System

Abstract

To provide a system which takes into account a schedule and likes and dislikes of a family, grasps a stock status of a refrigerator to suggest a menu with nutrient balance, and which even places an order of food materials.SOLUTION: A system includes a schedule grasping part, a stock analysis part, a menu suggesting part, and an order placing part. The schedule grasping part grasps a schedule and likes and dislikes of a family. The stock analysis part checks a stock status of a refrigerator using photographic analysis on the basis of the grasped information. The menu suggesting part suggests a menu according to a reference for nutrient balance, on the basis of the analysis by the stock analysis part. The order placing part places an order at a net supermarket, based on the suggestion of the menu suggesting part.SELECTED DRAWING: Figure 1

Description

[Technical field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including a description and related instruction sentence regarding 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] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] With conventional technology, it was difficult to take into account family schedules and likes and dislikes, grasp the inventory status of the refrigerator, suggest nutritionally balanced menus, and even order the ingredients.

[0005] The system of the embodiment aims to take into account family schedules and likes and dislikes, grasp the inventory status of the refrigerator, propose nutritionally balanced menus, and even order the ingredients. [Means for solving the problem]

[0006] The system according to the embodiment includes a schedule grasping unit, an inventory analysis unit, a menu suggestion unit, and an ordering unit. The schedule grasping unit grasps the schedule and likes and dislikes of family members. The inventory analysis unit checks the inventory status of the refrigerator through photo analysis based on the grasped information. The menu suggestion unit suggests a menu based on nutritional balance criteria based on the analysis by the inventory analysis unit. The ordering unit places an order with the online supermarket based on the suggestion by the menu suggestion unit. Effect of the Invention

[0007] The system according to the embodiment takes into account the schedules and likes and dislikes of family members, grasps the stock situation in the refrigerator, proposes nutritionally balanced menus, and even orders ingredients. [Brief description 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. [Diagram 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. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 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. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 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 PREFERRED EMBODIMENTS

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

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

[0011] In the following embodiments, a signed processor (hereinafter simply referred to as a "processor") may be one arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be one 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 signed RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by the processor.

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

[0014] In the following embodiments, a communication I / F (Interface) with a code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "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, the RAM 30, and the 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 (e.g., 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., voice and / or text). Display 40A displays visible information such as text and images according to instructions from processor 46. Speaker 40B outputs voice according to 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 Complementary Metal-Oxide-Semiconductor (CMOS) image sensor or a Charge Coupled Device (CCD) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

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

[0024] As shown in Fig. 2, 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. The specific process program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific process program 56 from the storage 32, and executes the read specific process 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 process 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[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 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. Also, the data processing device 12 may be a server device, or may be a terminal device (e.g., a mobile phone, a robot, a home appliance, etc.) owned by a user. Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The system according to the embodiment of the present invention is a system that grasps the schedule and likes and dislikes of the family, checks the stock of ingredients in the refrigerator, and proposes a menu for one week that takes into account nutritional balance. This system also handles the procurement of one week's worth of ingredients, which is difficult for dual-income families, and even orders them from online supermarkets, significantly reducing weekend housework. Since the stock status of the refrigerator can be constantly grasped through photo analysis, it is possible to propose a different menu even if there is a sudden shortage of ingredients. In addition, by working closely with the local community, it contributes to regional revitalization, reducing food waste, and SDGs. For example, in order to grasp the schedule and likes and dislikes of the family, the schedule grasper collects the family's plans and food preferences. For example, information is obtained from the family's calendar app and memo app, and AI analyzes it. Next, based on the information grasped by the schedule grasper, the inventory analysis unit checks the inventory status of the refrigerator through photo analysis. A photo of the inside of the refrigerator is taken, and AI performs image analysis to identify the stock of ingredients. For example, the types and amounts of vegetables, meat, dairy products, etc. are grasped from the photos of the inside of the refrigerator. Then, based on the analysis by the inventory analysis unit, the menu proposal unit considers nutritional balance and proposes a menu for one week. AI creates a balanced menu, taking into account the family's schedule, likes and dislikes, and available ingredients. For example, it proposes a rich variety of menus, such as a vegetable-centered menu on Monday, fish dishes on Tuesday, and meat dishes on Wednesday. In addition, the ordering department places an order with the online supermarket based on the proposals of the menu proposal department. The AI ​​lists the necessary ingredients, accesses the online supermarket's website, and completes the order. This saves dual-income families the trouble of procuring ingredients, and significantly reduces weekend housework. In addition, since the refrigerator's inventory status can be constantly monitored through photo analysis, it is also possible to respond to sudden shortages of ingredients. For example, even if an unexpected visitor arrives, the inventory analysis department checks the status of the refrigerator again and proposes a different menu. This system contributes to regional revitalization, food waste reduction, and SDGs by working closely with the local community. For example, by partnering with local farmers and food manufacturers and prioritizing the use of locally produced ingredients, it contributes to the revitalization of the local economy. In addition, the inventory analysis department proposes menus that use the ingredients in the refrigerator without waste, which also leads to a reduction in food waste.This system can understand the family's schedules and likes and dislikes, check the ingredients in the refrigerator, and suggest a week's worth of menus that take nutritional balance into consideration, significantly reducing the housework burden for dual-income families.

[0029] The menu proposal system according to the embodiment includes a schedule grasping unit, an inventory analysis unit, a menu proposal unit, and an ordering unit. The schedule grasping unit grasps the schedule and likes and dislikes of the family members. The family schedule includes, for example, work schedules, school events, family events, etc., but is not limited to such examples. The schedule grasping unit, for example, acquires information from a calendar app or a memo app of the family members, and the AI ​​analyzes it. For example, the AI ​​analyzes the schedule acquired from the calendar app to grasp the family schedule. In addition, the schedule grasping unit acquires information from a memo app of the family members to grasp the likes and dislikes of the family members, and the AI ​​analyzes it. For example, the AI ​​analyzes the food preferences of the family members recorded in the memo app to grasp the likes and dislikes. The inventory analysis unit checks the inventory status of the refrigerator by photo analysis. The inventory status in the refrigerator includes, for example, the types and amounts of vegetables, meat, dairy products, etc., but is not limited to such examples. The inventory analysis unit, for example, takes a photo of the inside of the refrigerator, and the AI ​​performs image analysis to identify the ingredients in stock. For example, the AI ​​analyzes the types and amounts of vegetables, meat, dairy products, etc. from photos of the inside of the refrigerator to grasp the inventory status. The menu suggestion unit proposes a menu for one week taking into consideration nutritional balance. The menu includes, for example, a vegetable-centered menu on Monday, fish dishes on Tuesday, and meat dishes on Wednesday, but is not limited to such examples. The menu suggestion unit generates a balanced menu by taking into consideration the family's schedule, likes and dislikes, and ingredients in stock. For example, the AI ​​makes the menu for busy days simple based on the family's schedule. In addition, the AI ​​proposes a menu that the whole family can enjoy based on the likes and dislikes of the family. Furthermore, the AI ​​proposes a menu that uses ingredients in the refrigerator without waste based on ingredients in stock. The ordering unit places an order with the online supermarket. The ordering includes, for example, listing the necessary ingredients and accessing the online supermarket's website to complete the order, but is not limited to such examples. In the ordering unit, the AI ​​lists the necessary ingredients based on the proposal of the menu suggestion unit, and accesses the online supermarket's website to complete the order. For example, AI can list the ingredients needed for a meal and access an online supermarket website to complete the order.As a result, the menu suggestion system according to the embodiment can grasp the schedule and likes and dislikes of the family, check the ingredients in the refrigerator, suggest a week's menu that takes into account nutritional balance, and even order the ingredients, significantly reducing the housework burden of dual-income families.

[0030] The schedule grasping unit grasps the schedule and likes and dislikes of the family members. The family schedule includes, for example, work schedules, school events, family events, etc., but is not limited to such examples. The schedule grasping unit, for example, obtains information from the family's calendar app or memo app, and the AI ​​analyzes it. Specifically, the AI ​​analyzes the schedule obtained from the calendar app and grasps the family's schedule. The AI ​​uses natural language processing technology to analyze the contents of the schedule written on the calendar and classify and organize the schedule of each member. For example, keywords such as "meeting" and "class" are extracted, and the importance and time period of each schedule are grasped. In addition, the schedule grasping unit obtains information from the family's memo app and the AI ​​analyzes it in order to grasp the likes and dislikes of the family members. For example, the AI ​​analyzes the food preferences of the family members recorded in the memo app and grasps the likes and dislikes. The AI ​​analyzes the text data written in the memo app and extracts information such as "disliked ingredients" and "favorite dishes". In addition, the AI ​​learns the food preferences of each member based on the past meal history and feedback from the family, and performs more accurate analysis. This enables the schedule understanding unit to accurately understand the family's schedules and likes and dislikes, and provide basic data for menu suggestions.

[0031] The inventory analysis unit checks the inventory status of the refrigerator by photo analysis. The inventory status in the refrigerator includes, for example, the types and amounts of vegetables, meat, dairy products, etc., but is not limited to such examples. For example, the inventory analysis unit takes a photo of the inside of the refrigerator, and the AI ​​performs image analysis to identify the ingredients in stock. Specifically, the AI ​​analyzes the types and amounts of vegetables, meat, dairy products, etc. from the photo of the inside of the refrigerator to grasp the inventory status. The AI ​​uses image recognition technology to automatically identify the ingredients in the refrigerator and identify the type and amount of each ingredient. For example, the AI ​​analyzes the shape and color of vegetables and the labels on the packages to identify ingredients such as cabbage, tomatoes, and milk. In addition, the AI ​​predicts the expiration date and frequency of use of ingredients based on past inventory data and consumption patterns, and improves the efficiency of inventory management. Furthermore, the inventory analysis unit can collect environmental data such as the temperature and humidity in the refrigerator and monitor the storage conditions of ingredients. As a result, the inventory analysis unit can accurately grasp the inventory status in the refrigerator, reduce food waste, and achieve efficient inventory management.

[0032] The menu suggestion unit proposes a menu for one week, taking into consideration nutritional balance. For example, the menu may include, but is not limited to, a vegetable-centered menu on Monday, fish dishes on Tuesday, and meat dishes on Wednesday. The menu suggestion unit generates a balanced menu by using the AI ​​to consider the family's schedule, likes and dislikes, and ingredients in stock. Specifically, the AI ​​makes the menu for busy days simpler based on the family's schedule. The AI ​​also proposes a menu that the whole family can enjoy based on the family's likes and dislikes. Furthermore, the AI ​​proposes a menu that uses the ingredients in the refrigerator without waste based on the ingredients in stock. The AI ​​calculates the nutritional value of each ingredient based on knowledge of nutritional science and proposes a balanced meal. For example, the AI ​​plans a menu for one week, taking into consideration nutrients such as vitamins, minerals, and protein. The AI ​​can also propose a menu that utilizes seasonal ingredients, taking into consideration the characteristics of the season and region. Furthermore, the menu suggestion unit collects feedback from the family and continuously improves the accuracy of the suggestions. This allows the menu suggestion unit to support the health of the family and provide the enjoyment of meals.

