System

Abstract

An object of a system according to an embodiment is to efficiently perform purchase of food materials, management of use-by dates, and design of menus.SOLUTION: A system includes a purchase part, a management part, a menu part, and a re-purchase part. The purchase part purchases the food material. The management part manages the expiration date of the food material purchased by the purchase part. The menu part devises a menu based on the information managed by the management part. A re-purchase part re-purchases the food material based on the menu devised by the menu 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 an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

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

[0004] With conventional technology, meal management, such as purchasing ingredients, managing expiration dates, and planning menus, required time and effort, making it difficult to carry out efficiently.

[0005] The system according to the embodiment aims to efficiently carry out tasks from purchasing ingredients to managing expiration dates and planning menus. [Means for solving the problem]

[0006] The system according to the embodiment includes a shopping unit, a management unit, a menu planning unit, and a re-shopping unit. The shopping unit purchases ingredients. The management unit manages the expiration dates of ingredients purchased by the shopping unit. The menu planning unit plans a menu based on the information managed by the management unit. The re-shopping unit purchases ingredients again based on the menu planned by the menu planning unit. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently carry out everything from purchasing ingredients to managing expiration dates and planning menus. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A meal management system according to an embodiment of the present invention uses a generative AI to solve household meal management problems. The meal management system purchases ingredients, creates a consumption plan for ingredients taking into account expiration dates and family schedules, designs a nutritionally balanced menu, and then purchases ingredients again. This cycle, performed automatically by the generative AI, streamlines household meal management. For example, the meal management system lists ingredients taking into account family preferences and allergy information. Next, the meal management system manages expiration dates for purchased ingredients and creates a plan to consume them without waste. Furthermore, the meal management system proposes healthy menus taking into account the nutritional balance of the family. Finally, the meal management system tracks the consumption status of ingredients purchased in the previous shopping trip and lists the necessary ingredients. This allows the meal management system to streamline household meal management and provide healthy meals without waste. For example, by listing ingredients taking into account family preferences and allergy information, meals that satisfy the entire family can be provided. Furthermore, managing expiration dates for purchased ingredients and creating a plan to consume them without waste can reduce food waste. Furthermore, by considering the nutritional balance of the family and proposing healthy menus, the health of the family can be maintained. Finally, by understanding the consumption status of ingredients purchased on the previous shopping trip and making a list of necessary ingredients, the family can shop efficiently.

[0029] A meal management system according to an embodiment includes a shopping unit, a management unit, a menu planning unit, and a re-shopping unit. The shopping unit purchases ingredients. For example, the shopping unit purchases ingredients online. The shopping unit can also purchase ingredients in-store. The shopping unit can also list ingredients taking into consideration family preferences and allergy information. For example, the shopping unit learns family preferences and prioritizes the purchase of specific ingredients. The shopping unit can also list allergen-free ingredients based on allergy information. The management unit manages the expiration dates of ingredients purchased by the shopping unit. For example, the management unit registers the expiration dates of purchased ingredients in a database and notifies the user of ingredients whose expiration dates are approaching. The management unit can also plan ingredient consumption taking into consideration family schedules. For example, the management unit keeps track of family schedules and suggests easy-to-prepare menus for busy days. The menu planning unit creates menus based on the information managed by the management unit. For example, the menu planning unit considers the nutritional balance of the family and suggests healthy menus. For example, the menu planning unit can learn about the health status of family members and suggest menus using ingredients that are rich in specific nutrients. The re-shopping unit re-shops for ingredients based on the menu devised by the menu planning unit. For example, the re-shopping unit can grasp the consumption status of ingredients purchased in the previous shopping trip and make a list of necessary ingredients. As a result, the meal management system according to the embodiment can improve the efficiency of household meal management and provide healthy meals without waste.

[0030] The shopping unit can list ingredients based on family preferences and allergy information. The shopping unit, for example, learns family preferences and prioritizes listing specific ingredients. For example, the shopping unit analyzes family members' favorite ingredients based on past purchase history and lists them. The shopping unit can also list allergen-free ingredients based on allergy information. For example, the shopping unit registers family members' allergy information in a database and lists allergen-free ingredients. This allows appropriate ingredients to be purchased based on family members' preferences and allergy information. Some or all of the above-mentioned processing in the shopping unit may be performed using or without the generation AI. For example, the shopping unit can input family members' preferences and allergy information into the generation AI, which then lists appropriate ingredients.

[0031] The management unit can manage the expiration dates of purchased ingredients and make plans to consume them without waste. For example, the management unit registers the expiration dates of purchased ingredients in a database and notifies users of ingredients that are approaching their expiration dates. For example, the management unit can list ingredients that are approaching their expiration dates and encourage consumption. The management unit can also make ingredient consumption plans taking into account family schedules. For example, the management unit can keep track of family schedules and suggest easy-to-prepare menus for busy days. Furthermore, the management unit can also suggest methods for storing ingredients. For example, the management unit can suggest the optimal storage method for ingredients that require refrigeration. This reduces ingredient waste and enables efficient consumption. Some or all of the above-mentioned processing in the management unit may be performed using or without the generation AI. For example, the management unit can input the expiration dates of purchased ingredients into the generation AI, which can then manage the expiration dates.

[0032] The menu section can suggest healthy menus based on the nutritional balance of the family. The menu section, for example, suggests healthy menus taking into account the nutritional balance of the family. For example, the menu section learns the health status of the family and suggests menus using ingredients that are rich in specific nutrients. The menu section can also customize menus according to the season or event. For example, the menu section suggests dishes using seasonal ingredients for each season. Furthermore, the menu section can suggest menus taking into account the preferences of the family. For example, the menu section analyzes and suggests dishes that the family likes based on past meal history. This makes it possible to suggest menus that take into account the health of the family. Some or all of the above-mentioned processing in the menu section may be performed using a generation AI, or may be performed without using a generation AI. For example, the menu section can input the nutritional balance and health status of the family into the generation AI, which then suggests healthy menus.

[0033] The re-shopping unit can grasp the consumption status of ingredients purchased in the previous shopping trip and list the necessary ingredients. For example, the re-shopping unit registers the consumption status of ingredients purchased in the previous shopping trip in a database and grasps the consumption status. For example, the re-shopping unit lists consumed ingredients and necessary ingredients. The re-shopping unit can also analyze consumption frequency and determine priorities for re-shopping. For example, the re-shopping unit prioritizes the list of ingredients consumed frequently. Furthermore, the re-shopping unit can adjust the re-shopping list according to the season and weather. For example, the re-shopping unit lists ingredients suitable for cold dishes in the summer. This allows ingredients to be purchased efficiently and without waste. Some or all of the above-mentioned processes in the re-shopping unit may be performed using or without the generation AI. For example, the re-shopping unit can input the consumption status of ingredients purchased in the previous shopping trip into the generation AI and have the generation AI list the necessary ingredients.

[0034] The shopping department can analyze past purchase history and select a method for listing ingredients. For example, the shopping department prioritizes listing ingredients that have been frequently purchased in the past. For example, the shopping department analyzes and lists ingredients that family members prefer based on past purchase history. The shopping department can also list ingredients by season based on past purchase history. For example, the shopping department lists watermelon and tomatoes in summer and radishes and Chinese cabbage in winter. This allows the optimal ingredients to be listed based on past purchase history. Some or all of the above-mentioned processing in the shopping department may be performed using or without the generation AI. For example, the shopping department can input past purchase history into the generation AI, which then lists the optimal ingredients.

