the nutritional components of ingredients and suggests well-balanced recipes. For example, the generation AI suggests nutritionally balanced recipes based on nutritional component data of ingredients. The analysis unit can also suggest recipes containing necessary nutrients based on the user's health condition. For example, the generation AI suggests recipes containing necessary nutrients based on the user's health data. The analysis unit can also suggest healthy meals based on the nutritional value of ingredients. For example, the generation AI suggests healthy meals based on the nutritional value data of ingredients. This supports a healthy diet by suggesting nutritionally balanced recipes. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input nutritional value data of ingredients to the generation AI and cause the generation AI to suggest recipes.

[0075] The analysis unit can analyze the expiration dates of ingredients and generate recipes that prioritize using ingredients with upcoming expiration dates. The analysis unit, for example, analyzes the expiration dates of ingredients and proposes recipes that prioritize using ingredients with upcoming expiration dates. For example, the generation AI proposes recipes that prioritize using ingredients with upcoming expiration dates based on ingredient expiration date data. The analysis unit can also propose recipes that reduce food waste by using ingredients with upcoming expiration dates. For example, the generation AI proposes recipes that reduce waste by using ingredients with upcoming expiration dates. Furthermore, the analysis unit can also propose recipes that reduce waste by using ingredients with upcoming expiration dates. This makes it possible to reduce food waste by prioritizing the use of ingredients with upcoming expiration dates. Some or all of the above-described processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input ingredient expiration date data into the generation AI and cause the generation AI to generate a recipe.

[0076] The analysis unit can estimate the user's emotions and prioritize the analysis results based on the estimated user emotions. For example, the analysis unit estimates the user's emotions and prioritizes the analysis results based on the estimated user emotions. For example, if the user is feeling stressed, it can prioritize analyzing ingredients that have a relaxing effect. Also, if the user is relaxed, it can prioritize analyzing ingredients that are good for health. Furthermore, if the user is in a hurry, it can prioritize analyzing ingredients that are easy to prepare. This prioritizes the analysis results according to the user's emotions, allowing for more appropriate information to be provided. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI determine the priorities.

[0077] The analysis unit can analyze allergen information of ingredients and suggest recipes suitable for users with allergies. The analysis unit, for example, analyzes allergen information of ingredients and suggests recipes suitable for users with allergies. For example, the generation AI suggests recipes suitable for users with allergies based on the allergen information data of ingredients. The analysis unit can also suggest recipes that do not contain allergens based on the user's allergy information. For example, the generation AI suggests recipes that do not contain allergens based on the user's allergy information. The analysis unit can also suggest ingredients suitable for users with allergies based on the allergen information of ingredients. For example, the generation AI suggests ingredients suitable for users with allergies based on the allergen information data of ingredients. This supports a healthy diet by suggesting recipes suitable for users with allergies. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input allergen information data of ingredients to the generation AI and cause the generation AI to suggest recipes.

[0078] The analysis unit can analyze information on the origin of ingredients and propose recipes that promote local production and consumption. The analysis unit, for example, analyzes information on the origin of ingredients and proposes recipes using local ingredients. For example, the generation AI proposes recipes using local ingredients based on the origin information data of ingredients. The analysis unit can also propose recipes that prioritize using local ingredients to promote local production and consumption. For example, the generation AI proposes recipes that prioritize using local ingredients. Furthermore, the analysis unit can also propose healthy recipes using local ingredients based on the origin information data of ingredients. For example, the generation AI proposes healthy recipes using local ingredients based on the origin information data of ingredients. This contributes to revitalizing the local economy by proposing recipes that promote local production and consumption. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input origin information data of ingredients to the generation AI and cause the generation AI to propose recipes.

[0079] The generation unit can estimate the user's emotions and adjust the difficulty of the recipe based on the estimated user's emotions. For example, the generation unit can estimate the user's emotions and adjust the difficulty of the recipe based on the estimated user's emotions. For example, if the user is stressed, the generation unit can suggest a simple and hassle-free recipe. If the user is relaxed, the generation unit can suggest a slightly more hassle-free recipe. Furthermore, if the user is in a hurry, the generation unit can suggest a recipe that can be made in a short time. This allows the user to adjust the difficulty of the recipe according to the user's emotions, thereby providing a more appropriate recipe. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the generation unit can be performed using an AI, for example, or without an AI. For example, the generation unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the difficulty of the recipe.