[0033] The ordering unit places an order with the online supermarket. For example, the ordering includes, but is not limited to, listing the necessary ingredients and accessing the online supermarket's website to complete the order. The ordering unit lists the necessary ingredients based on the proposal of the menu suggestion unit by the AI, and accesses the online supermarket's website to complete the order. Specifically, the AI ​​lists the ingredients necessary for the menu, and accesses the online supermarket's website to complete the order. The AI ​​automatically generates a list of necessary ingredients based on the information provided by the menu suggestion unit, and specifies the quantity and brand of each ingredient. The AI ​​also obtains inventory status and price information of the online supermarket in real time and selects the optimal purchase destination. Furthermore, the AI ​​takes into account past purchase history and user preferences, and utilizes repeat purchase and special sale information to efficiently place an order. The ordering unit checks the order details and accesses the online supermarket's website to complete the order. As a result, the ordering unit can quickly and accurately order the necessary ingredients, reducing the burden of housework. Furthermore, the ordering unit can monitor the progress of the order and delivery schedule, and notify the user. This enables the ordering department to centrally manage the process from ordering ingredients to delivery, improving user convenience.

[0034] The schedule understanding unit can obtain information from a family member's calendar app or memo app, and the AI ​​can analyze the information. The schedule understanding unit, for example, obtains information from a family member's calendar app, and the AI ​​can analyze the information. For example, the AI ​​analyzes the plans obtained from the calendar app and understands the family member's schedule. The schedule understanding unit can also obtain information from a family member's memo app, and the AI ​​can analyze the information. For example, the AI ​​analyzes the family member's food preferences recorded in the memo app and understands their likes and dislikes. This makes it possible to accurately understand the family member's schedule and likes and dislikes. Some or all of the above-mentioned processing in the schedule understanding unit may be performed, for example, using AI, or may be performed without using AI. For example, the schedule understanding unit can input information obtained from the calendar app to AI, and have the AI ​​execute analysis of the schedule.

[0035] The inventory analysis unit can take a photo of the inside of the refrigerator, and the AI ​​can perform image analysis to identify the ingredients in stock. The inventory analysis unit can, for example, take a photo of the inside of the refrigerator, and the AI ​​can perform image analysis to identify the ingredients in stock. For example, the AI ​​can analyze the types and amounts of vegetables, meat, dairy products, etc. from the photo of the inside of the refrigerator to grasp the inventory status. The inventory analysis unit can also take a photo of the inside of the refrigerator periodically to keep the inventory status always up to date. For example, the inventory analysis unit can take a photo every time the refrigerator door is opened and closed, and the AI ​​can analyze the photo to grasp the inventory status in real time. This makes it possible to accurately grasp the ingredients in stock in the refrigerator. Some or all of the above-mentioned processing in the inventory analysis unit can be performed, for example, using AI, or can be performed without using AI. For example, the inventory analysis unit can input a photo of the inside of the refrigerator to AI and have AI perform image analysis.

[0036] The menu suggestion unit can propose a menu for one week based on a criterion for nutritional balance. The menu suggestion unit proposes a menu for one week based on a criterion for nutritional balance, for example. For example, a wide variety of menus are proposed, such as a vegetable-centered menu on Monday, fish dishes on Tuesday, and meat dishes on Wednesday. The menu suggestion unit also generates a balanced menu taking into consideration the family's schedule, likes and dislikes, and ingredients in stock. For example, the AI ​​makes a menu for busy days simple based on the family's schedule. The AI ​​also proposes a menu that the whole family can enjoy based on the family's likes and dislikes. Furthermore, the AI ​​proposes a menu that uses ingredients in the refrigerator without waste based on ingredients in stock. This makes it possible to propose a nutritionally balanced menu. Some or all of the above-mentioned processing in the menu suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the menu suggestion unit can input information on the family's schedule, likes and dislikes, and ingredients in stock into the AI, and have the AI ​​generate the menu.

[0037] The ordering unit can place an order to an online supermarket. The ordering unit, for example, places an order to an online supermarket. For example, the ordering unit lists the necessary ingredients and accesses the online supermarket's website to complete the order. The ordering unit can also list the necessary ingredients based on a proposal by the menu suggestion unit using AI, and accesses the online supermarket's website to complete the order. For example, the AI ​​lists the ingredients necessary for a menu, and accesses the online supermarket's website to complete the order. This automates the ordering of ingredients, thereby reducing the burden of housework. Some or all of the above-mentioned processing in the ordering unit may be performed using AI, or may be performed without using AI. For example, the ordering unit can input a list of necessary ingredients into AI and have the AI ​​execute the ordering procedure.

[0038] The inventory analysis unit can check the situation in the refrigerator again and propose a different menu to deal with a sudden shortage of ingredients. The inventory analysis unit can check the situation in the refrigerator again and propose a different menu to deal with a sudden shortage of ingredients, for example. Even if an unexpected visitor comes, the inventory analysis unit can check the situation in the refrigerator again and propose a different menu. The inventory analysis unit can also periodically check the inventory status in the refrigerator to prepare for a sudden shortage of ingredients. For example, the inventory analysis unit can grasp the inventory status in real time by taking a photo every time the refrigerator door is opened and closed and analyzing it with AI. This makes it possible to flexibly deal with a sudden shortage of ingredients. Some or all of the above-mentioned processing in the inventory analysis unit can be performed, for example, using AI or can be performed without using AI. For example, the inventory analysis unit can input a photo of the inside of the refrigerator to AI and have AI execute an analysis of the inventory status.

[0039] The menu proposal unit can cooperate with local farmers and food manufacturers to propose a menu that prioritizes the use of locally produced ingredients. The menu proposal unit, for example, cooperates with local farmers and food manufacturers to propose a menu that prioritizes the use of locally produced ingredients. For example, the menu proposal unit cooperates with local farmers to propose a menu that uses fresh locally produced vegetables and fruits. The menu proposal unit can also cooperate with local food manufacturers to propose a menu that uses locally produced dairy products and meat. For example, the menu proposal unit cooperates with local dairy manufacturers to propose a menu that uses fresh milk and cheese. This can contribute to revitalizing the local economy and reducing food waste. Some or all of the above-mentioned processing in the menu proposal unit may be performed, for example, using AI, or may be performed without using AI. For example, the menu proposal unit can input information about local farmers and food manufacturers into AI and have the AI ​​execute the menu proposal.

[0040] The schedule comprehension unit can analyze the past schedule history of the family and select an appropriate schedule acquisition method. The schedule comprehension unit, for example, analyzes the past schedule history of the family and selects an appropriate schedule acquisition method. For example, frequently occurring events are automatically incorporated into the schedule from the past schedule of the family. The schedule comprehension unit can also propose the most efficient schedule pattern based on the past schedule history of the family. Furthermore, the schedule comprehension unit can analyze the past schedule history of the family and adjust the schedule to avoid overlapping events. This makes it possible to obtain an optimal schedule based on the past schedule history. A part or all of the above-mentioned processing in the schedule comprehension unit may be performed, for example, using AI, or may be performed without using AI. For example, the schedule comprehension unit can input the past schedule history into AI and cause AI to perform schedule analysis.

[0041] The schedule understanding unit can perform filtering based on the health condition and life rhythm of the family when acquiring the schedule. For example, the schedule understanding unit performs filtering based on the health condition and life rhythm of the family when acquiring the schedule. For example, the schedule understanding unit proposes a reasonable schedule taking into account the health condition of the family. The schedule understanding unit can also adjust the schedule to avoid early morning and late night events in accordance with the family's life rhythm. Furthermore, the schedule understanding unit can also incorporate appropriate exercise and rest times into the schedule based on the health data of the family. This makes it possible to propose a schedule according to the health condition and life rhythm of the family. A part or all of the above-mentioned processing in the schedule understanding unit may be performed using, for example, AI, or may be performed without using AI. For example, the schedule understanding unit can input the health data of the family into AI and cause AI to execute filtering of the schedule.

[0042] The schedule understanding unit can preferentially acquire a highly relevant schedule in consideration of the geographical location information of the family when acquiring a schedule. For example, the schedule understanding unit preferentially acquires a highly relevant schedule in consideration of the geographical location information of the family when acquiring a schedule. For example, an event close to the current location of the family members is preferentially incorporated into the schedule. In addition, the schedule understanding unit can also preferentially acquire an event related to a place frequently visited by the family members. Furthermore, the schedule understanding unit can also preferentially acquire an event related to a place where the family members can easily move. In this way, a highly relevant schedule based on the geographical location information can be acquired. A part or all of the above-mentioned processing in the schedule understanding unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the schedule understanding unit can input the geographical location information of the family members to the AI ​​and cause the AI ​​to acquire the schedule.

[0043] The schedule grasping unit can analyze the social media activities of the family members when acquiring the schedule and acquire the related schedule. The schedule grasping unit can, for example, analyze the social media activities of the family members when acquiring the schedule and acquire the related schedule. For example, events in which the family members have shown interest on social media are incorporated into the schedule. The schedule grasping unit can also preferentially acquire events of accounts that the family members follow on social media. Furthermore, the schedule grasping unit can also incorporate events that the family members plan to participate in on social media into the schedule. This makes it possible to acquire a highly relevant schedule based on the social media activities. A part or all of the above-mentioned processing in the schedule grasping unit can be performed, for example, using AI or can be performed without using AI. For example, the schedule grasping unit can input the social media activity data of the family members into AI and cause AI to acquire the schedule.

[0044] The inventory analysis unit can perform inventory analysis taking into account the expiration date of the ingredients in the refrigerator. The inventory analysis unit performs inventory analysis taking into account the expiration date of the ingredients in the refrigerator, for example. For example, the inventory analysis unit prioritizes analysis of ingredients in the refrigerator that are close to their expiration date and encourages their use. The inventory analysis unit can also suggest an optimal order of use based on the expiration date of the ingredients in the refrigerator. Furthermore, the inventory analysis unit can also make suggestions to reduce waste by taking into account the expiration date of the ingredients in the refrigerator. This makes it possible to perform inventory analysis taking into account the expiration date. Some or all of the above-mentioned processing in the inventory analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the inventory analysis unit can input expiration date data of ingredients in the refrigerator to AI and have AI perform inventory analysis.