[0035] The shopping department can adjust the list of ingredients according to the season and weather. For example, in summer, the shopping department lists ingredients suitable for cold dishes. For example, the shopping department lists watermelon and tomatoes. The shopping department can also list ingredients suitable for hot dishes in winter. For example, the shopping department lists radishes and Chinese cabbage. The shopping department can also list ingredients that can be stored during rainy weather. For example, the shopping department lists canned goods and dried foods. This makes it possible to list ingredients according to the season and weather. Some or all of the above-mentioned processes in the shopping department may be performed using or without the generation AI. For example, the shopping department can input information about the season and weather into the generation AI, which then lists appropriate ingredients.

[0036] The shopping unit can learn the health status of family members and list ingredients suitable for specific health conditions. For example, if a family member has high blood pressure, the shopping unit can list low-salt ingredients. For example, the shopping unit can list low-salt soy sauce and unsalted butter. The shopping unit can also list low-carb ingredients if a family member has diabetes. For example, the shopping unit can list low-carb bread and zero-carb drinks. The shopping unit can also list allergen-free ingredients if a family member has allergies. For example, the shopping unit can list gluten-free pasta and dairy-free ice cream. This makes it possible to list ingredients according to the family member's health status. Some or all of the above-mentioned processes in the shopping unit may be performed using or without the generation AI. For example, the shopping unit can input the family member's health status into the generation AI, which can then list appropriate ingredients.

[0037] The shopping unit can create a list based on the geographical availability of ingredients. For example, the shopping unit lists ingredients available at a local market. For example, the shopping unit lists fresh vegetables and fruits at a local market. The shopping unit can also create a list of ingredients harvested locally by season. For example, the shopping unit lists bamboo shoots and strawberries in spring and sweet potatoes and persimmons in autumn. The shopping unit can also create a list of ingredients produced in a nearby geographical location. For example, the shopping unit lists vegetables and fruits produced by local farms. This allows for a list that takes into account the geographical availability of ingredients. Some or all of the above-described processing in the shopping unit may be performed using or without the generation AI. For example, the shopping unit can input the geographical availability of ingredients into the generation AI, which then creates a list of appropriate ingredients.

[0038] The shopping unit can analyze the social media activity of the family and list related ingredients. For example, the shopping unit lists ingredients based on recipes shared by the family on social media. For example, the shopping unit lists ingredients needed for recipes shared by the family. The shopping unit can also list ingredients based on dishes that the family has "liked" on social media. For example, the shopping unit lists ingredients used in dishes that the family has "liked." The shopping unit can also list ingredients based on posts of cooking accounts that the family follows on social media. For example, the shopping unit lists ingredients used in posts of cooking accounts that the family follows. This makes it possible to list ingredients based on the social media activity of the family. Some or all of the above-mentioned processing in the shopping unit may be performed using or without the generation AI. For example, the shopping unit can input the social media activity of the family into the generation AI, which then lists appropriate ingredients.

[0039] The shopping department can customize the list-up method by reflecting past feedback. For example, the shopping department prioritizes the list of preferred ingredients based on feedback from family members. For example, the shopping department lists ingredients that family members like. The shopping department can also exclude ingredients that should be avoided from the list based on feedback from family members. For example, the shopping department excludes ingredients that family members dislike from the list. The shopping department can also periodically review the list-up method based on feedback from family members. For example, the shopping department periodically updates family member preferences and allergy information and reviews the list-up method. This makes it possible to customize the list-up method based on past feedback. Some or all of the above-described processing in the shopping department may be performed using or without the generation AI. For example, the shopping department can input feedback from family members into the generation AI, which can then customize an appropriate list-up method.

[0040] The management unit can learn how to store purchased ingredients and suggest storage methods. For example, the management unit suggests the optimal temperature and humidity for ingredients that require refrigeration. For example, the management unit suggests the temperature setting for a refrigerator. The management unit can also suggest the optimal storage period for ingredients that can be frozen. For example, the management unit suggests the frozen storage period. The management unit can also suggest the optimal storage location for ingredients that can be stored at room temperature. For example, the management unit suggests a location that is suitable for room temperature storage. This makes it possible to suggest the optimal storage method. Some or all of the above-mentioned processing in the management unit may be performed using or without the generation AI. For example, the management unit can input the storage method for purchased ingredients into the generation AI, which then suggests an appropriate storage method.

[0041] The management unit can determine the priority of expiration dates based on the consumption frequency of ingredients. The management unit, for example, suggests that frequently consumed ingredients be consumed preferentially. For example, the management unit lists frequently consumed ingredients and encourages their consumption. The management unit can also suggest that ingredients with an approaching expiration date be consumed preferentially. For example, the management unit lists ingredients with an approaching expiration date and encourages their consumption. The management unit can also suggest that storage methods be reviewed for ingredients that are consumed less frequently. For example, the management unit reviews storage methods for ingredients that are consumed less frequently. This makes it possible to determine the priority of expiration dates based on consumption frequency. Some or all of the above-described processing in the management unit may be performed using or without the generation AI. For example, the management unit can input the consumption frequency of ingredients into the generation AI, which can then determine appropriate expiration date priorities.

[0042] The management unit can manage expiration dates based on the nutritional value of ingredients. The management unit, for example, suggests that nutritious ingredients be consumed first. For example, the management unit can list ingredients rich in vitamins and minerals and encourage their consumption. The management unit can also suggest that ingredients whose nutritional value is likely to deteriorate be consumed early. For example, the management unit can list vegetables and fruits whose nutritional value is likely to deteriorate and encourage their consumption. The management unit can also manage expiration dates by taking nutritional balance into consideration. For example, the management unit can take into consideration the nutritional balance of a family and suggest that ingredients containing large amounts of specific nutrients be consumed first. This makes it possible to manage expiration dates by taking nutritional value into consideration. Some or all of the above-mentioned processing in the management unit may be performed using or without the generation AI. For example, the management unit can input the nutritional value of ingredients into the generation AI, which can then manage expiration dates appropriately.

[0043] The management unit can manage ingredients taking into account the geographical storage conditions of the ingredients. For example, the management unit suggests storage methods suitable for the local climate. For example, the management unit suggests refrigerated or frozen storage methods suited to the local climate. The management unit can also manage ingredients taking into account seasonal storage conditions. For example, the management unit recommends refrigerated storage in summer and room temperature storage in winter. The management unit can also suggest optimal storage methods for ingredients with geographically different storage conditions. For example, the management unit suggests storage methods for imported ingredients. This enables management taking into account geographical storage conditions. Some or all of the above-mentioned processing in the management unit may be performed using or without the generation AI. For example, the management unit can input the geographical storage conditions of ingredients into the generation AI, which then suggests an appropriate storage method.

[0044] The management unit can improve the accuracy of expiration date management by referring to related recipe information. The management unit, for example, suggests recipes using ingredients with upcoming expiration dates. For example, the management unit lists ingredients with upcoming expiration dates and suggests recipes using them. The management unit can also suggest recipes using ingredients with long expiration dates. For example, the management unit lists ingredients with long expiration dates and suggests recipes using them. The management unit can also suggest recipes that prioritize the consumption of ingredients with upcoming expiration dates. For example, the management unit suggests dishes that use ingredients with upcoming expiration dates and encourages consumption. This improves the accuracy of expiration date management by referring to recipe information. Some or all of the above-mentioned processes in the management unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the management unit can input related recipe information into the generation AI, which then suggests appropriate recipes.

[0045] The management unit can manage expiration dates based on the market price of the ingredients. The management unit, for example, suggests that ingredients with high market prices be consumed first. For example, the management unit can list ingredients with high market prices and encourage their consumption. The management unit can also suggest ways to extend the storage period of ingredients with low market prices. For example, the management unit can suggest storage methods for ingredients with low market prices. The management unit can also manage expiration dates taking market price fluctuations into account. For example, the management unit can register market price fluctuations in a database and manage expiration dates based on that. This makes it possible to manage expiration dates taking market prices into account. Some or all of the above-mentioned processing in the management unit may be performed using or without the generation AI. For example, the management unit can input the market price of ingredients into the generation AI, which can then manage expiration dates appropriately.