[0080] The generation unit can analyze the user's past cooking history and generate recipes that match the user's preferences. The generation unit, for example, analyzes the user's past cooking history and suggests recipes that match the user's preferences. For example, the generation AI suggests recipes that match the user's preferences based on the user's past cooking history data. The generation unit can also suggest similar recipes based on dishes the user has made in the past. For example, the generation AI suggests similar recipes based on data on dishes the user has made in the past. The generation unit can also analyze the user's preferences and suggest new recipes. For example, the generation AI suggests new recipes based on the user's preference data. This improves meal satisfaction by providing recipes that match the user's preferences. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's cooking history data into the generation AI and have the generation AI suggest recipes.

[0081] The generation unit can generate recipes according to the season and weather. The generation unit, for example, suggests recipes using ingredients according to the season. For example, the generation AI suggests recipes according to the season based on seasonal ingredient data. The generation unit can also suggest recipes using ingredients according to the weather. For example, the generation AI suggests recipes according to the weather based on weather data. The generation unit can also suggest healthy recipes according to the season and weather. For example, the generation AI suggests healthy recipes based on the season and weather data. This improves the user's satisfaction with their meal by providing recipes according to the season and weather. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input season and weather data into the generation AI and cause the generation AI to suggest recipes.

[0082] The generation unit can estimate the user's emotions and adjust the recipe portion sizes based on the estimated user emotions. For example, the generation unit can estimate the user's emotions and adjust the recipe portion sizes based on the estimated user emotions. For example, if the user is feeling stressed, the generation unit can suggest recipes that are satisfying with small portions. If the user is relaxed, the generation unit can suggest recipes with normal portion sizes. Furthermore, if the user is in a hurry, the generation unit can suggest recipes with small portions that are easy to make. This allows the user to adjust the portion sizes of the recipe according to their emotions, providing more appropriate meal sizes. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the generation unit can be performed using, for example, an AI, or without an AI. For example, the generation unit can input the user's emotion data into the generation AI and have the generation AI adjust the portion sizes of the recipe.

[0083] The generation unit can generate recipes according to the user's dietary restrictions and diet goals. The generation unit, for example, suggests recipes according to the user's dietary restrictions. For example, the generation AI suggests recipes according to the user's dietary restrictions based on the user's dietary restriction data. The generation unit can also suggest recipes according to the user's diet goals. For example, the generation AI suggests recipes according to the user's diet goals based on the user's diet goal data. The generation unit can also suggest recipes according to the user's health condition. For example, the generation AI suggests recipes according to the user's health data. This supports a healthy diet by providing recipes according to the user's dietary restrictions and diet goals. Some or all of the above-mentioned processing in the generation unit may be performed using, or without, AI. For example, the generation unit can input the user's dietary restriction data into the generation AI and have the generation AI suggest recipes.

[0084] The generation unit can generate recipes that take into account the preferences of family members or housemates. The generation unit, for example, suggests recipes that match the preferences of family members or housemates. For example, the generation AI suggests recipes that match the preferences based on preference data of family members or housemates. The generation unit can also suggest recipes that match the preferences based on the family members or housemates' past cooking history. For example, the generation AI suggests recipes that match the preferences based on the family members or housemates' past cooking history data. The generation unit can also suggest recipes that match the health status of family members or housemates. For example, the generation AI suggests recipes that match the health status based on the family members or housemates' health data. This improves meal satisfaction by providing recipes that match the preferences of family members or housemates. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input preference data of family members or housemates into the generation AI and cause the generation AI to suggest recipes.