[0045] The inventory analysis unit can evaluate the nutritional value and storage state of ingredients during inventory analysis. The inventory analysis unit, for example, evaluates the nutritional value and storage state of ingredients during inventory analysis. For example, the inventory analysis unit evaluates the nutritional value of ingredients in the refrigerator and proposes a balanced menu. The inventory analysis unit can also evaluate the storage state of ingredients in the refrigerator and identify usable ingredients. Furthermore, the inventory analysis unit can also propose an optimal use method based on the nutritional value and storage state of ingredients in the refrigerator. This makes it possible to perform inventory analysis taking into account the nutritional value and storage state of ingredients. Some or all of the above-mentioned processing in the inventory analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the inventory analysis unit can input nutritional value data and storage state data of ingredients in the refrigerator to AI and have AI perform inventory analysis.

[0046] The inventory analysis unit can perform the analysis while taking into consideration the arrangement of ingredients in the refrigerator during the inventory analysis. The inventory analysis unit, for example, performs the analysis while taking into consideration the arrangement of ingredients in the refrigerator during the inventory analysis. For example, the inventory analysis unit proposes an optimal order of use based on the arrangement of ingredients in the refrigerator. The inventory analysis unit can also perform efficient inventory management while taking into consideration the arrangement of ingredients in the refrigerator. Furthermore, the inventory analysis unit can also make suggestions to reduce waste based on the arrangement of ingredients in the refrigerator. This enables inventory analysis while taking into consideration the arrangement of ingredients. Some or all of the above-mentioned processing in the inventory analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the inventory analysis unit can input the arrangement data of ingredients in the refrigerator to AI and have the AI ​​perform the inventory analysis.

[0047] The inventory analysis unit can improve the accuracy of the analysis by referring to the purchase history of ingredients during inventory analysis. The inventory analysis unit can improve the accuracy of the analysis by referring to the purchase history of ingredients during inventory analysis, for example. For example, the accuracy of the inventory analysis is improved based on the purchase history of ingredients. The inventory analysis unit can also suggest an optimal use order by referring to the purchase history of ingredients. Furthermore, the inventory analysis unit can also make suggestions to reduce waste based on the purchase history of ingredients. This enables highly accurate inventory analysis based on the purchase history. Some or all of the above-mentioned processing in the inventory analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the inventory analysis unit can input the purchase history data of ingredients to AI and have the AI ​​perform the inventory analysis.

[0048] The menu suggestion unit can make a suggestion taking into consideration the health condition and nutritional balance of the family when proposing a menu. The menu suggestion unit, for example, makes a suggestion taking into consideration the health condition and nutritional balance of the family when proposing a menu. For example, the menu suggestion unit proposes a balanced menu taking into consideration the health condition of the family. The menu suggestion unit can also propose a menu containing necessary nutrients taking into consideration the nutritional balance of the family. Furthermore, the menu suggestion unit can also propose an optimal menu based on the health data of the family. This makes it possible to propose a menu that takes into consideration the health condition and nutritional balance of the family. A part or all of the above-mentioned processing in the menu suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the menu suggestion unit can input the health data of the family into AI and have the AI ​​execute the menu suggestion.

[0049] The menu proposal unit can propose a menu according to the season and weather when proposing a menu. The menu proposal unit, for example, proposes a menu according to the season and weather when proposing a menu. For example, a menu using seasonal ingredients according to the season is proposed. The menu proposal unit can also propose hot dishes and cold dishes according to the weather. Furthermore, the menu proposal unit can also propose an optimal menu taking into account the season and weather. This makes it possible to propose an optimal menu according to the season and weather. Some or all of the above-mentioned processing in the menu proposal unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the menu proposal unit can input data on the season and weather into the AI ​​and cause the AI ​​to execute the menu proposal.

[0050] The menu suggestion unit can make a suggestion taking into consideration the food allergy information of the family members when proposing a menu. For example, the menu suggestion unit makes a suggestion taking into consideration the food allergy information of the family members when proposing a menu. For example, the menu suggestion unit proposes a safe menu based on the allergy information of the family members. The menu suggestion unit can also propose a menu using alternative ingredients by taking into consideration the allergy information of the family members. Furthermore, the menu suggestion unit can also propose an optimal menu based on the allergy information of the family members. This makes it possible to propose a safe menu taking into consideration the allergy information of the family members. A part or all of the above-mentioned processing in the menu suggestion unit may be performed, for example, using AI or may be performed without using AI. For example, the menu suggestion unit can input the allergy information of the family members to the AI ​​and have the AI ​​execute the menu suggestion.

[0051] The menu suggestion unit can improve the accuracy of the suggestion by referring to the family's past meal history when proposing a menu. The menu suggestion unit can improve the accuracy of the suggestion by referring to the family's past meal history when proposing a menu. For example, the menu suggestion unit can suggest a favorite menu based on the family's past meal history. The menu suggestion unit can also suggest a menu with a wide variety of menus by referring to the family's past meal history. Furthermore, the menu suggestion unit can also suggest an optimal menu based on the family's past meal history. This makes it possible to suggest a highly accurate menu based on the past meal history. Some or all of the above-mentioned processing in the menu suggestion unit may be performed, for example, using AI or may be performed without using AI. For example, the menu suggestion unit can input the family's past meal history data into AI and have the AI ​​execute the menu suggestion.

[0052] The ordering unit can select the optimal ordering method in consideration of the family's budget and purchase history when placing an order. The ordering unit, for example, selects the optimal ordering method in consideration of the family's budget and purchase history when placing an order. For example, the ordering unit selects ingredients with high cost performance in consideration of the family's budget. The ordering unit can also prioritize ordering ingredients that are frequently purchased based on the family's purchase history. Furthermore, the ordering unit can also propose the optimal ordering method in consideration of the family's budget and purchase history. This makes it possible to place an optimal order in consideration of the budget and purchase history. 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 can input the family's budget data and purchase history data into AI and have the AI ​​select the ordering method.

[0053] The ordering unit can place an order taking into consideration the supply status and price fluctuations of ingredients when placing an order. The ordering unit, for example, places an order taking into consideration the supply status and price fluctuations of ingredients when placing an order. For example, the supply status of ingredients is checked in real time and an order is placed at the optimal timing. The ordering unit can also place an order at a reduced cost taking into consideration price fluctuations of ingredients. Furthermore, the ordering unit can also propose an optimal ordering method based on the supply status and price fluctuations of ingredients. This makes it possible to place an optimal order taking into consideration the supply status and price fluctuations. 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 can input the supply status data and price fluctuation data of ingredients into AI and have the AI ​​select the ordering method.

[0054] The ordering unit can place an order while taking into consideration the partnership status with local farmers and food manufacturers. For example, the ordering unit places an order while taking into consideration the partnership status with local farmers and food manufacturers. For example, the ordering unit partners with local farmers and gives priority to ordering fresh locally produced ingredients. The ordering unit can also place an order that revitalizes the local economy while taking into consideration the partnership status with local food manufacturers. Furthermore, the ordering unit can also propose an optimal ordering method based on the partnership status with local farmers and food manufacturers. This makes it possible to place an order while taking into consideration the partnership status with local farmers and food manufacturers. 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 can input partnership status data of local farmers and food manufacturers into AI and have the AI ​​select the ordering method.

[0055] The ordering unit can monitor the delivery status of ingredients in real time at the time of ordering and select the optimal delivery method. The ordering unit, for example, monitors the delivery status of ingredients in real time at the time of ordering and selects the optimal delivery method. For example, the delivery status of ingredients is monitored in real time and the optimal delivery method is selected. The ordering unit can also propose optimal delivery timing based on the delivery status of ingredients. Furthermore, the ordering unit can monitor the delivery status of ingredients in real time and propose measures to avoid delays. This makes it possible to monitor the delivery status in real time and select the optimal delivery method. 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 can input delivery status data of ingredients to AI and have AI select the delivery method.

[0056] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0057] The inventory analysis unit can perform inventory analysis taking into account the expiration dates of ingredients in the refrigerator. For example, the inventory analysis unit can prioritize the analysis of ingredients in the refrigerator that are close to their expiration date and encourage their use. The inventory analysis unit can also suggest the optimal order of use based on the expiration dates of ingredients in the refrigerator. Furthermore, the inventory analysis unit can also make suggestions to reduce waste by taking into account the expiration dates of ingredients in the refrigerator. This enables inventory analysis that takes expiration dates into account. Some or all of the above-mentioned processing in the inventory analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the inventory analysis unit can input expiration date data of ingredients in the refrigerator to AI and have AI perform inventory analysis.

[0058] The schedule understanding unit can analyze the past schedule history of the family and select an appropriate schedule acquisition method. For example, frequently occurring events are automatically incorporated into the schedule from the past schedule of the family. The schedule understanding unit can also propose the most efficient schedule pattern based on the past schedule history of the family. Furthermore, the schedule understanding unit can analyze the past schedule history of the family and adjust the schedule to avoid overlapping events. In this way, an optimal schedule can be acquired based on the past schedule history. A part or all of the above-mentioned processing in the schedule understanding unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the schedule understanding unit can input the past schedule history into an AI and have the AI ​​perform schedule analysis.

[0059] The inventory analysis unit can perform inventory analysis while taking into consideration the arrangement of ingredients in the refrigerator. For example, the inventory analysis unit can suggest an optimal order of use based on the arrangement of ingredients in the refrigerator. The inventory analysis unit can also perform efficient inventory management while taking into consideration the arrangement of ingredients in the refrigerator. Furthermore, the inventory analysis unit can make suggestions to reduce waste based on the arrangement of ingredients in the refrigerator. This enables inventory analysis that takes into consideration the arrangement of ingredients. Some or all of the above-mentioned processing in the inventory analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the inventory analysis unit can input data on the arrangement of ingredients in the refrigerator to AI and have the AI ​​perform inventory analysis.

[0060] The menu suggestion unit can suggest a menu according to the season and weather when suggesting a menu. For example, a menu using seasonal ingredients according to the season is suggested. The menu suggestion unit can also suggest hot dishes and cold dishes according to the weather. Furthermore, the menu suggestion unit can also suggest an optimal menu taking into consideration the season and weather. This makes it possible to suggest an optimal menu according to the season and weather. Some or all of the above-mentioned processing in the menu suggestion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the menu suggestion unit can input data on the season and weather into the AI ​​and have the AI ​​execute the menu suggestion.

[0061] The ordering unit can select the optimal ordering method in consideration of the family's budget and purchase history when placing an order. For example, the ordering unit selects ingredients with high cost performance in consideration of the family's budget. The ordering unit can also prioritize ordering of ingredients that are frequently purchased based on the family's purchase history. Furthermore, the ordering unit can also suggest the optimal ordering method in consideration of the family's budget and purchase history. This enables optimal ordering in consideration of the budget and purchase history. 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 can input the family's budget data and purchase history data into AI and have the AI ​​select the ordering method.