[0046] The menu section can analyze the family's past meal history and suggest a menu. For example, the menu section can re-suggest dishes that were popular in the past. For example, the menu section can analyze the family's favorite dishes from the past meal history and suggest them. The menu section can also suggest seasonal dishes based on the past meal history. For example, the menu section can suggest cold dishes in the summer and hot dishes in the winter. This makes it possible to suggest an optimal menu based on the past meal history. Some or all of the above-mentioned processing in the menu section may be performed using a generation AI, or may be performed without using a generation AI. For example, the menu section can input the family's past meal history into the generation AI, which can then suggest an appropriate menu.

[0047] The menu section can customize menus according to the season or event. The menu section, for example, suggests dishes using seasonal ingredients for each season. For example, the menu section suggests dishes using bamboo shoots and strawberries in spring, and dishes using sweet potatoes and persimmons in autumn. The menu section can also suggest dishes suited to specific events (birthdays, Christmas, etc.). For example, the menu section suggests roast chicken and Christmas cake for Christmas. The menu section can also suggest special menus according to the season or event. For example, the menu section suggests yakisoba and takoyaki for a summer festival. This makes it possible to customize menus according to the season or event. Some or all of the above-mentioned processing in the menu section may be performed using a generation AI, or may be performed without using a generation AI. For example, the menu section can input information about the season or event into the generation AI, which then suggests an appropriate menu.

[0048] The menu section can learn the health status of family members and suggest menus that are rich in specific nutrients. For example, if a family member has high blood pressure, the menu section can suggest low-salt dishes. For example, the menu section can suggest dishes that use reduced-salt soy sauce or unsalted butter. The menu section can also suggest low-carb dishes if a family member has diabetes. For example, the menu section can suggest dishes that use low-carb bread or zero-carb drinks. The menu section can also suggest dishes that do not contain allergens if a family member has allergies. For example, the menu section can suggest dishes that use gluten-free pasta or dairy-free ice cream. This makes it possible to suggest menus that are rich in nutrients according to the family member's health status. Some or all of the above-mentioned processes in the menu section can be performed using or without the generation AI. For example, the menu section can input the health status of family members into the generation AI, which can then suggest an appropriate menu.

[0049] The menu unit can suggest menus based on the geographical availability of ingredients. The menu unit, for example, suggests dishes using ingredients available at a local market. For example, the menu unit suggests dishes using fresh vegetables and fruits at a local market. The menu unit can also suggest dishes using locally harvested ingredients each season. For example, the menu unit suggests dishes using bamboo shoots and strawberries in spring, and dishes using sweet potatoes and persimmons in autumn. The menu unit can also suggest dishes using ingredients produced in a geographically nearby location. For example, the menu unit suggests dishes using vegetables and fruits produced by local farms. This makes it possible to suggest menus that take into account the geographical availability of ingredients. Some or all of the above-mentioned processing in the menu unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the menu unit can input the geographical availability of ingredients into the generation AI, which then suggests an appropriate menu.

[0050] The menu section can analyze the social media activities of family members and suggest related menus. For example, the menu section can suggest menus based on recipes shared by family members on social media. For example, the menu section can suggest dishes using ingredients needed for recipes shared by family members. The menu section can also suggest menus based on dishes that family members have "liked" on social media. For example, the menu section can suggest dishes using ingredients used in dishes that family members have "liked." The menu section can also suggest menus based on posts from cooking accounts that family members follow on social media. For example, the menu section can suggest dishes using ingredients used in posts from cooking accounts that family members follow. This makes it possible to suggest menus based on the social media activities of family members. Some or all of the above-mentioned processing in the menu section can be performed using or without the generation AI. For example, the menu section can input the social media activities of family members into the generation AI, which can then suggest appropriate menus.

[0051] The menu section can customize the menu suggestion method by reflecting past feedback. For example, the menu section prioritizes suggesting preferred dishes based on feedback from family members. For example, the menu section suggests dishes that family members like. The menu section can also exclude dishes that should be avoided from the suggestions based on feedback from family members. For example, the menu section excludes dishes that family members dislike from the suggestions. The menu section can also periodically review the suggestion method based on feedback from family members. For example, the menu section periodically updates family member preferences and allergy information and reviews the suggestion method. This makes it possible to customize the menu suggestion method based on past feedback. Some or all of the above-mentioned processing in the menu section may be performed using or without the generation AI. For example, the menu section can input feedback from family members into the generation AI, which can then customize an appropriate suggestion method.

[0052] The re-shopping unit can analyze the consumption status of ingredients purchased in the previous shopping trip and select a re-shopping method. The re-shopping unit, for example, lists necessary ingredients based on the consumption status of ingredients purchased in the previous shopping trip. For example, the re-shopping unit lists consumed ingredients and necessary ingredients. The re-shopping unit can also analyze the consumption frequency of ingredients purchased in the previous shopping trip and determine the priority of re-shopping. For example, the re-shopping unit prioritizes the list of ingredients consumed frequently. Furthermore, the re-shopping unit can plan a re-shopping trip to ensure efficient consumption based on the consumption status of ingredients purchased in the previous shopping trip. For example, the re-shopping unit reviews the storage method for ingredients consumed infrequently. This makes it possible to select an optimal re-shopping method based on the previous consumption status. Some or all of the above-mentioned processing in the re-shopping unit may be performed using or without the generation AI. For example, the re-shopping unit can input the consumption status of ingredients purchased in the previous shopping trip into the generation AI, which can select an appropriate re-shopping method.

[0053] The re-shopping unit can adjust the re-shopping list according to the season and weather. For example, in summer, the re-shopping unit lists ingredients suitable for cold dishes. For example, the re-shopping unit lists watermelon and tomatoes. The re-shopping unit can also list ingredients suitable for hot dishes in winter. For example, the re-shopping unit lists radishes and Chinese cabbage. The re-shopping unit can also list ingredients that can be stored during rainy weather. For example, the re-shopping unit lists canned goods and dried foods. This makes it possible to create a re-shopping list according to the season and weather. Some or all of the above-mentioned processes in the re-shopping unit may be performed using or without the generation AI. For example, the re-shopping unit can input information about the season and weather into the generation AI, which then lists appropriate ingredients.

[0054] The re-shopping unit can learn the health conditions of family members and add ingredients suitable for specific health conditions to the re-shopping list. For example, if a family member has high blood pressure, the re-shopping unit can list low-salt ingredients. For example, the re-shopping unit can list low-salt soy sauce and unsalted butter. The re-shopping unit can also list low-carb ingredients if a family member has diabetes. For example, the re-shopping unit can list low-carb bread and zero-carb drinks. The re-shopping unit can also list allergen-free ingredients if a family member has allergies. For example, the re-shopping unit can list gluten-free pasta and dairy-free ice cream. This makes it possible to create a re-shopping list based on the family member's health condition. Some or all of the above-described processing in the re-shopping unit may be performed using or without the generation AI. For example, the re-shopping unit can input the family member's health conditions into the generation AI, which can then list appropriate ingredients.