[0085] The ordering unit can estimate the user's emotions and adjust the timing of an order based on the estimated user emotions. The ordering unit, for example, estimates the user's emotions and adjusts the timing of an order based on the estimated user emotions. For example, if the user is stressed, the ordering unit can place an order early to prevent ingredients from running out. Also, if the user is relaxed, the ordering unit can place an order at a normal time. Furthermore, if the user is in a hurry, the ordering unit can place an order immediately and arrange for ingredients to be delivered quickly. This allows the timing of an order to be adjusted according to the user's emotions, enabling ingredients to be ordered at a more appropriate time. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the ordering unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the ordering unit can input the user's emotion data into the generation AI and have the generation AI adjust the timing of the order.

[0086] The ordering unit can place an order at the optimal timing, taking into account price fluctuations of ingredients. The ordering unit, for example, places an order when the price of an ingredient drops. For example, the generation AI places an order when the price drops based on ingredient price data. The ordering unit can also place an order before the price of an ingredient rises. For example, the generation AI places an order before the price rises based on ingredient price data. Furthermore, the ordering unit can monitor ingredient price fluctuations in real time and place an order at the optimal timing. For example, the generation AI places an order at the optimal timing based on ingredient price fluctuation data. This allows for costs to be reduced by taking ingredient price fluctuations into account when placing an order. Some or all of the above-described processing in the ordering unit may be performed using, for example, AI, or may be performed without using AI. For example, the ordering unit can input ingredient price data into the generation AI and have the generation AI determine the timing of the order.

[0087] The ordering unit can check the inventory status of ingredients in real time and automatically replenish necessary ingredients. The ordering unit, for example, checks the inventory status of ingredients in a refrigerator in real time and automatically orders necessary ingredients. For example, the generation AI automatically orders necessary ingredients based on inventory data of ingredients in the refrigerator. The ordering unit can also immediately place an order and replenish ingredients when ingredients are low in stock. For example, the generation AI immediately places an order when ingredients are low based on ingredient inventory data. The ordering unit can also monitor the inventory status of ingredients and automatically place an order when necessary. For example, the generation AI automatically places an order when necessary based on ingredient inventory data. This makes it possible to check the inventory status of ingredients in real time and automatically replenish ingredients, thereby preventing shortages of ingredients. Some or all of the above-mentioned processing in the ordering unit may be performed using AI, for example, or may be performed without using AI. For example, the ordering unit can input ingredient inventory data into the generation AI and have the generation AI determine the timing of replenishment.

[0088] The ordering unit can estimate the user's emotions and prioritize the ingredients to be ordered based on the estimated user emotions. The ordering unit, for example, estimates the user's emotions and prioritizes the ingredients to be ordered based on the estimated user emotions. For example, if the user is feeling stressed, ingredients with a relaxing effect can be prioritized. Also, if the user is relaxed, ingredients that are good for health can be prioritized. Furthermore, if the user is in a hurry, ingredients that are easy to prepare can be prioritized. This allows for more appropriate ingredients to be provided by prioritizing the ingredients to be ordered based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the ordering unit can be performed using, for example, AI, or without AI. For example, the ordering unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of the ingredients to be ordered.

[0089] The ordering unit can analyze the user's purchase history and prioritize ordering ingredients that the user has purchased in the past. The ordering unit, for example, analyzes the user's past purchase history and prioritizes ordering ingredients that the user has purchased frequently. For example, the generation AI prioritizes ordering ingredients that the user has purchased frequently based on the user's past purchase history data. The ordering unit can also suggest and order similar ingredients based on ingredients the user has purchased in the past. For example, the generation AI suggests and orders similar ingredients based on the user's past purchase history data. The ordering unit can also prioritize ordering ingredients that match the user's preferences based on the user's purchase history. For example, the generation AI prioritizes ordering ingredients that match the user's preferences based on the user's purchase history data. In this way, by ordering ingredients based on the user's purchase history, ingredients that match the user's preferences can be provided. Some or all of the above-described processing in the ordering unit may be performed using, for example, AI, or may be performed without using AI. For example, the ordering unit can input the user's purchase history data into the generation AI and have the generation AI determine the ingredients to order.