[0062] The ordering unit can place an order while taking into consideration the supply status and price fluctuations of ingredients. For example, the ordering unit can check the supply status of ingredients in real time and place an order at the optimal timing. The ordering unit can also place an order at a reduced cost while taking into consideration price fluctuations of ingredients. Furthermore, the ordering unit can also propose an optimal ordering method based on the supply status and price fluctuations of ingredients. This enables optimal ordering while taking into consideration the supply status and price fluctuations. 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 can input food supply status data and price fluctuation data into AI and have AI select the ordering method.

[0063] The process flow of the first embodiment will be briefly described below.

[0064] Step 1: The schedule understanding unit understands the family members' schedules and likes and dislikes. The family schedule includes work plans, school events, family events, etc. The schedule understanding unit obtains information from the family members' calendar and memo apps, which the AI ​​analyzes. For example, the AI ​​analyzes plans obtained from a calendar app to understand the family members' schedules. The AI ​​also analyzes the family members' food preferences recorded in the memo app to understand their likes and dislikes. Step 2: The inventory analysis unit checks the stock status in the refrigerator through photo analysis. The stock status in the refrigerator includes the types and quantities of vegetables, meat, dairy products, etc. The inventory analysis unit takes a photo of the inside of the refrigerator, and AI performs image analysis to identify the ingredients in stock. For example, AI can analyze the types and quantities of vegetables, meat, dairy products, etc. from a photo of the inside of the refrigerator to grasp the stock status. Step 3: The menu suggestion unit proposes a weekly menu, taking into consideration nutritional balance. The menu might include a vegetable-based menu on Monday, fish dishes on Tuesday, and meat dishes on Wednesday. The menu suggestion unit uses AI to generate balanced menus, taking into consideration the family's schedule, likes and dislikes, and ingredients in stock. For example, the AI ​​will make simple menus for busy days based on the family's schedule. The AI ​​will also propose menus that the whole family can enjoy based on the family's likes and dislikes. Furthermore, the AI ​​will suggest menus that use ingredients in the refrigerator without waste, based on ingredients in stock. Step 4: The ordering unit places an order with the online supermarket. The ordering includes listing the necessary ingredients and accessing the online supermarket's website to complete the order. The ordering unit has the AI ​​list the necessary ingredients based on the proposal from the menu proposal unit, and access the online supermarket's website to complete the order. For example, the AI ​​lists the ingredients needed for a menu, and accesses the online supermarket's website to complete the order.

[0065] (Example 2) The system according to the embodiment of the present invention is a system that grasps the schedule and likes and dislikes of the family, checks the stock of ingredients in the refrigerator, and proposes a menu for one week that takes into account nutritional balance. This system also handles the procurement of one week's worth of ingredients, which is difficult for dual-income families, and even orders them from online supermarkets, significantly reducing weekend housework. Since the stock status of the refrigerator can be constantly grasped through photo analysis, it is possible to propose a different menu even if there is a sudden shortage of ingredients. In addition, by working closely with the local community, it contributes to regional revitalization, reducing food waste, and SDGs. For example, in order to grasp the schedule and likes and dislikes of the family, the schedule grasper collects the family's plans and food preferences. For example, information is obtained from the family's calendar app and memo app, and AI analyzes it. Next, based on the information grasped by the schedule grasper, the inventory analysis unit checks the inventory status of the refrigerator through photo analysis. A photo of the inside of the refrigerator is taken, and AI performs image analysis to identify the stock of ingredients. For example, the types and amounts of vegetables, meat, dairy products, etc. are grasped from the photos of the inside of the refrigerator. Then, based on the analysis by the inventory analysis unit, the menu proposal unit considers nutritional balance and proposes a menu for one week. AI creates a balanced menu, taking into account the family's schedule, likes and dislikes, and available ingredients. For example, it proposes a rich variety of menus, such as a vegetable-centered menu on Monday, fish dishes on Tuesday, and meat dishes on Wednesday. In addition, the ordering department places an order with the online supermarket based on the proposals of the menu proposal department. The AI ​​lists the necessary ingredients, accesses the online supermarket's website, and completes the order. This saves dual-income families the trouble of procuring ingredients, and significantly reduces weekend housework. In addition, since the refrigerator's inventory status can be constantly monitored through photo analysis, it is also possible to respond to sudden shortages of ingredients. For example, even if an unexpected visitor arrives, the inventory analysis department checks the status of the refrigerator again and proposes a different menu. This system contributes to regional revitalization, food waste reduction, and SDGs by working closely with the local community. For example, by partnering with local farmers and food manufacturers and prioritizing the use of locally produced ingredients, it contributes to the revitalization of the local economy. In addition, the inventory analysis department proposes menus that use the ingredients in the refrigerator without waste, which also leads to a reduction in food waste.This system can understand the family's schedules and likes and dislikes, check the ingredients in the refrigerator, and suggest a week's worth of menus that take nutritional balance into consideration, significantly reducing the housework burden for dual-income families.

[0066] The menu proposal system according to the embodiment includes a schedule grasping unit, an inventory analysis unit, a menu proposal unit, and an ordering unit. The schedule grasping unit grasps the schedule and likes and dislikes of the family members. The family schedule includes, for example, work schedules, school events, family events, etc., but is not limited to such examples. The schedule grasping unit, for example, acquires information from a calendar app or a memo app of the family members, and the AI ​​analyzes it. For example, the AI ​​analyzes the schedule acquired from the calendar app to grasp the family schedule. In addition, the schedule grasping unit acquires information from a memo app of the family members to grasp the likes and dislikes of the family members, and the AI ​​analyzes it. For example, the AI ​​analyzes the food preferences of the family members recorded in the memo app to grasp the likes and dislikes. The inventory analysis unit checks the inventory status of the refrigerator by photo analysis. The inventory status in the refrigerator includes, for example, the types and amounts of vegetables, meat, dairy products, etc., but is not limited to such examples. The inventory analysis unit, for example, takes a photo of the inside of the refrigerator, and the AI ​​performs image analysis to identify the ingredients in stock. For example, the AI ​​analyzes the types and amounts of vegetables, meat, dairy products, etc. from photos of the inside of the refrigerator to grasp the inventory status. The menu suggestion unit proposes a menu for one week taking into consideration nutritional balance. The menu includes, for example, a vegetable-centered menu on Monday, fish dishes on Tuesday, and meat dishes on Wednesday, but is not limited to such examples. The menu suggestion unit generates a balanced menu by taking into consideration the family's schedule, likes and dislikes, and ingredients in stock. For example, the AI ​​makes the menu for busy days simple based on the family's schedule. In addition, the AI ​​proposes a menu that the whole family can enjoy based on the likes and dislikes of the family. Furthermore, the AI ​​proposes a menu that uses ingredients in the refrigerator without waste based on ingredients in stock. The ordering unit places an order with the online supermarket. The ordering includes, for example, listing the necessary ingredients and accessing the online supermarket's website to complete the order, but is not limited to such examples. In the ordering unit, the AI ​​lists the necessary ingredients based on the proposal of the menu suggestion unit, and accesses the online supermarket's website to complete the order. For example, AI can list the ingredients needed for a meal and access an online supermarket website to complete the order.As a result, the menu suggestion system according to the embodiment can grasp the schedule and likes and dislikes of the family, check the ingredients in the refrigerator, suggest a week's menu that takes into account nutritional balance, and even order the ingredients, significantly reducing the housework burden of dual-income families.

[0067] The schedule grasping unit grasps the schedule and likes and dislikes of the family members. The family schedule includes, for example, work schedules, school events, family events, etc., but is not limited to such examples. The schedule grasping unit, for example, obtains information from the family's calendar app or memo app, and the AI ​​analyzes it. Specifically, the AI ​​analyzes the schedule obtained from the calendar app and grasps the family's schedule. The AI ​​uses natural language processing technology to analyze the contents of the schedule written on the calendar and classify and organize the schedule of each member. For example, keywords such as "meeting" and "class" are extracted, and the importance and time period of each schedule are grasped. In addition, the schedule grasping unit obtains information from the family's memo app and the AI ​​analyzes it in order to grasp the likes and dislikes of the family members. For example, the AI ​​analyzes the food preferences of the family members recorded in the memo app and grasps the likes and dislikes. The AI ​​analyzes the text data written in the memo app and extracts information such as "disliked ingredients" and "favorite dishes". In addition, the AI ​​learns the food preferences of each member based on the past meal history and feedback from the family, and performs more accurate analysis. This enables the schedule understanding unit to accurately understand the family's schedules and likes and dislikes, and provide basic data for menu suggestions.

[0068] The inventory analysis unit checks the inventory status of the refrigerator by photo analysis. The inventory status in the refrigerator includes, for example, the types and amounts of vegetables, meat, dairy products, etc., but is not limited to such examples. For example, the inventory analysis unit takes a photo of the inside of the refrigerator, and the AI ​​performs image analysis to identify the ingredients in stock. Specifically, the AI ​​analyzes the types and amounts of vegetables, meat, dairy products, etc. from the photo of the inside of the refrigerator to grasp the inventory status. The AI ​​uses image recognition technology to automatically identify the ingredients in the refrigerator and identify the type and amount of each ingredient. For example, the AI ​​analyzes the shape and color of vegetables and the labels on the packages to identify ingredients such as cabbage, tomatoes, and milk. In addition, the AI ​​predicts the expiration date and frequency of use of ingredients based on past inventory data and consumption patterns, and improves the efficiency of inventory management. Furthermore, the inventory analysis unit can collect environmental data such as the temperature and humidity in the refrigerator and monitor the storage conditions of ingredients. As a result, the inventory analysis unit can accurately grasp the inventory status in the refrigerator, reduce food waste, and achieve efficient inventory management.

[0069] The menu suggestion unit proposes a menu for one week, taking into consideration nutritional balance. For example, the menu may include, but is not limited to, a vegetable-centered menu on Monday, fish dishes on Tuesday, and meat dishes on Wednesday. The menu suggestion unit generates a balanced menu by using the AI ​​to consider the family's schedule, likes and dislikes, and ingredients in stock. Specifically, the AI ​​makes the menu for busy days simpler based on the family's schedule. The AI ​​also proposes a menu that the whole family can enjoy based on the family's likes and dislikes. Furthermore, the AI ​​proposes a menu that uses the ingredients in the refrigerator without waste based on the ingredients in stock. The AI ​​calculates the nutritional value of each ingredient based on knowledge of nutritional science and proposes a balanced meal. For example, the AI ​​plans a menu for one week, taking into consideration nutrients such as vitamins, minerals, and protein. The AI ​​can also propose a menu that utilizes seasonal ingredients, taking into consideration the characteristics of the season and region. Furthermore, the menu suggestion unit collects feedback from the family and continuously improves the accuracy of the suggestions. This allows the menu suggestion unit to support the health of the family and provide the enjoyment of meals.