[0055] The re-shopping unit can perform re-shopping based on the geographical availability of ingredients. The re-shopping unit, for example, lists ingredients available at a local market. For example, the re-shopping unit lists fresh vegetables and fruits at a local market. The re-shopping unit can also list ingredients harvested locally by season. For example, the re-shopping unit lists bamboo shoots and strawberries in spring and sweet potatoes and persimmons in autumn. The re-shopping unit can also list ingredients produced in a geographically nearby location. For example, the re-shopping unit lists vegetables and fruits produced by local farms. This enables re-shopping that takes into account the geographical availability of ingredients. Some or all of the above-described processing in the re-shopping unit may be performed using or without the generation AI. For example, the re-shopping unit can input the geographical availability of ingredients into the generation AI, which then lists appropriate ingredients.

[0056] The re-shopping unit can analyze the social media activity of the family and add related ingredients to the re-shopping list. For example, the re-shopping unit lists ingredients based on recipes shared by the family on social media. For example, the re-shopping unit lists ingredients needed for recipes shared by the family. The re-shopping unit can also list ingredients based on dishes that the family has "liked" on social media. For example, the re-shopping unit lists ingredients used in dishes that the family has "liked." The re-shopping unit can also list ingredients based on posts of cooking accounts that the family follows on social media. For example, the re-shopping unit lists ingredients used in posts of cooking accounts that the family follows. This makes it possible to create a re-shopping list based on the social media activity of the family. Some or all of the above-described processing in the re-shopping unit may be performed using or without the generation AI. For example, the re-shopping unit can input the social media activity of the family into the generation AI, which then lists appropriate ingredients.

[0057] The re-shopping unit can customize the re-shopping method by reflecting past feedback. For example, the re-shopping unit prioritizes a list of preferred ingredients based on feedback from family members. For example, the re-shopping unit lists ingredients that family members like. The re-shopping unit can also exclude ingredients that should be avoided from the list based on feedback from family members. For example, the re-shopping unit excludes ingredients that family members dislike from the list. The re-shopping unit can also periodically review the list method based on feedback from family members. For example, the re-shopping unit periodically updates family member preferences and allergy information and reviews the list method. This makes it possible to customize the re-shopping method based on past feedback. Some or all of the above-described processing in the re-shopping unit may be performed using or without the generation AI. For example, the re-shopping unit can input feedback from family members into the generation AI, which can then customize an appropriate list method.

[0058] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0059] The purchasing unit can create a list based on the geographical availability of ingredients. For example, it can list ingredients available at a local market. For example, it can list fresh vegetables and fruits at a local market. It can also list ingredients harvested locally by season. For example, it can list bamboo shoots and strawberries in spring and sweet potatoes and persimmons in autumn. It can also list ingredients produced in a nearby geographical location. For example, it can list vegetables and fruits produced by local farms. This makes it possible to create a list that takes into account the geographical availability of ingredients. Some or all of the above-mentioned processing in the purchasing unit may be performed using or without the generation AI. For example, the purchasing unit can input the geographical availability of ingredients into the generation AI, which can then list appropriate ingredients.

[0060] The management unit can manage expiration dates based on the nutritional value of ingredients. For example, it can suggest that nutritious ingredients be consumed first. For example, it can list ingredients rich in vitamins and minerals and encourage their consumption. It can also suggest that ingredients whose nutritional value is likely to deteriorate be consumed early. For example, it can list vegetables and fruits whose nutritional value is likely to deteriorate and encourage their consumption. It can also manage expiration dates by taking nutritional balance into consideration. For example, it can take into consideration the nutritional balance of a family and suggest that ingredients containing large amounts of specific nutrients be consumed first. This makes it possible to manage expiration dates by taking nutritional value into consideration. Some or all of the above-mentioned processing in the management unit may be performed using or without the generation AI. For example, the management unit can input the nutritional value of ingredients into the generation AI, which can then manage expiration dates appropriately.

[0061] The menu section can analyze the family's past meal history and suggest menus. For example, it can suggest dishes that were popular in the past again. For example, it can analyze the family's favorite dishes from the past meal history and suggest them. It can also suggest seasonal dishes based on the past meal history. For example, it can suggest cold dishes in the summer and hot dishes in the winter. This makes it possible to suggest optimal menus based on the past meal history. Some or all of the above-mentioned processing in the menu section may be performed using a generation AI, or may be performed without using a generation AI. For example, the menu section can input the family's past meal history into the generation AI, which then suggests an appropriate menu.

[0062] The re-shopping unit can analyze the consumption status of ingredients purchased in the previous shopping trip and select a re-shopping method. For example, it can list necessary ingredients based on the consumption status of ingredients purchased in the previous shopping trip. For example, it can list consumed ingredients and then list necessary ingredients. It can also analyze the consumption frequency of ingredients purchased in the previous shopping trip and determine the priority of re-shopping. For example, it can prioritize and list ingredients that are consumed frequently. Furthermore, it can plan a re-shopping trip to ensure that ingredients are consumed without waste based on the consumption status of ingredients purchased in the previous shopping trip. For example, it can review the storage method for ingredients that are consumed infrequently. This makes it possible to select the optimal re-shopping method based on the previous consumption status. Some or all of the above-mentioned processing in the re-shopping unit can be performed using or without using the generation AI. For example, the consumption status of ingredients purchased in the previous shopping trip can be input into the generation AI, and the generation AI can select an appropriate re-shopping method.

[0063] The re-shopping unit can adjust the re-shopping list according to the season and weather. For example, in summer, it can list ingredients suitable for cold dishes, such as watermelon and tomatoes. It can also list ingredients suitable for hot dishes in winter, such as radishes and Chinese cabbage. It can also list ingredients that can be stored well in rainy weather, such as canned goods and dried foods. This makes it possible to create a re-shopping list according to the season and weather. Some or all of the above-mentioned processes in the re-shopping unit may be performed using or without the generation AI. For example, information about the season and weather can be input into the generation AI, which can then list appropriate ingredients.

[0064] The re-shopping unit can learn the health conditions of family members and add ingredients suitable for specific health conditions to the re-shopping list. For example, if a family member has high blood pressure, it can list low-salt ingredients. For example, it can list low-salt soy sauce and unsalted butter. Also, if a family member has diabetes, it can list low-carb ingredients. For example, it can list low-carb bread and zero-carb drinks. Also, if a family member has allergies, it can list ingredients that do not contain allergens. For example, it can list gluten-free pasta and dairy-free ice cream. This makes it possible to create a re-shopping list according to the family member's health conditions. Some or all of the above-mentioned processes in the re-shopping unit may be performed using or without the generation AI. For example, the re-shopping unit can input the health conditions of family members into the generation AI, which can then list appropriate ingredients.

[0065] The re-shopping unit can analyze the social media activity of the family and add related ingredients to the re-shopping list. For example, it can list ingredients based on recipes shared by the family on social media. For example, it can list ingredients needed for recipes shared by the family. It can also list ingredients based on dishes that the family has "liked" on social media. For example, it can list ingredients used in dishes that the family has "liked." It can also list ingredients based on posts of cooking accounts that the family follows on social media. For example, it can list ingredients used in posts of cooking accounts that the family follows. This makes it possible to create a re-shopping list based on the family's social media activity. Some or all of the above-mentioned processing in the re-shopping unit may be performed using or without the generation AI. For example, the re-shopping unit can input the family's social media activity into the generation AI, which can then list appropriate ingredients.

[0066] The re-shopping unit can customize the re-shopping method by reflecting past feedback. For example, based on feedback from family members, it can prioritize and list preferred ingredients. For example, it can list ingredients that family members like. Also, based on feedback from family members, it can exclude ingredients that should be avoided from the list. For example, it can exclude ingredients that family members dislike from the list. Also, it can periodically review the list method based on feedback from family members. For example, it can periodically update family members' preferences and allergy information and review the list method. This makes it possible to customize the re-shopping method based on past feedback. Some or all of the above-mentioned processes in the re-shopping unit may be performed using or without using the generation AI. For example, the re-shopping unit can input feedback from family members into the generation AI, which can then customize an appropriate list method.