[0090] The ordering unit can place an order that suits the user's schedule, taking into account the delivery time of the ingredients. The ordering unit, for example, selects the optimal delivery time based on the user's schedule and places an order. For example, the generation AI selects the optimal delivery time based on the user's schedule data and places an order. The ordering unit can also place an order for ingredients that avoids time periods when the user is not at home. For example, the generation AI places an order based on the user's schedule data, avoiding time periods when the user is not at home. The ordering unit can also adjust the delivery time of ingredients to suit the user's schedule and place an order. For example, the generation AI adjusts the delivery time based on the user's schedule data and places an order. This allows ingredients to be ordered according to the user's schedule, making it easier to receive the ingredients. Some or all of the above-described processing in the ordering unit may be performed using AI, or may be performed without using AI. For example, the ordering unit can input the user's schedule data into the generation AI and have the generation AI determine the delivery time. === Hard Collateral 1-1 === Each of the multiple elements, including the detection unit, analysis unit, generation unit, and ordering unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the detection unit is realized by using the camera 42 of the smart device 14 to photograph ingredients in the refrigerator and having the generation AI perform image analysis. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the ingredient information transmitted from the detection unit. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates a recipe based on the user's preferences and past cooking history. The ordering unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and orders missing ingredients from an online supermarket based on the generated recipe, or notifies the user. === Hard Collateral 1-2 === Each of the multiple elements, including the detection unit, analysis unit, generation unit, and order unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the detection unit is realized by using the camera 42 of the smart glasses 214 to photograph ingredients in the refrigerator and having the generation AI perform image analysis. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the ingredient information transmitted from the detection unit. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates a recipe based on the user's preferences and past cooking history. The order unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and orders missing ingredients from an online supermarket based on the generated recipe, or notifies the user. === Hard Collateral 1-3 === Each of the multiple elements, including the detection unit, analysis unit, generation unit, and ordering unit, described above, is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the detection unit is realized by using the camera 42 of the headset terminal 314 to photograph ingredients in the refrigerator and having the generation AI perform image analysis. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the ingredient information transmitted from the detection unit. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates a recipe based on the user's preferences and past cooking history. The ordering unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and orders missing ingredients from an online supermarket based on the generated recipe, or notifies the user. === Hard Collateral 1-4 === Each of the multiple elements, including the detection unit, analysis unit, generation unit, and ordering unit, described above, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the detection unit is realized by using the camera 42 of the robot 414 to photograph ingredients in the refrigerator and having the generation AI perform image analysis. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the ingredient information transmitted from the detection unit. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates a recipe based on the user's preferences and past cooking history. The ordering unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and orders missing ingredients from an online supermarket based on the generated recipe, or notifies the user.

[0091] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0092] The analysis unit can estimate the user's food preferences and suggest ingredient combinations based on the estimated preferences. For example, if the user prefers a particular ingredient, the analysis unit can suggest recipes centered around that ingredient. Also, if the user prefers a particular cooking style, the analysis unit can suggest ingredient combinations that suit that style. Furthermore, the analysis unit can suggest new ingredient combinations based on dishes the user has enjoyed in the past. This improves meal satisfaction by suggesting ingredient combinations that suit the user's preferences. Some or all of the above-mentioned processing by the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's preference data into a generation AI and have the generation AI suggest ingredient combinations.

[0093] The generation unit can estimate the user's emotions and suggest a food preservation method based on the estimated emotions. For example, if the user is stressed, an easy preservation method can be suggested. Furthermore, if the user is relaxed, a more time-consuming preservation method can be suggested. Furthermore, if the user is in a hurry, a quick preservation method can be suggested. This makes food preservation more efficient by suggesting a preservation method according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the generation unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the generation unit can input the user's emotion data into the generation AI and have the generation AI suggest a preservation method.

[0094] The detection unit can detect the frequency of ingredient use and place frequently used ingredients in locations that make them easy to access in order to optimize the placement of ingredients in the refrigerator. For example, the generation AI can identify frequently used ingredients based on the user's past usage data and suggest placing them in locations that make them easy to access. The detection unit can also suggest optimal storage locations based on the type of ingredient. For example, the generation AI can identify optimal storage locations based on the type of ingredient and suggest them to the user. Furthermore, the detection unit can take into account the freshness of the ingredients and prioritize placing ingredients that are likely to lose freshness in locations that make them easy to access. This optimizes the placement of ingredients, improving user convenience. Some or all of the above-mentioned processing in the detection unit may be performed using AI, for example, or may be performed without using AI. For example, the detection unit can input ingredient usage data into the generation AI and cause the generation AI to optimize the placement.