[0070] The ordering unit places an order with the online supermarket. For example, the ordering includes, but is not limited to, listing the necessary ingredients and accessing the online supermarket's website to complete the order. The ordering unit lists the necessary ingredients based on the proposal of the menu suggestion unit by the AI, and accesses the online supermarket's website to complete the order. Specifically, the AI ​​lists the ingredients necessary for the menu, and accesses the online supermarket's website to complete the order. The AI ​​automatically generates a list of necessary ingredients based on the information provided by the menu suggestion unit, and specifies the quantity and brand of each ingredient. The AI ​​also obtains inventory status and price information of the online supermarket in real time and selects the optimal purchase destination. Furthermore, the AI ​​takes into account past purchase history and user preferences, and utilizes repeat purchase and special sale information to efficiently place an order. The ordering unit checks the order details and accesses the online supermarket's website to complete the order. As a result, the ordering unit can quickly and accurately order the necessary ingredients, reducing the burden of housework. Furthermore, the ordering unit can monitor the progress of the order and delivery schedule, and notify the user. This enables the ordering department to centrally manage the process from ordering ingredients to delivery, improving user convenience.

[0071] The schedule understanding unit can obtain information from a family member's calendar app or memo app, and the AI ​​can analyze the information. The schedule understanding unit, for example, obtains information from a family member's calendar app, and the AI ​​can analyze the information. For example, the AI ​​analyzes the plans obtained from the calendar app and understands the family member's schedule. The schedule understanding unit can also obtain information from a family member's memo app, and the AI ​​can analyze the information. For example, the AI ​​analyzes the family member's food preferences recorded in the memo app and understands their likes and dislikes. This makes it possible to accurately understand the family member's schedule and likes and dislikes. Some or all of the above-mentioned processing in the schedule understanding unit may be performed, for example, using AI, or may be performed without using AI. For example, the schedule understanding unit can input information obtained from the calendar app to AI, and have the AI ​​execute analysis of the schedule.

[0072] The inventory analysis unit can take a photo of the inside of the refrigerator, and the AI ​​can perform image analysis to identify the ingredients in stock. The inventory analysis unit can, for example, take a photo of the inside of the refrigerator, and the AI ​​can perform image analysis to identify the ingredients in stock. For example, the AI ​​can analyze the types and amounts of vegetables, meat, dairy products, etc. from the photo of the inside of the refrigerator to grasp the inventory status. The inventory analysis unit can also take a photo of the inside of the refrigerator periodically to keep the inventory status always up to date. For example, the inventory analysis unit can take a photo every time the refrigerator door is opened and closed, and the AI ​​can analyze the photo to grasp the inventory status in real time. This makes it possible to accurately grasp the ingredients in stock in the refrigerator. Some or all of the above-mentioned processing in the inventory analysis unit can be performed, for example, using AI, or can be performed without using AI. For example, the inventory analysis unit can input a photo of the inside of the refrigerator to AI and have AI perform image analysis.

[0073] The menu suggestion unit can propose a menu for one week based on a criterion for nutritional balance. The menu suggestion unit proposes a menu for one week based on a criterion for nutritional balance, for example. For example, a wide variety of menus are proposed, such as a vegetable-centered menu on Monday, fish dishes on Tuesday, and meat dishes on Wednesday. The menu suggestion unit also generates a balanced menu taking into consideration the family's schedule, likes and dislikes, and ingredients in stock. For example, the AI ​​makes a menu for busy days simple based on the family's schedule. The AI ​​also proposes a menu that the whole family can enjoy based on the family's likes and dislikes. Furthermore, the AI ​​proposes a menu that uses ingredients in the refrigerator without waste based on ingredients in stock. This makes it possible to propose a nutritionally balanced menu. Some or all of the above-mentioned processing in the menu suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the menu suggestion unit can input information on the family's schedule, likes and dislikes, and ingredients in stock into the AI, and have the AI ​​generate the menu.

[0074] The ordering unit can place an order to an online supermarket. The ordering unit, for example, places an order to an online supermarket. For example, the ordering unit lists the necessary ingredients and accesses the online supermarket's website to complete the order. The ordering unit can also list the necessary ingredients based on a proposal by the menu suggestion unit using AI, and accesses the online supermarket's website to complete the order. For example, the AI ​​lists the ingredients necessary for a menu, and accesses the online supermarket's website to complete the order. This automates the ordering of ingredients, thereby reducing the burden of housework. Some or all of the above-mentioned processing in the ordering unit may be performed using AI, or may be performed without using AI. For example, the ordering unit can input a list of necessary ingredients into AI and have the AI ​​execute the ordering procedure.

[0075] The inventory analysis unit can check the situation in the refrigerator again and propose a different menu to deal with a sudden shortage of ingredients. The inventory analysis unit can check the situation in the refrigerator again and propose a different menu to deal with a sudden shortage of ingredients, for example. Even if an unexpected visitor comes, the inventory analysis unit can check the situation in the refrigerator again and propose a different menu. The inventory analysis unit can also periodically check the inventory status in the refrigerator to prepare for a sudden shortage of ingredients. For example, the inventory analysis unit can grasp the inventory status in real time by taking a photo every time the refrigerator door is opened and closed and analyzing it with AI. This makes it possible to flexibly deal with a sudden shortage of ingredients. Some or all of the above-mentioned processing in the inventory analysis unit can be performed, for example, using AI or can be performed without using AI. For example, the inventory analysis unit can input a photo of the inside of the refrigerator to AI and have AI execute an analysis of the inventory status.

[0076] The menu proposal unit can cooperate with local farmers and food manufacturers to propose a menu that prioritizes the use of locally produced ingredients. The menu proposal unit, for example, cooperates with local farmers and food manufacturers to propose a menu that prioritizes the use of locally produced ingredients. For example, the menu proposal unit cooperates with local farmers to propose a menu that uses fresh locally produced vegetables and fruits. The menu proposal unit can also cooperate with local food manufacturers to propose a menu that uses locally produced dairy products and meat. For example, the menu proposal unit cooperates with local dairy manufacturers to propose a menu that uses fresh milk and cheese. This can contribute to revitalizing the local economy and reducing food waste. Some or all of the above-mentioned processing in the menu proposal unit may be performed, for example, using AI, or may be performed without using AI. For example, the menu proposal unit can input information about local farmers and food manufacturers into AI and have the AI ​​execute the menu proposal.

[0077] The schedule comprehension unit can estimate the emotions of the family members and adjust the priority of the schedule based on the estimated emotions. The schedule comprehension unit, for example, estimates the emotions of the family members and adjusts the priority of the schedule based on the estimated emotions. For example, if a family member is feeling stressed, a time for relaxation is preferentially incorporated into the schedule. In addition, if a family member is having fun, the schedule can be adjusted to increase activity. Furthermore, if a family member is tired, the schedule can be adjusted to ensure more rest time. This makes it possible to adjust the schedule according to the emotions of the family members. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the schedule comprehension unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the schedule comprehension unit can input emotion data of the family members to an AI and cause the AI ​​to execute emotion estimation.

[0078] The schedule comprehension unit can analyze the past schedule history of the family and select an appropriate schedule acquisition method. The schedule comprehension unit, for example, analyzes the past schedule history of the family and selects an appropriate schedule acquisition method. For example, frequently occurring events are automatically incorporated into the schedule from the past schedule of the family. The schedule comprehension unit can also propose the most efficient schedule pattern based on the past schedule history of the family. Furthermore, the schedule comprehension unit can analyze the past schedule history of the family and adjust the schedule to avoid overlapping events. This makes it possible to obtain an optimal schedule based on the past schedule history. A part or all of the above-mentioned processing in the schedule comprehension unit may be performed, for example, using AI, or may be performed without using AI. For example, the schedule comprehension unit can input the past schedule history into AI and cause AI to perform schedule analysis.

[0079] The schedule understanding unit can perform filtering based on the health condition and life rhythm of the family when acquiring the schedule. For example, the schedule understanding unit performs filtering based on the health condition and life rhythm of the family when acquiring the schedule. For example, the schedule understanding unit proposes a reasonable schedule taking into account the health condition of the family. The schedule understanding unit can also adjust the schedule to avoid early morning and late night events in accordance with the family's life rhythm. Furthermore, the schedule understanding unit can also incorporate appropriate exercise and rest times into the schedule based on the health data of the family. This makes it possible to propose a schedule according to the health condition and life rhythm of the family. A part or all of the above-mentioned processing in the schedule understanding unit may be performed using, for example, AI, or may be performed without using AI. For example, the schedule understanding unit can input the health data of the family into AI and cause AI to execute filtering of the schedule.

[0080] The schedule comprehension unit can estimate the emotions of the family members and adjust the display method of the schedule based on the estimated emotions. The schedule comprehension unit, for example, estimates the emotions of the family members and adjusts the display method of the schedule based on the estimated emotions. For example, when a family member is stressed, a simple and highly visible display method is provided. Also, when a family member is having fun, a colorful and fun display method can be provided. Furthermore, when a family member is tired, a display method with calm colors can be provided. This enables the schedule display according to the emotions of the family members. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the schedule comprehension unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the schedule comprehension unit can input emotion data of the family members to an AI and cause the AI ​​to execute emotion estimation.

[0081] The schedule understanding unit can preferentially acquire a highly relevant schedule in consideration of the geographical location information of the family when acquiring a schedule. For example, the schedule understanding unit preferentially acquires a highly relevant schedule in consideration of the geographical location information of the family when acquiring a schedule. For example, an event close to the current location of the family members is preferentially incorporated into the schedule. In addition, the schedule understanding unit can also preferentially acquire an event related to a place frequently visited by the family members. Furthermore, the schedule understanding unit can also preferentially acquire an event related to a place where the family members can easily move. In this way, a highly relevant schedule based on the geographical location information can be acquired. A part or all of the above-mentioned processing in the schedule understanding unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the schedule understanding unit can input the geographical location information of the family members to the AI ​​and cause the AI ​​to acquire the schedule.