[0067] The processing flow of the first embodiment will be briefly explained below.

[0068] Step 1: The shopping section purchases ingredients. For example, ingredients can be purchased online or in-store. It is also possible to create a list of ingredients taking into account family preferences and allergy information. It is possible to learn family preferences and prioritize the purchase of specific ingredients, or to create a list of allergen-free ingredients based on allergy information. Step 2: The management department manages the expiration dates of ingredients purchased by the purchasing department. For example, it registers the expiration dates of purchased ingredients in a database and notifies customers of ingredients that are approaching their expiration date. It can also plan food consumption taking into account family schedules. It keeps track of family schedules and suggests easy-to-cook menus for busy days. Step 3: The menu department creates a menu based on the information managed by the management department. For example, it can propose a healthy menu that takes into account the nutritional balance of the family. It can learn about the family's health status and suggest menus that use ingredients that are rich in specific nutrients. Step 4: The re-shopping department goes out to buy ingredients again based on the menu devised by the menu planning department. For example, they check the consumption status of ingredients purchased in the previous shopping trip and make a list of the ingredients that will be needed.

[0069] (Example 2) A meal management system according to an embodiment of the present invention uses a generative AI to solve household meal management problems. The meal management system purchases ingredients, creates a consumption plan for ingredients taking into account expiration dates and family schedules, designs a nutritionally balanced menu, and then purchases ingredients again. This cycle, performed automatically by the generative AI, streamlines household meal management. For example, the meal management system lists ingredients taking into account family preferences and allergy information. Next, the meal management system manages expiration dates for purchased ingredients and creates a plan to consume them without waste. Furthermore, the meal management system proposes healthy menus taking into account the nutritional balance of the family. Finally, the meal management system tracks the consumption status of ingredients purchased in the previous shopping trip and lists the necessary ingredients. This allows the meal management system to streamline household meal management and provide healthy meals without waste. For example, by listing ingredients taking into account family preferences and allergy information, meals that satisfy the entire family can be provided. Furthermore, managing expiration dates for purchased ingredients and creating a plan to consume them without waste can reduce food waste. Furthermore, by considering the nutritional balance of the family and proposing healthy menus, the health of the family can be maintained. Finally, by understanding the consumption status of ingredients purchased on the previous shopping trip and making a list of necessary ingredients, the family can shop efficiently.

[0070] A meal management system according to an embodiment includes a shopping unit, a management unit, a menu planning unit, and a re-shopping unit. The shopping unit purchases ingredients. For example, the shopping unit purchases ingredients online. The shopping unit can also purchase ingredients in-store. The shopping unit can also list ingredients taking into consideration family preferences and allergy information. For example, the shopping unit learns family preferences and prioritizes the purchase of specific ingredients. The shopping unit can also list allergen-free ingredients based on allergy information. The management unit manages the expiration dates of ingredients purchased by the shopping unit. For example, the management unit registers the expiration dates of purchased ingredients in a database and notifies the user of ingredients whose expiration dates are approaching. The management unit can also plan ingredient consumption taking into consideration family schedules. For example, the management unit keeps track of family schedules and suggests easy-to-prepare menus for busy days. The menu planning unit creates menus based on the information managed by the management unit. For example, the menu planning unit considers the nutritional balance of the family and suggests healthy menus. For example, the menu planning unit can learn about the health status of family members and suggest menus using ingredients that are rich in specific nutrients. The re-shopping unit re-shops for ingredients based on the menu devised by the menu planning unit. For example, the re-shopping unit can grasp the consumption status of ingredients purchased in the previous shopping trip and make a list of necessary ingredients. As a result, the meal management system according to the embodiment can improve the efficiency of household meal management and provide healthy meals without waste.

[0071] The shopping unit can list ingredients based on family preferences and allergy information. The shopping unit, for example, learns family preferences and prioritizes listing specific ingredients. For example, the shopping unit analyzes family members' favorite ingredients based on past purchase history and lists them. The shopping unit can also list allergen-free ingredients based on allergy information. For example, the shopping unit registers family members' allergy information in a database and lists allergen-free ingredients. This allows appropriate ingredients to be purchased based on family members' preferences and allergy information. Some or all of the above-mentioned processing in the shopping unit may be performed using or without the generation AI. For example, the shopping unit can input family members' preferences and allergy information into the generation AI, which then lists appropriate ingredients.

[0072] The management unit can manage the expiration dates of purchased ingredients and make plans to consume them without waste. For example, the management unit registers the expiration dates of purchased ingredients in a database and notifies users of ingredients that are approaching their expiration dates. For example, the management unit can list ingredients that are approaching their expiration dates and encourage consumption. The management unit can also make ingredient consumption plans taking into account family schedules. For example, the management unit can keep track of family schedules and suggest easy-to-prepare menus for busy days. Furthermore, the management unit can also suggest methods for storing ingredients. For example, the management unit can suggest the optimal storage method for ingredients that require refrigeration. This reduces ingredient waste and enables efficient consumption. Some or all of the above-mentioned processing in the management unit may be performed using or without the generation AI. For example, the management unit can input the expiration dates of purchased ingredients into the generation AI, which can then manage the expiration dates.

[0073] The menu section can suggest healthy menus based on the nutritional balance of the family. The menu section, for example, suggests healthy menus taking into account the nutritional balance of the family. For example, the menu section learns the health status of the family and suggests menus using ingredients that are rich in specific nutrients. The menu section can also customize menus according to the season or event. For example, the menu section suggests dishes using seasonal ingredients for each season. Furthermore, the menu section can suggest menus taking into account the preferences of the family. For example, the menu section analyzes and suggests dishes that the family likes based on past meal history. This makes it possible to suggest menus that take into account the health of the family. Some or all of the above-mentioned processing in the menu section may be performed using a generation AI, or may be performed without using a generation AI. For example, the menu section can input the nutritional balance and health status of the family into the generation AI, which then suggests healthy menus.

[0074] The re-shopping unit can grasp the consumption status of ingredients purchased in the previous shopping trip and list the necessary ingredients. For example, the re-shopping unit registers the consumption status of ingredients purchased in the previous shopping trip in a database and grasps the consumption status. For example, the re-shopping unit lists consumed ingredients and necessary ingredients. The re-shopping unit can also analyze consumption frequency and determine priorities for re-shopping. For example, the re-shopping unit prioritizes the list of ingredients consumed frequently. Furthermore, the re-shopping unit can adjust the re-shopping list according to the season and weather. For example, the re-shopping unit lists ingredients suitable for cold dishes in the summer. This allows ingredients to be purchased efficiently and without waste. Some or all of the above-mentioned processes in the re-shopping unit may be performed using or without the generation AI. For example, the re-shopping unit can input the consumption status of ingredients purchased in the previous shopping trip into the generation AI and have the generation AI list the necessary ingredients.

[0075] The shopping unit can estimate the emotions of family members and adjust the list of ingredients based on the estimated emotions of the family members. For example, if a family member is feeling stressed, the shopping unit prioritizes ingredients with a relaxing effect on the list. For example, the shopping unit may list chamomile tea and lavender. If a family member is having fun, the shopping unit can also list ingredients suitable for a party. For example, the shopping unit may list cheese and crackers. If a family member is tired, the shopping unit can also list ingredients that are easy to prepare. For example, the shopping unit may list frozen foods and instant foods. This enables the list of ingredients to be tailored to the emotions of the family members. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the shopping unit may be performed using the generative AI, or may be performed without using the generative AI. For example, the shopping team can input the family's emotions into the generation AI, which can then list appropriate ingredients.