[0095] The generation unit can estimate the user's emotions and suggest recipes that take into account the expiration dates of ingredients based on the estimated emotions. For example, if the user is feeling stressed, the generation unit can suggest simple recipes using ingredients with upcoming expiration dates. Furthermore, if the user is relaxed, the generation unit can suggest elaborate recipes using ingredients with upcoming expiration dates. Furthermore, if the user is in a hurry, the generation unit can suggest recipes that can be made quickly using ingredients with upcoming expiration dates. This reduces food waste by suggesting recipes that take expiration dates into account based on the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit can be performed using, for example, an AI. For example, the generation unit can input the user's emotion data into the generation AI and have the generation AI suggest recipes.

[0096] The analysis unit can analyze the nutritional value of ingredients and suggest nutritionally balanced recipes that suit the user's health condition. For example, the generation AI can suggest recipes that suit the user's health condition based on the nutritional data of ingredients. The analysis unit can also suggest recipes that contain necessary nutrients based on the user's health data. For example, the generation AI can suggest recipes that contain necessary nutrients based on the user's health data. The analysis unit can also suggest healthy meals based on the nutritional value of ingredients. For example, the generation AI can suggest healthy meals based on the nutritional value data of ingredients. This supports a healthy diet by suggesting nutritionally balanced recipes. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input nutritional value data of ingredients into the generation AI and cause the generation AI to suggest recipes.

[0097] The detection unit can detect the location information of ingredients in the refrigerator and suggest an arrangement that makes it easy for the user to take them out. For example, the detection unit can detect the location of ingredients using a camera and suggest placing ingredients in a location that makes them easy to take out. It can also suggest placing ingredients in a location that makes them easy to take out based on how often the ingredients are used. It can also suggest the optimal storage location depending on the type of ingredient. This improves user convenience by detecting the location information of ingredients and suggesting an arrangement that makes them easy to take out. Some or all of the above-mentioned processing in the detection unit may be performed using AI, for example, or may be performed without using AI. For example, the detection unit can input image data captured by a camera to the generation AI and have the generation AI detect the location information.

[0098] The generation unit can estimate the user's emotions and adjust the recipe portion sizes based on the estimated emotions. For example, if the user is feeling stressed, the generation unit can suggest recipes with small portions that are satisfying. If the user is relaxed, the generation unit can suggest recipes with normal portion sizes. Furthermore, if the user is in a hurry, the generation unit can suggest recipes with small portions that are easy to make. This allows the user to provide more appropriate meal portions by adjusting the recipe portion sizes according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the generation unit can be performed using AI, for example, or without AI. For example, the generation unit can input the user's emotion data into the generation AI and have the generation AI adjust the recipe portion sizes.

[0099] The analysis unit can analyze allergen information of ingredients and suggest recipes suitable for users with allergies. For example, the generation AI suggests recipes suitable for users with allergies based on the allergen information data of ingredients. The analysis unit can also suggest recipes that do not contain allergens based on the user's allergy information. For example, the generation AI suggests recipes that do not contain allergens based on the user's allergy information. Furthermore, the analysis unit can suggest ingredients suitable for users with allergies based on the allergen information of ingredients. This supports a healthy diet by suggesting recipes suitable for users with allergies. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input allergen information data of ingredients to the generation AI and cause the generation AI to suggest recipes.

[0100] The generation unit can estimate the user's emotions and adjust the difficulty of the recipe based on the estimated emotions. For example, if the user is feeling stressed, it can suggest a simple and hassle-free recipe. Also, if the user is relaxed, it can suggest a slightly more hassle-free recipe. Furthermore, if the user is in a hurry, it can suggest a recipe that can be made in a short time. By adjusting the difficulty of the recipe according to the user's emotions, more appropriate recipes can be provided. The emotion estimation is realized using an emotion estimation function, for example, with an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the generation unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the generation unit can input the user's emotion data into the generation AI and have the generation AI adjust the difficulty of the recipe.