[0082] The schedule grasping unit can analyze the social media activities of the family members when acquiring the schedule and acquire the related schedule. The schedule grasping unit can, for example, analyze the social media activities of the family members when acquiring the schedule and acquire the related schedule. For example, events in which the family members have shown interest on social media are incorporated into the schedule. The schedule grasping unit can also preferentially acquire events of accounts that the family members follow on social media. Furthermore, the schedule grasping unit can also incorporate events that the family members plan to participate in on social media into the schedule. This makes it possible to acquire a highly relevant schedule based on the social media activities. A part or all of the above-mentioned processing in the schedule grasping unit can be performed, for example, using AI or can be performed without using AI. For example, the schedule grasping unit can input the social media activity data of the family members into AI and cause AI to acquire the schedule.

[0083] The inventory analysis unit can estimate the emotions of the family members and adjust the frequency of inventory analysis based on the estimated emotions. The inventory analysis unit, for example, estimates the emotions of the family members and adjusts the frequency of inventory analysis based on the estimated emotions. For example, if a family member is stressed, the frequency of inventory analysis is reduced to reduce the burden. Also, if a family member is having fun, the frequency of inventory analysis can be increased and suggestions can be made proactively. Furthermore, if a family member is tired, the frequency of inventory analysis can be adjusted and suggestions can be made within a reasonable range. This makes it possible to adjust the frequency of inventory analysis according to the emotions of the family members. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the inventory analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the inventory analysis unit can input emotion data of the family members to an AI and have the AI ​​execute emotion estimation.

[0084] The inventory analysis unit can perform inventory analysis taking into account the expiration date of the ingredients in the refrigerator. The inventory analysis unit performs inventory analysis taking into account the expiration date of the ingredients in the refrigerator, for example. For example, the inventory analysis unit prioritizes analysis of ingredients in the refrigerator that are close to their expiration date and encourages their use. The inventory analysis unit can also suggest an optimal order of use based on the expiration date of the ingredients in the refrigerator. Furthermore, the inventory analysis unit can also make suggestions to reduce waste by taking into account the expiration date of the ingredients in the refrigerator. This makes it possible to perform inventory analysis taking into account the expiration date. Some or all of the above-mentioned processing in the inventory analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the inventory analysis unit can input expiration date data of ingredients in the refrigerator to AI and have AI perform inventory analysis.

[0085] The inventory analysis unit can evaluate the nutritional value and storage state of ingredients during inventory analysis. The inventory analysis unit, for example, evaluates the nutritional value and storage state of ingredients during inventory analysis. For example, the inventory analysis unit evaluates the nutritional value of ingredients in the refrigerator and proposes a balanced menu. The inventory analysis unit can also evaluate the storage state of ingredients in the refrigerator and identify usable ingredients. Furthermore, the inventory analysis unit can also propose an optimal use method based on the nutritional value and storage state of ingredients in the refrigerator. This makes it possible to perform inventory analysis taking into account the nutritional value and storage state of ingredients. Some or all of the above-mentioned processing in the inventory analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the inventory analysis unit can input nutritional value data and storage state data of ingredients in the refrigerator to AI and have AI perform inventory analysis.

[0086] The inventory analysis unit can estimate the emotions of the family members and adjust the order in which the results of the inventory analysis are displayed based on the estimated emotions. The inventory analysis unit, for example, estimates the emotions of the family members and adjusts the order in which the results of the inventory analysis are displayed based on the estimated emotions. For example, when a family member is feeling stressed, important information is preferentially displayed. Also, when a family member is having fun, a display method including detailed information can be provided. Furthermore, when a family member is tired, a display method that is simple and highly visible can be provided. This makes it possible to display the results of the inventory analysis according to the emotions of the family members. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the inventory analysis unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the inventory analysis unit can input emotion data of the family members to an AI and cause the AI ​​to execute emotion estimation.

[0087] The inventory analysis unit can perform the analysis while taking into consideration the arrangement of ingredients in the refrigerator during the inventory analysis. The inventory analysis unit, for example, performs the analysis while taking into consideration the arrangement of ingredients in the refrigerator during the inventory analysis. For example, the inventory analysis unit proposes an optimal order of use based on the arrangement of ingredients in the refrigerator. The inventory analysis unit can also perform efficient inventory management while taking into consideration the arrangement of ingredients in the refrigerator. Furthermore, the inventory analysis unit can also make suggestions to reduce waste based on the arrangement of ingredients in the refrigerator. This enables inventory analysis while taking into consideration the arrangement of ingredients. Some or all of the above-mentioned processing in the inventory analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the inventory analysis unit can input the arrangement data of ingredients in the refrigerator to AI and have the AI ​​perform the inventory analysis.

[0088] The inventory analysis unit can improve the accuracy of the analysis by referring to the purchase history of ingredients during inventory analysis. The inventory analysis unit can improve the accuracy of the analysis by referring to the purchase history of ingredients during inventory analysis, for example. For example, the accuracy of the inventory analysis is improved based on the purchase history of ingredients. The inventory analysis unit can also suggest an optimal use order by referring to the purchase history of ingredients. Furthermore, the inventory analysis unit can also make suggestions to reduce waste based on the purchase history of ingredients. This enables highly accurate inventory analysis based on the purchase history. Some or all of the above-mentioned processing in the inventory analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the inventory analysis unit can input the purchase history data of ingredients to AI and have the AI ​​perform the inventory analysis.

[0089] The menu suggestion unit can estimate the emotions of the family members and adjust the menu suggestion method based on the estimated emotions. The menu suggestion unit, for example, estimates the emotions of the family members and adjusts the menu suggestion method based on the estimated emotions. For example, if a family member is stressed, a menu that is simple and easy to make is suggested. Also, if a family member is having fun, a menu with a wide variety can be suggested. Furthermore, if a family member is tired, a nutritious menu can be suggested. This makes it possible to suggest a menu according to the emotions of the family members. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the menu suggestion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the menu suggestion unit can input emotion data of the family members to an AI and have the AI ​​execute emotion estimation.

[0090] The menu suggestion unit can make a suggestion taking into consideration the health condition and nutritional balance of the family when proposing a menu. The menu suggestion unit, for example, makes a suggestion taking into consideration the health condition and nutritional balance of the family when proposing a menu. For example, the menu suggestion unit proposes a balanced menu taking into consideration the health condition of the family. The menu suggestion unit can also propose a menu containing necessary nutrients taking into consideration the nutritional balance of the family. Furthermore, the menu suggestion unit can also propose an optimal menu based on the health data of the family. This makes it possible to propose a menu that takes into consideration the health condition and nutritional balance of the family. A part or all of the above-mentioned processing in the menu suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the menu suggestion unit can input the health data of the family into AI and have the AI ​​execute the menu suggestion.

[0091] The menu proposal unit can propose a menu according to the season and weather when proposing a menu. The menu proposal unit, for example, proposes a menu according to the season and weather when proposing a menu. For example, a menu using seasonal ingredients according to the season is proposed. The menu proposal unit can also propose hot dishes and cold dishes according to the weather. Furthermore, the menu proposal unit can also propose an optimal menu taking into account the season and weather. This makes it possible to propose an optimal menu according to the season and weather. Some or all of the above-mentioned processing in the menu proposal unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the menu proposal unit can input data on the season and weather into the AI ​​and cause the AI ​​to execute the menu proposal.

[0092] The menu suggestion unit can estimate the emotions of the family members and determine the priority order of the menu based on the estimated emotions. The menu suggestion unit, for example, estimates the emotions of the family members and determines the priority order of the menu based on the estimated emotions. For example, if a family member is feeling stressed, a menu that allows the family member to relax is preferentially suggested. In addition, if a family member is enjoying themselves, a menu with a wide variety can be preferentially suggested. Furthermore, if a family member is tired, a menu with high nutritional value can be preferentially suggested. This makes it possible to determine the priority order of the menu according to the emotions of the family members. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the menu suggestion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the menu suggestion unit can input emotion data of the family members to an AI and cause the AI ​​to execute emotion estimation.

[0093] The menu suggestion unit can make a suggestion taking into consideration the food allergy information of the family members when proposing a menu. For example, the menu suggestion unit makes a suggestion taking into consideration the food allergy information of the family members when proposing a menu. For example, the menu suggestion unit proposes a safe menu based on the allergy information of the family members. The menu suggestion unit can also propose a menu using alternative ingredients by taking into consideration the allergy information of the family members. Furthermore, the menu suggestion unit can also propose an optimal menu based on the allergy information of the family members. This makes it possible to propose a safe menu taking into consideration the allergy information of the family members. A part or all of the above-mentioned processing in the menu suggestion unit may be performed, for example, using AI or may be performed without using AI. For example, the menu suggestion unit can input the allergy information of the family members to the AI ​​and have the AI ​​execute the menu suggestion.

[0094] The menu suggestion unit can improve the accuracy of the suggestion by referring to the family's past meal history when proposing a menu. The menu suggestion unit can improve the accuracy of the suggestion by referring to the family's past meal history when proposing a menu. For example, the menu suggestion unit can suggest a favorite menu based on the family's past meal history. The menu suggestion unit can also suggest a menu with a wide variety of menus by referring to the family's past meal history. Furthermore, the menu suggestion unit can also suggest an optimal menu based on the family's past meal history. This makes it possible to suggest a highly accurate menu based on the past meal history. Some or all of the above-mentioned processing in the menu suggestion unit may be performed, for example, using AI or may be performed without using AI. For example, the menu suggestion unit can input the family's past meal history data into AI and have the AI ​​execute the menu suggestion.

[0095] The ordering unit can estimate the emotions of the family members and adjust the timing of ordering based on the estimated emotions. The ordering unit, for example, estimates the emotions of the family members and adjusts the timing of ordering based on the estimated emotions. For example, if a family member is stressed, the timing of ordering is delayed. Also, if a family member is having fun, the timing of ordering can be advanced. Furthermore, if a family member is tired, the timing of ordering can be adjusted. This makes it possible to adjust the timing of ordering according to the emotions of the family members. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the ordering unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the ordering unit can input emotion data of the family members to an AI and cause the AI ​​to execute emotion estimation.

[0096] The ordering unit can select the optimal ordering method in consideration of the family's budget and purchase history when placing an order. The ordering unit, for example, selects the optimal ordering method in consideration of the family's budget and purchase history when placing an order. For example, the ordering unit selects ingredients with high cost performance in consideration of the family's budget. The ordering unit can also prioritize ordering ingredients that are frequently purchased based on the family's purchase history. Furthermore, the ordering unit can also propose the optimal ordering method in consideration of the family's budget and purchase history. This makes it possible to place an optimal order in consideration of the budget and purchase history. 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 can input the family's budget data and purchase history data into AI and have the AI ​​select the ordering method.