[0076] The shopping department can analyze past purchase history and select a method for listing ingredients. For example, the shopping department prioritizes listing ingredients that have been frequently purchased in the past. For example, the shopping department analyzes and lists ingredients that family members prefer based on past purchase history. The shopping department can also list ingredients by season based on past purchase history. For example, the shopping department lists watermelon and tomatoes in summer and radishes and Chinese cabbage in winter. This allows the optimal ingredients to be listed based on past purchase history. Some or all of the above-mentioned processing in the shopping department may be performed using or without the generation AI. For example, the shopping department can input past purchase history into the generation AI, which then lists the optimal ingredients.

[0077] The shopping department can adjust the list of ingredients according to the season and weather. For example, in summer, the shopping department lists ingredients suitable for cold dishes. For example, the shopping department lists watermelon and tomatoes. The shopping department can also list ingredients suitable for hot dishes in winter. For example, the shopping department lists radishes and Chinese cabbage. The shopping department can also list ingredients that can be stored during rainy weather. For example, the shopping department lists canned goods and dried foods. This makes it possible to list ingredients according to the season and weather. Some or all of the above-mentioned processes in the shopping department may be performed using or without the generation AI. For example, the shopping department can input information about the season and weather into the generation AI, which then lists appropriate ingredients.

[0078] The shopping unit can learn the health status of family members and list ingredients suitable for specific health conditions. For example, if a family member has high blood pressure, the shopping unit can list low-salt ingredients. For example, the shopping unit can list low-salt soy sauce and unsalted butter. The shopping unit can also list low-carb ingredients if a family member has diabetes. For example, the shopping unit can list low-carb bread and zero-carb drinks. The shopping unit can also list allergen-free ingredients if a family member has allergies. For example, the shopping unit can list gluten-free pasta and dairy-free ice cream. This makes it possible to list ingredients according to the family member's health status. Some or all of the above-mentioned processes in the shopping unit may be performed using or without the generation AI. For example, the shopping unit can input the family member's health status into the generation AI, which can then list appropriate ingredients.

[0079] The shopping unit can estimate the emotions of family members and prioritize ingredients based on the estimated emotions of the family members. For example, if a family member is feeling stressed, the shopping unit can prioritize purchasing ingredients that have a relaxing effect. For example, the shopping unit can prioritize purchasing chamomile tea and lavender. Also, if a family member is having fun, the shopping unit can prioritize purchasing ingredients for a party. For example, the shopping unit can prioritize purchasing cheese and crackers. Also, if a family member is tired, the shopping unit can prioritize purchasing ingredients that are easy to prepare. For example, the shopping unit can prioritize purchasing frozen foods and instant foods. This allows the prioritization of ingredients according to the emotions of the family members. The emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the shopping unit can be performed using the generative AI, or can be performed without using the generative AI. For example, the shopping team can input the family's emotions into the generation AI, which can then determine the appropriate priorities for ingredients.

[0080] The shopping unit can create a list based on the geographical availability of ingredients. For example, the shopping unit lists ingredients available at a local market. For example, the shopping unit lists fresh vegetables and fruits at a local market. The shopping unit can also create a list of ingredients harvested locally by season. For example, the shopping unit lists bamboo shoots and strawberries in spring and sweet potatoes and persimmons in autumn. The shopping unit can also create a list of ingredients produced in a nearby geographical location. For example, the shopping unit lists vegetables and fruits produced by local farms. This allows for a list that takes into account the geographical availability of ingredients. Some or all of the above-described processing in the shopping unit may be performed using or without the generation AI. For example, the shopping unit can input the geographical availability of ingredients into the generation AI, which then creates a list of appropriate ingredients.

[0081] The shopping unit can analyze the social media activity of the family and list related ingredients. For example, the shopping unit lists ingredients based on recipes shared by the family on social media. For example, the shopping unit lists ingredients needed for recipes shared by the family. The shopping unit can also list ingredients based on dishes that the family has "liked" on social media. For example, the shopping unit lists ingredients used in dishes that the family has "liked." The shopping unit can also list ingredients based on posts of cooking accounts that the family follows on social media. For example, the shopping unit lists ingredients used in posts of cooking accounts that the family follows. This makes it possible to list ingredients based on the social media activity of the family. Some or all of the above-mentioned processing in the shopping unit may be performed using or without the generation AI. For example, the shopping unit can input the social media activity of the family into the generation AI, which then lists appropriate ingredients.

[0082] The shopping department can customize the list-up method by reflecting past feedback. For example, the shopping department prioritizes the list of preferred ingredients based on feedback from family members. For example, the shopping department lists ingredients that family members like. The shopping department can also exclude ingredients that should be avoided from the list based on feedback from family members. For example, the shopping department excludes ingredients that family members dislike from the list. The shopping department can also periodically review the list-up method based on feedback from family members. For example, the shopping department periodically updates family member preferences and allergy information and reviews the list-up method. This makes it possible to customize the list-up method based on past feedback. Some or all of the above-described processing in the shopping department may be performed using or without the generation AI. For example, the shopping department can input feedback from family members into the generation AI, which can then customize an appropriate list-up method.

[0083] The management unit can estimate the emotions of family members and adjust the expiration date management method based on the estimated emotions of family members. For example, if a family member is feeling stressed, the management unit suggests that they prioritize consuming ingredients with upcoming expiration dates. For example, the management unit may list ingredients with upcoming expiration dates and encourage consumption. The management unit can also suggest dishes using fresh ingredients if the family member is enjoying themselves. For example, the management unit may suggest dishes using fresh vegetables and fruits. The management unit can also suggest that they prioritize consuming ingredients that are easy to prepare if the family member is tired. For example, the management unit may suggest that they prioritize consuming frozen foods and instant foods. This makes it possible to adjust the expiration date management method according to the emotions of the family members. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the management unit may be performed using the generative AI, or may be performed without using the generative AI. For example, the management department can input the family's emotions into the generation AI, which can then adjust the appropriate expiration date management method.

[0084] The management unit can learn how to store purchased ingredients and suggest storage methods. For example, the management unit suggests the optimal temperature and humidity for ingredients that require refrigeration. For example, the management unit suggests the temperature setting for a refrigerator. The management unit can also suggest the optimal storage period for ingredients that can be frozen. For example, the management unit suggests the frozen storage period. The management unit can also suggest the optimal storage location for ingredients that can be stored at room temperature. For example, the management unit suggests a location that is suitable for room temperature storage. This makes it possible to suggest the optimal storage method. Some or all of the above-mentioned processing in the management unit may be performed using or without the generation AI. For example, the management unit can input the storage method for purchased ingredients into the generation AI, which then suggests an appropriate storage method.

[0085] The management unit can determine the priority of expiration dates based on the consumption frequency of ingredients. The management unit, for example, suggests that frequently consumed ingredients be consumed preferentially. For example, the management unit lists frequently consumed ingredients and encourages their consumption. The management unit can also suggest that ingredients with an approaching expiration date be consumed preferentially. For example, the management unit lists ingredients with an approaching expiration date and encourages their consumption. The management unit can also suggest that storage methods be reviewed for ingredients that are consumed less frequently. For example, the management unit reviews storage methods for ingredients that are consumed less frequently. This makes it possible to determine the priority of expiration dates based on consumption frequency. Some or all of the above-described processing in the management unit may be performed using or without the generation AI. For example, the management unit can input the consumption frequency of ingredients into the generation AI, which can then determine appropriate expiration date priorities.