[0101] The ordering unit can place an order at the optimal timing, taking into account fluctuations in the price of ingredients. For example, it can place an order when the price of ingredients drops. It can also place an order before the price of ingredients rises. It can also monitor price fluctuations of ingredients in real time and place an order at the optimal timing. This allows costs to be reduced by taking price fluctuations of ingredients into account when placing an order. Some or all of the above-mentioned processing in the ordering unit may be performed using, for example, AI, or may be performed without using AI. For example, the ordering unit can input price data of ingredients into the generation AI and have the generation AI decide the timing of the order.

[0102] The processing flow of the second embodiment will be briefly explained below.

[0103] Step 1: The detection unit detects ingredients in the refrigerator. For example, a camera installed inside the refrigerator takes a picture of the ingredients, and the generation AI analyzes the image to identify the type and quantity of ingredients. The camera has high resolution and is positioned to cover the entire interior of the refrigerator, taking pictures periodically. The generation AI uses object detection algorithms and image classification algorithms to identify the ingredients in the image and determine their type and quantity. Step 2: The analysis unit analyzes the food information detected by the detection unit. For example, the state of food in the refrigerator is managed based on the food information analyzed by the generation AI. The analysis unit analyzes and manages the freshness and expiration date of the food. Step 3: The generation unit generates recipes based on the information analyzed by the analysis unit. For example, the generation AI generates recipes based on information previously registered by the user and past behavioral data. The generation AI suggests optimal recipes based on the user's preferences and past cooking history. It can also suggest recipes that take into account the user's health condition and nutritional balance. Step 4: The ordering unit orders missing ingredients based on the recipe generated by the generation unit. For example, the generation AI orders missing ingredients from an online supermarket. The generation AI identifies the ingredients needed for the recipe and sends an order to the online supermarket. It can also notify the user of missing ingredients and prompt the user to order them manually.

[0104] 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.

[0105] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). 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 (e.g., still image data or video data). 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 one or more data formats of voice data, text data, image data, etc. 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 may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0106] 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.

[0107] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0108] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0109] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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).

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0122] 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.

[0123] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0124] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0125] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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).

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification 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 identification processing unit 290 using these models.

[0135] 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.

[0136] 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.

[0137] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0138] 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.

[0139] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0140] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0141] 7, a 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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).

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also 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 perform the same process as the identification processing unit 290 using these models.

[0152] 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.

[0153] 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.

[0154] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0155] 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.

[0156] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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).

[0161] 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.

[0162] 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."

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] The hardware resource for executing a specific process can be any of the following processors: A CPU is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A dedicated electrical circuit, such as a field-programmable gate array (FPGA), a programmable logic device (PLD), or an application-specific integrated circuit (ASIC), is a processor with a circuit configuration specifically designed to execute a specific process. Each processor has built-in or connected memory, and uses the memory to execute the specific process.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] [Explanation of symbols]

[0176] 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. a detection unit that detects food ingredients in the refrigerator; an analysis unit that analyzes the ingredient information detected by the detection unit; a generation unit that generates a recipe based on the information analyzed by the analysis unit; an ordering unit that orders missing ingredients based on the recipe generated by the generation unit; Equipped with A system characterized by:

2. The detection unit The food in the refrigerator is photographed with a camera, and the image is analyzed by the generative AI.

2. The system of claim 1.

3. The generation unit The AI ​​generates recipes based on the information the user has registered in advance or their past behavioral data.

2. The system of claim 1.

4. The ordering unit Order ingredients missing from an online supermarket for the recipe generated by the AI 2. The system of claim 1.

5. The ordering unit The AI ​​notifies the user of any missing ingredients in the generated recipe.

2. The system of claim 1.

6. The generation unit Generative AI suggests recipes based on the user's health status or nutritional balance 2. The system of claim 1.

7. The detection unit Estimate the user's emotions and adjust the frequency of ingredient detection based on the estimated user emotions.

2. The system of claim 1.

8. The detection unit Detects the freshness of ingredients and gives priority to notifying ingredients that have lost their freshness 2. The system of claim 1.

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