[0097] The ordering unit can place an order taking into consideration the supply status and price fluctuations of ingredients when placing an order. The ordering unit, for example, places an order taking into consideration the supply status and price fluctuations of ingredients when placing an order. For example, the supply status of ingredients is checked in real time and an order is placed at the optimal timing. The ordering unit can also place an order at a reduced cost taking into consideration price fluctuations of ingredients. Furthermore, the ordering unit can also propose an optimal ordering method based on the supply status and price fluctuations of ingredients. This makes it possible to place an optimal order taking into consideration the supply status and price fluctuations. 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 can input the supply status data and price fluctuation data of ingredients into AI and have the AI ​​select the ordering method.

[0098] The ordering unit can estimate the emotions of the family members and determine the priority of orders based on the estimated emotions. The ordering unit, for example, estimates the emotions of the family members and determines the priority of orders based on the estimated emotions. For example, if a family member is feeling stressed, the minimum necessary ingredients are preferentially ordered. Also, if a family member is having fun, ingredients with a wide variety can be preferentially ordered. Furthermore, if a family member is tired, ingredients with high nutritional value can be preferentially ordered. This makes it possible to determine the priority of orders according to the emotions of the family members. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the ordering unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the ordering unit can input emotion data of the family members to an AI and cause the AI ​​to execute emotion estimation.

[0099] The ordering unit can place an order while taking into consideration the partnership status with local farmers and food manufacturers. For example, the ordering unit places an order while taking into consideration the partnership status with local farmers and food manufacturers. For example, the ordering unit partners with local farmers and gives priority to ordering fresh locally produced ingredients. The ordering unit can also place an order that revitalizes the local economy while taking into consideration the partnership status with local food manufacturers. Furthermore, the ordering unit can also propose an optimal ordering method based on the partnership status with local farmers and food manufacturers. This makes it possible to place an order while taking into consideration the partnership status with local farmers and food manufacturers. 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 can input partnership status data of local farmers and food manufacturers into AI and have the AI ​​select the ordering method.

[0100] The ordering unit can monitor the delivery status of ingredients in real time at the time of ordering and select the optimal delivery method. The ordering unit, for example, monitors the delivery status of ingredients in real time at the time of ordering and selects the optimal delivery method. For example, the delivery status of ingredients is monitored in real time and the optimal delivery method is selected. The ordering unit can also propose optimal delivery timing based on the delivery status of ingredients. Furthermore, the ordering unit can monitor the delivery status of ingredients in real time and propose measures to avoid delays. This makes it possible to monitor the delivery status in real time and select the optimal delivery method. 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 can input delivery status data of ingredients to AI and have AI select the delivery method.

[0101] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0102] The schedule comprehension unit can estimate the emotions of the family members and adjust the priority of the schedule based on the estimated emotions. For example, if a family member is feeling stressed, the schedule can be prioritized to include relaxation time. In addition, if a family member is having fun, the schedule can be adjusted to increase activity. Furthermore, if a family member is tired, the schedule can be adjusted to ensure more rest time. This makes it possible to adjust the schedule according to the emotions of the family members. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the schedule comprehension unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the schedule comprehension unit can input emotion data of the family members to an AI and cause the AI ​​to execute emotion estimation.

[0103] The inventory analysis unit can perform inventory analysis taking into account the expiration dates of ingredients in the refrigerator. For example, the inventory analysis unit can prioritize the analysis of ingredients in the refrigerator that are close to their expiration date and encourage their use. The inventory analysis unit can also suggest the optimal order of use based on the expiration dates of ingredients in the refrigerator. Furthermore, the inventory analysis unit can also make suggestions to reduce waste by taking into account the expiration dates of ingredients in the refrigerator. This enables inventory analysis that takes expiration dates into account. Some or all of the above-mentioned processing in the inventory analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the inventory analysis unit can input expiration date data of ingredients in the refrigerator to AI and have AI perform inventory analysis.

[0104] The menu suggestion unit can estimate the emotions of family members and adjust the menu suggestion method based on the estimated emotions. For example, if a family member is stressed, a simple and easy-to-make menu is suggested. Also, if a family member is having fun, a menu with a wide variety can be suggested. Furthermore, if a family member is tired, a nutritious menu can be suggested. This makes it possible to suggest a menu according to the emotions of the family. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the menu suggestion unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the menu suggestion unit can input emotion data of the family members to an AI and have the AI ​​execute emotion estimation.

[0105] The ordering unit can estimate the emotions of the family members and adjust the timing of the order based on the estimated emotions. For example, if a family member is stressed, the timing of the order is delayed. Also, if a family member is having fun, the timing of the order can be advanced. Furthermore, if a family member is tired, the timing of the order can be adjusted. This makes it possible to adjust the timing of the order according to the emotions of the family members. The estimation of the emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the ordering unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the ordering unit can input the emotion data of the family members to an AI and cause the AI ​​to execute the emotion estimation.

[0106] The inventory analysis unit can estimate the emotions of the family members and adjust the order of displaying the inventory analysis results based on the estimated emotions. For example, if a family member is feeling stressed, important information is preferentially displayed. Also, if a family member is having fun, a display method including detailed information can be provided. Furthermore, if a family member is tired, a simple and highly visible display method can be provided. This makes it possible to display the inventory analysis results according to the emotions of the family members. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the inventory analysis unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the inventory analysis unit can input the emotion data of the family members to an AI and cause the AI ​​to execute the emotion estimation.

[0107] The schedule understanding unit can analyze the past schedule history of the family and select an appropriate schedule acquisition method. For example, frequently occurring events are automatically incorporated into the schedule from the past schedule of the family. The schedule understanding unit can also propose the most efficient schedule pattern based on the past schedule history of the family. Furthermore, the schedule understanding unit can analyze the past schedule history of the family and adjust the schedule to avoid overlapping events. In this way, an optimal schedule can be acquired based on the past schedule history. A part or all of the above-mentioned processing in the schedule understanding unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the schedule understanding unit can input the past schedule history into an AI and have the AI ​​perform schedule analysis.

[0108] The inventory analysis unit can perform inventory analysis while taking into consideration the arrangement of ingredients in the refrigerator. For example, the inventory analysis unit can suggest an optimal order of use based on the arrangement of ingredients in the refrigerator. The inventory analysis unit can also perform efficient inventory management while taking into consideration the arrangement of ingredients in the refrigerator. Furthermore, the inventory analysis unit can make suggestions to reduce waste based on the arrangement of ingredients in the refrigerator. This enables inventory analysis that takes into consideration the arrangement of ingredients. Some or all of the above-mentioned processing in the inventory analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the inventory analysis unit can input data on the arrangement of ingredients in the refrigerator to AI and have the AI ​​perform inventory analysis.

[0109] The menu suggestion unit can suggest a menu according to the season and weather when suggesting a menu. For example, a menu using seasonal ingredients according to the season is suggested. The menu suggestion unit can also suggest hot dishes and cold dishes according to the weather. Furthermore, the menu suggestion unit can also suggest an optimal menu taking into consideration the season and weather. This makes it possible to suggest an optimal menu according to the season and weather. Some or all of the above-mentioned processing in the menu suggestion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the menu suggestion unit can input data on the season and weather into the AI ​​and have the AI ​​execute the menu suggestion.

[0110] The ordering unit can select the optimal ordering method in consideration of the family's budget and purchase history when placing an order. For example, the ordering unit selects ingredients with high cost performance in consideration of the family's budget. The ordering unit can also prioritize ordering of ingredients that are frequently purchased based on the family's purchase history. Furthermore, the ordering unit can also suggest the optimal ordering method in consideration of the family's budget and purchase history. This enables optimal ordering in consideration of the budget and purchase history. 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 can input the family's budget data and purchase history data into AI and have the AI ​​select the ordering method.

[0111] The ordering unit can place an order while taking into consideration the supply status and price fluctuations of ingredients. For example, the ordering unit can check the supply status of ingredients in real time and place an order at the optimal timing. The ordering unit can also place an order at a reduced cost while taking into consideration price fluctuations of ingredients. Furthermore, the ordering unit can also propose an optimal ordering method based on the supply status and price fluctuations of ingredients. This enables optimal ordering while taking into consideration the supply status and price fluctuations. 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 can input food supply status data and price fluctuation data into AI and have AI select the ordering method.

[0112] The process flow of the second embodiment will be briefly described below.

[0113] Step 1: The schedule understanding unit understands the family members' schedules and likes and dislikes. The family schedule includes work plans, school events, family events, etc. The schedule understanding unit obtains information from the family members' calendar and memo apps, which the AI ​​analyzes. For example, the AI ​​analyzes plans obtained from a calendar app to understand the family members' schedules. The AI ​​also analyzes the family members' food preferences recorded in the memo app to understand their likes and dislikes. Step 2: The inventory analysis unit checks the stock status in the refrigerator through photo analysis. The stock status in the refrigerator includes the types and quantities of vegetables, meat, dairy products, etc. The inventory analysis unit takes a photo of the inside of the refrigerator, and AI performs image analysis to identify the ingredients in stock. For example, AI can analyze the types and quantities of vegetables, meat, dairy products, etc. from a photo of the inside of the refrigerator to grasp the stock status. Step 3: The menu suggestion unit proposes a weekly menu, taking into consideration nutritional balance. The menu might include a vegetable-based menu on Monday, fish dishes on Tuesday, and meat dishes on Wednesday. The menu suggestion unit uses AI to generate balanced menus, taking into consideration the family's schedule, likes and dislikes, and ingredients in stock. For example, the AI ​​will make simple menus for busy days based on the family's schedule. The AI ​​will also propose menus that the whole family can enjoy based on the family's likes and dislikes. Furthermore, the AI ​​will suggest menus that use ingredients in the refrigerator without waste, based on ingredients in stock. Step 4: The ordering unit places an order with the online supermarket. The ordering includes listing the necessary ingredients and accessing the online supermarket's website to complete the order. The ordering unit has the AI ​​list the necessary ingredients based on the proposal from the menu proposal unit, and access the online supermarket's website to complete the order. For example, the AI ​​lists the ingredients needed for a menu, and accesses the online supermarket's website to complete the order.

[0114] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice 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 voice data.

[0115] 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<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0116] Furthermore, the above-described processing by the data processing system 10 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 required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0117] For example, the schedule comprehension unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. For example, information is obtained from a family calendar app or a memo app, and the AI ​​analyzes it. For example, the inventory analysis unit takes a photo of the inside of the refrigerator using the camera 42 of the smart device 14, and performs image analysis by the specific processing unit 290 of the data processing device 12 to identify ingredients in stock. For example, the menu suggestion unit is realized by the specific processing unit 290 of the data processing device 12, and the AI ​​generates a balanced menu taking into account the family's schedule, likes and dislikes, and ingredients in stock. For example, the ordering unit is realized by the specific processing unit 290 of the data processing device 12, and the AI ​​lists the necessary ingredients and accesses the online supermarket site to complete the order. The correspondence between each unit and the device or control unit is not limited to the above example, and various changes are possible.