[0086] The management unit can manage expiration dates based on the nutritional value of ingredients. The management unit, for example, suggests that nutritious ingredients be consumed first. For example, the management unit can list ingredients rich in vitamins and minerals and encourage their consumption. The management unit can also suggest that ingredients whose nutritional value is likely to deteriorate be consumed early. For example, the management unit can list vegetables and fruits whose nutritional value is likely to deteriorate and encourage their consumption. The management unit can also manage expiration dates by taking nutritional balance into consideration. For example, the management unit can take into consideration the nutritional balance of a family and suggest that ingredients containing large amounts of specific nutrients be consumed first. This makes it possible to manage expiration dates by taking nutritional value into consideration. Some or all of the above-mentioned processing in the management unit may be performed using or without the generation AI. For example, the management unit can input the nutritional value of ingredients into the generation AI, which can then manage expiration dates appropriately.

[0087] The management unit can estimate the emotions of family members and adjust the display method of the expiration date based on the estimated emotions of the family members. For example, if a family member is feeling stressed, the management unit provides a simple, highly visible display method. For example, the management unit displays the expiration date in large letters to increase visibility. Furthermore, if a family member is having fun, the management unit can provide a colorful and fun display method. For example, the management unit displays the expiration date in a colorful design to create a fun atmosphere. Furthermore, if a family member is tired, the management unit can provide an easily understandable display method. For example, the management unit displays the expiration date with a simple icon to make it easy to understand. This makes it possible to adjust the display method of the expiration date according to the emotions of the family members. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the management unit can be performed using the generation AI, or can be performed without using the generation AI. For example, the management department can input the family's emotions into the generation AI, which can then adjust the way the expiration date is displayed appropriately.

[0088] The management unit can manage ingredients taking into account the geographical storage conditions of the ingredients. For example, the management unit suggests storage methods suitable for the local climate. For example, the management unit suggests refrigerated or frozen storage methods suited to the local climate. The management unit can also manage ingredients taking into account seasonal storage conditions. For example, the management unit recommends refrigerated storage in summer and room temperature storage in winter. The management unit can also suggest optimal storage methods for ingredients with geographically different storage conditions. For example, the management unit suggests storage methods for imported ingredients. This enables management taking into account geographical storage conditions. Some or all of the above-mentioned processing in the management unit may be performed using or without the generation AI. For example, the management unit can input the geographical storage conditions of ingredients into the generation AI, which then suggests an appropriate storage method.

[0089] The management unit can improve the accuracy of expiration date management by referring to related recipe information. The management unit, for example, suggests recipes using ingredients with upcoming expiration dates. For example, the management unit lists ingredients with upcoming expiration dates and suggests recipes using them. The management unit can also suggest recipes using ingredients with long expiration dates. For example, the management unit lists ingredients with long expiration dates and suggests recipes using them. The management unit can also suggest recipes that prioritize the consumption of ingredients with upcoming expiration dates. For example, the management unit suggests dishes that use ingredients with upcoming expiration dates and encourages consumption. This improves the accuracy of expiration date management by referring to recipe information. Some or all of the above-mentioned processes in the management unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the management unit can input related recipe information into the generation AI, which then suggests appropriate recipes.

[0090] The management unit can manage expiration dates based on the market price of the ingredients. The management unit, for example, suggests that ingredients with high market prices be consumed first. For example, the management unit can list ingredients with high market prices and encourage their consumption. The management unit can also suggest ways to extend the storage period of ingredients with low market prices. For example, the management unit can suggest storage methods for ingredients with low market prices. The management unit can also manage expiration dates taking market price fluctuations into account. For example, the management unit can register market price fluctuations in a database and manage expiration dates based on that. This makes it possible to manage expiration dates taking market prices into account. Some or all of the above-mentioned processing in the management unit may be performed using or without the generation AI. For example, the management unit can input the market price of ingredients into the generation AI, which can then manage expiration dates appropriately.

[0091] The menu planning unit can estimate the emotions of family members and adjust the menu suggestion method based on the estimated emotions of family members. For example, if a family member is feeling stressed, the menu planning unit can suggest dishes that have a relaxing effect. For example, the menu planning unit can suggest dishes using chamomile tea or lavender. Furthermore, if a family member is having fun, the menu planning unit can also suggest party dishes. For example, the menu planning unit can suggest dishes using cheese and crackers. Furthermore, if a family member is tired, the menu planning unit can also suggest dishes that are easy to prepare. For example, the menu planning unit can suggest dishes using frozen foods or instant foods. This makes it possible to adjust the menu suggestion method according to the emotions of family members. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the menu planning unit may be performed using the generative AI, or may be performed without using the generative AI. For example, the menu planning section can input the family's emotions into the generation AI, which can then adjust how it suggests appropriate menus.

[0092] The menu section can analyze the family's past meal history and suggest a menu. For example, the menu section can re-suggest dishes that were popular in the past. For example, the menu section can analyze the family's favorite dishes from the past meal history and suggest them. The menu section can also suggest seasonal dishes based on the past meal history. For example, the menu section can suggest cold dishes in the summer and hot dishes in the winter. This makes it possible to suggest an optimal menu based on the past meal history. Some or all of the above-mentioned processing in the menu section may be performed using a generation AI, or may be performed without using a generation AI. For example, the menu section can input the family's past meal history into the generation AI, which can then suggest an appropriate menu.

[0093] The menu section can customize menus according to the season or event. The menu section, for example, suggests dishes using seasonal ingredients for each season. For example, the menu section suggests dishes using bamboo shoots and strawberries in spring, and dishes using sweet potatoes and persimmons in autumn. The menu section can also suggest dishes suited to specific events (birthdays, Christmas, etc.). For example, the menu section suggests roast chicken and Christmas cake for Christmas. The menu section can also suggest special menus according to the season or event. For example, the menu section suggests yakisoba and takoyaki for a summer festival. This makes it possible to customize menus according to the season or event. Some or all of the above-mentioned processing in the menu section may be performed using a generation AI, or may be performed without using a generation AI. For example, the menu section can input information about the season or event into the generation AI, which then suggests an appropriate menu.

[0094] The menu section can learn the health status of family members and suggest menus that are rich in specific nutrients. For example, if a family member has high blood pressure, the menu section can suggest low-salt dishes. For example, the menu section can suggest dishes that use reduced-salt soy sauce or unsalted butter. The menu section can also suggest low-carb dishes if a family member has diabetes. For example, the menu section can suggest dishes that use low-carb bread or zero-carb drinks. The menu section can also suggest dishes that do not contain allergens if a family member has allergies. For example, the menu section can suggest dishes that use gluten-free pasta or dairy-free ice cream. This makes it possible to suggest menus that are rich in nutrients according to the family member's health status. Some or all of the above-mentioned processes in the menu section can be performed using or without the generation AI. For example, the menu section can input the health status of family members into the generation AI, which can then suggest an appropriate menu.

[0095] The menu planning unit can estimate the emotions of family members and determine menu priorities based on the estimated emotions of the family members. For example, if a family member is feeling stressed, the menu planning unit prioritizes dishes that have a relaxing effect. For example, the menu planning unit prioritizes dishes using chamomile tea or lavender. Also, if a family member is having fun, the menu planning unit can prioritize dishes suitable for a party. For example, the menu planning unit prioritizes dishes using cheese and crackers. Also, if a family member is tired, the menu planning unit can prioritize dishes that are easy to prepare. For example, the menu planning unit prioritizes dishes using frozen foods or instant foods. This makes it possible to determine menu priorities according to the emotions of the family members. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the menu planning unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the menu planning section can input the family's emotions into the generation AI, which can then determine appropriate menu priorities.