[0118] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0119] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0120] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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.

[0121] 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.

[0122] 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 the voice according to instructions from the processor 46.

[0123] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0124] 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 performed in a secure state.

[0125] Fig. 4 shows an example of 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.

[0126] 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.

[0127] 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0128] In the smart glasses 214, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 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 also perform processing similar to that of the specific processing unit 290 using these models.

[0129] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0130] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the 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.

[0131] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0132] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0133] For example, the schedule comprehension unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. For example, information is obtained from a family calendar app or memo app, and the AI ​​analyzes it. For example, the inventory analysis unit takes a photo of the inside of the refrigerator using the camera 42 of the smart glasses 214, and performs image analysis by the specific processing unit 290 of the data processing device 12 to identify ingredients in stock. For example, the menu suggestion unit is realized by the specific processing unit 290 of the data processing device 12, and the AI ​​generates a balanced menu taking into account the family's schedule, likes and dislikes, and ingredients in stock. For example, the ordering unit is realized by the specific processing unit 290 of the data processing device 12, and the AI ​​lists the necessary ingredients and accesses the online supermarket site to complete the order. The correspondence between each unit and the device or control unit is not limited to the above example, and various changes are possible.

[0134] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0135] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0136] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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.

[0137] 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.

[0138] 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 the voice according to instructions from the processor 46.

[0139] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0140] 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 performed in a secure state.

[0141] 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.

[0142] 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.

[0143] 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0144] In the headset type terminal 314, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50, and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific 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.

[0145] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0146] 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 voice indicating a user input for 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.

[0147] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0148] 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 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. In addition, 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, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0149] For example, the schedule comprehension unit is realized by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing device 12. For example, information is obtained from a family calendar app or memo app, and the AI ​​analyzes it. For example, the inventory analysis unit takes a photo of the inside of the refrigerator using the camera 42 of the headset terminal 314, and performs image analysis by the specific processing unit 290 of the data processing device 12 to identify ingredients in stock. For example, the menu suggestion unit is realized by the specific processing unit 290 of the data processing device 12, and the AI ​​generates a balanced menu taking into account the family's schedule, likes and dislikes, and ingredients in stock. For example, the ordering unit is realized by the specific processing unit 290 of the data processing device 12, and the AI ​​lists the ingredients needed and accesses the online supermarket site to complete the order. The correspondence between each unit and the device or control unit is not limited to the above example, and various changes are possible.

[0150] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0151] 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.

[0152] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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.

[0153] 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.

[0154] 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 the voice according to instructions from the processor 46.

[0155] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0156] 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 performed in a secure state.

[0157] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. 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.

[0158] 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.

[0159] 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.

[0160] 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0161] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific 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 program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0162] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0163] 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 a voice indicating a user input for 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.

[0164] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0165] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information 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.

[0166] For example, the schedule comprehension unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. For example, information is obtained from a family calendar app or memo app, and the AI ​​analyzes it. For example, the inventory analysis unit takes a photo of the inside of the refrigerator using the camera 42 of the robot 414, and the specific processing unit 290 of the data processing device 12 performs image analysis to identify ingredients in stock. For example, the menu suggestion unit is realized by the specific processing unit 290 of the data processing device 12, and the AI ​​generates a balanced menu taking into account the family's schedule, likes and dislikes, and ingredients in stock. For example, the ordering unit is realized by the specific processing unit 290 of the data processing device 12, and the AI ​​lists the necessary ingredients and accesses the online supermarket site to complete the order. The correspondence between each unit and the device or control unit is not limited to the above example, and various changes are possible.

[0167] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0168] FIG. 9 is a diagram showing 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. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0169] These emotions are distributed in the three o'clock direction of emotion map 400 and usually 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.

[0170] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out you go on emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0171] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0172] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0173] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the 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, "relief," "calm," and "encouraging," have similar emotion values.

[0174] 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.

[0175] 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 Universal Serial Bus (USB) 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.

[0176] In addition, 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 upon request from the data processing device 12.

[0177] 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.

[0178] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0179] The hardware resource that executes the specific process may be one of these various processors, or may be 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.

[0180] As an example of a configuration using one 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 configuration using a processor that realizes the functions of the 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.

[0181] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0182] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0183] The above description and illustrations are detailed descriptions 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, function, action, and effect is an example of the configuration, function, action, and effect 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 replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0184] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0185] (Appendix 1) A schedule management department that manages the schedule and likes and dislikes of each family member; an inventory analysis unit that checks the inventory status of the refrigerator by photo analysis based on the grasped information; A menu suggestion unit that suggests a menu based on a criterion for nutritional balance based on the analysis by the inventory analysis unit; An ordering unit that places an order with an online supermarket based on the proposal of the menu proposal unit. A system characterized by: (Appendix 2) The schedule understanding unit is AI will analyze information obtained from family calendar and memo apps 2. The system of claim 1, (Appendix 3) The inventory analysis unit is A photo of the inside of the refrigerator is taken, and AI performs image analysis to identify ingredients in stock. 2. The system of claim 1, (Appendix 4) The menu suggestion unit includes: Suggest a weekly menu based on nutritional balance criteria 2. The system of claim 1, (Appendix 5) The ordering unit, Place an order with the online supermarket 2. The system of claim 1, (Appendix 6) The inventory analysis unit is In response to a sudden shortage of ingredients, the chef checks the refrigerator again and suggests a different menu. 2. The system of claim 1, (Appendix 7) The menu suggestion unit includes: Partner with local farmers and food manufacturers to propose menus that prioritize the use of locally produced ingredients. 2. The system of claim 1, (Appendix 8) The schedule understanding unit is Estimate family members' emotions and adjust schedule priorities based on those emotions 2. The system of claim 1, (Appendix 9) The schedule understanding unit is Analyze the family's past schedule history and select the appropriate schedule acquisition method 2. The system of claim 1, (Appendix 10) The schedule understanding unit is When retrieving schedules, filtering is performed based on the health status and daily rhythm of family members. 2. The system of claim 1, (Appendix 11) The schedule understanding unit is Estimate the emotions of your family members and adjust how your schedule is displayed based on those emotions 2. The system of claim 1, (Appendix 12) The schedule understanding unit is When retrieving schedules, the system prioritizes retrieving the most relevant schedules by taking into account the geographic location information of family members. 2. The system of claim 1, (Appendix 13) The schedule understanding unit is When schedules are retrieved, analyze the family's social media activity and retrieve relevant schedules 2. The system of claim 1, (Appendix 14) The inventory analysis unit is Estimate family members' emotions and adjust the frequency of inventory analysis based on the estimated emotions 2. The system of claim 1, (Appendix 15) The inventory analysis unit is Conduct inventory analysis taking into account the expiration dates of food items in the refrigerator 2. The system of claim 1, (Appendix 16) The inventory analysis unit is Evaluate the nutritional value and preservation condition of ingredients during inventory analysis 2. The system of claim 1, (Appendix 17) The inventory analysis unit is Infer family members' emotions and adjust the order in which inventory analysis results are displayed based on the inferred emotions 2. The system of claim 1, (Appendix 18) The inventory analysis unit is When analyzing inventory, the layout of ingredients in the refrigerator is taken into account. 2. The system of claim 1, (Appendix 19) The inventory analysis unit is When analyzing inventory, improve the accuracy of the analysis by referring to the purchase history of ingredients. 2. The system of claim 1, (Appendix 20) The menu suggestion unit includes: Infer family members' emotions and adjust menu suggestions based on the inferred emotions 2. The system of claim 1, (Appendix 21) The menu suggestion unit includes: When proposing menus, consider the family's health and nutritional balance. 2. The system of claim 1, (Appendix 22) The menu suggestion unit includes: When proposing menus, suggest menus according to the season and weather 2. The system of claim 1, (Appendix 23) The menu suggestion unit includes: Estimate the emotions of family members and prioritize menus based on the estimated emotions 2. The system of claim 1, (Appendix 24) The menu suggestion unit includes: When proposing menus, make suggestions taking into account food allergy information of family members. 2. The system of claim 1, (Appendix 25) The menu suggestion unit includes: When suggesting menus, the system will refer to the family's past meal history to improve the accuracy of the suggestions. 2. The system of claim 1, (Appendix 26) The ordering unit, Inferring family members' emotions and adjusting order timing based on the inferred emotions 2. The system of claim 1, (Appendix 27) The ordering unit, When placing an order, choose the best ordering method based on the family's budget and purchasing history. 2. The system of claim 1, (Appendix 28) The ordering unit, When placing an order, take into consideration the supply situation and price fluctuations of ingredients. 2. The system of claim 1, (Appendix 29) The ordering unit, Estimate the emotions of family members and prioritize orders based on the estimated emotions 2. The system of claim 1, (Appendix 30) The ordering unit, When placing an order, consider the partnerships with local farmers and food manufacturers. 2. The system of claim 1, (Appendix 31) The ordering unit, When placing an order, monitor the delivery status of ingredients in real time and select the optimal delivery method. 2. The system of claim 1, [Explanation of symbols]

[0186] 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 schedule management department that manages the schedule and likes and dislikes of each family member; an inventory analysis unit that checks the inventory status of the refrigerator by photo analysis based on the grasped information; A menu suggestion unit that suggests a menu based on a criterion for nutritional balance based on the analysis by the inventory analysis unit; An ordering unit that places an order with an online supermarket based on the proposal of the menu proposal unit. A system characterized by:

2. The schedule understanding unit is Information is obtained from the family's calendar and memo apps and analyzed by AI.

2. The system of claim 1 .

3. The inventory analysis unit is A photo of the inside of the refrigerator is taken, and AI performs image analysis to identify ingredients in stock.

2. The system of claim 1 .

4. The menu suggestion unit includes: Suggest a weekly menu based on nutritional balance criteria 2. The system of claim 1 .

5. The inventory analysis unit is In response to a sudden shortage of ingredients, the chef checks the refrigerator again and suggests a different menu.

2. The system of claim 1 .

6. The menu suggestion unit includes: Partner with local farmers and food manufacturers to propose menus that prioritize the use of locally produced ingredients.

2. The system of claim 1 .

7. The schedule understanding unit is Estimate family members' emotions and adjust schedule priorities based on those emotions 2. The system of claim 1 .

8. The schedule understanding unit is Analyze the family's past schedule history and select the appropriate schedule acquisition method 2. The system of claim 1 .

9. The schedule understanding unit is When retrieving schedules, filtering is performed based on the health status and daily rhythm of family members.

2. The system of claim 1 .

Images