[0096] The menu unit can suggest menus based on the geographical availability of ingredients. The menu unit, for example, suggests dishes using ingredients available at a local market. For example, the menu unit suggests dishes using fresh vegetables and fruits at a local market. The menu unit can also suggest dishes using locally harvested ingredients each season. For example, the menu unit suggests dishes using bamboo shoots and strawberries in spring, and dishes using sweet potatoes and persimmons in autumn. The menu unit can also suggest dishes using ingredients produced in a geographically nearby location. For example, the menu unit suggests dishes using vegetables and fruits produced by local farms. This makes it possible to suggest menus that take into account the geographical availability of ingredients. Some or all of the above-mentioned processing in the menu unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the menu unit can input the geographical availability of ingredients into the generation AI, which then suggests an appropriate menu.

[0097] The menu section can analyze the social media activities of family members and suggest related menus. For example, the menu section can suggest menus based on recipes shared by family members on social media. For example, the menu section can suggest dishes using ingredients needed for recipes shared by family members. The menu section can also suggest menus based on dishes that family members have "liked" on social media. For example, the menu section can suggest dishes using ingredients used in dishes that family members have "liked." The menu section can also suggest menus based on posts from cooking accounts that family members follow on social media. For example, the menu section can suggest dishes using ingredients used in posts from cooking accounts that family members follow. This makes it possible to suggest menus based on the social media activities of family members. Some or all of the above-mentioned processing in the menu section can be performed using or without the generation AI. For example, the menu section can input the social media activities of family members into the generation AI, which can then suggest appropriate menus.

[0098] The menu section can customize the menu suggestion method by reflecting past feedback. For example, the menu section prioritizes suggesting preferred dishes based on feedback from family members. For example, the menu section suggests dishes that family members like. The menu section can also exclude dishes that should be avoided from the suggestions based on feedback from family members. For example, the menu section excludes dishes that family members dislike from the suggestions. The menu section can also periodically review the suggestion method based on feedback from family members. For example, the menu section periodically updates family member preferences and allergy information and reviews the suggestion method. This makes it possible to customize the menu suggestion method based on past feedback. Some or all of the above-mentioned processing in the menu section may be performed using or without the generation AI. For example, the menu section can input feedback from family members into the generation AI, which can then customize an appropriate suggestion method.

[0099] The re-shopping unit can estimate the emotions of the family members and adjust the re-shopping list based on the estimated emotions of the family members. For example, if the family members are feeling stressed, the re-shopping unit prioritizes ingredients with a relaxing effect on the list. For example, the re-shopping unit lists chamomile tea and lavender. If the family members are having fun, the re-shopping unit can also list ingredients suitable for a party. For example, the re-shopping unit lists cheese and crackers. If the family members are tired, the re-shopping unit can also list ingredients that are easy to prepare. For example, the re-shopping unit lists frozen foods and instant foods. This enables the re-shopping list to be tailored to the emotions of the family members. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the re-shopping unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the re-shopping department can input the family's emotions into the generation AI, which can then list appropriate ingredients.

[0100] The re-shopping unit can analyze the consumption status of ingredients purchased in the previous shopping trip and select a re-shopping method. The re-shopping unit, for example, lists necessary ingredients based on the consumption status of ingredients purchased in the previous shopping trip. For example, the re-shopping unit lists consumed ingredients and necessary ingredients. The re-shopping unit can also analyze the consumption frequency of ingredients purchased in the previous shopping trip and determine the priority of re-shopping. For example, the re-shopping unit prioritizes the list of ingredients consumed frequently. Furthermore, the re-shopping unit can plan a re-shopping trip to ensure efficient consumption based on the consumption status of ingredients purchased in the previous shopping trip. For example, the re-shopping unit reviews the storage method for ingredients consumed infrequently. This makes it possible to select an optimal re-shopping method based on the previous consumption status. Some or all of the above-mentioned processing in the re-shopping unit may be performed using or without the generation AI. For example, the re-shopping unit can input the consumption status of ingredients purchased in the previous shopping trip into the generation AI, which can select an appropriate re-shopping method.

[0101] The re-shopping unit can adjust the re-shopping list according to the season and weather. For example, in summer, the re-shopping unit lists ingredients suitable for cold dishes. For example, the re-shopping unit lists watermelon and tomatoes. The re-shopping unit can also list ingredients suitable for hot dishes in winter. For example, the re-shopping unit lists radishes and Chinese cabbage. The re-shopping unit can also list ingredients that can be stored during rainy weather. For example, the re-shopping unit lists canned goods and dried foods. This makes it possible to create a re-shopping list according to the season and weather. Some or all of the above-mentioned processes in the re-shopping unit may be performed using or without the generation AI. For example, the re-shopping unit can input information about the season and weather into the generation AI, which then lists appropriate ingredients.

[0102] The re-shopping unit can learn the health conditions of family members and add ingredients suitable for specific health conditions to the re-shopping list. For example, if a family member has high blood pressure, the re-shopping unit can list low-salt ingredients. For example, the re-shopping unit can list low-salt soy sauce and unsalted butter. The re-shopping unit can also list low-carb ingredients if a family member has diabetes. For example, the re-shopping unit can list low-carb bread and zero-carb drinks. The re-shopping unit can also list allergen-free ingredients if a family member has allergies. For example, the re-shopping unit can list gluten-free pasta and dairy-free ice cream. This makes it possible to create a re-shopping list based on the family member's health condition. Some or all of the above-described processing in the re-shopping unit may be performed using or without the generation AI. For example, the re-shopping unit can input the family member's health conditions into the generation AI, which can then list appropriate ingredients.

[0103] The re-shopping unit can estimate the emotions of the family members and determine re-shopping priorities based on the estimated emotions of the family members. For example, if the family members are feeling stressed, the re-shopping unit prioritizes purchasing ingredients that have a relaxing effect. For example, the re-shopping unit prioritizes purchasing chamomile tea and lavender. Also, if the family members are having fun, the re-shopping unit can prioritize purchasing ingredients for a party. For example, the re-shopping unit prioritizes purchasing cheese and crackers. Also, if the family members are tired, the re-shopping unit can prioritize purchasing ingredients that are easy to prepare. For example, the re-shopping unit prioritizes purchasing frozen foods and instant foods. This makes it possible to determine re-shopping priorities according to the emotions of the family members. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the re-shopping unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the re-shopping unit may input the family's emotions into the generation AI, which may then determine the priority of appropriate ingredients.

[0104] The re-shopping unit can perform re-shopping based on the geographical availability of ingredients. The re-shopping unit, for example, lists ingredients available at a local market. For example, the re-shopping unit lists fresh vegetables and fruits at a local market. The re-shopping unit can also list ingredients harvested locally by season. For example, the re-shopping unit lists bamboo shoots and strawberries in spring and sweet potatoes and persimmons in autumn. The re-shopping unit can also list ingredients produced in a geographically nearby location. For example, the re-shopping unit lists vegetables and fruits produced by local farms. This enables re-shopping that takes into account the geographical availability of ingredients. Some or all of the above-described processing in the re-shopping unit may be performed using or without the generation AI. For example, the re-shopping unit can input the geographical availability of ingredients into the generation AI, which then lists appropriate ingredients.

[0105] The re-shopping unit can analyze the social media activity of the family and add related ingredients to the re-shopping list. For example, the re-shopping unit lists ingredients based on recipes shared by the family on social media. For example, the re-shopping unit lists ingredients needed for recipes shared by the family. The re-shopping unit can also list ingredients based on dishes that the family has "liked" on social media. For example, the re-shopping unit lists ingredients used in dishes that the family has "liked." The re-shopping unit can also list ingredients based on posts of cooking accounts that the family follows on social media. For example, the re-shopping unit lists ingredients used in posts of cooking accounts that the family follows. This makes it possible to create a re-shopping list based on the social media activity of the family. Some or all of the above-described processing in the re-shopping unit may be performed using or without the generation AI. For example, the re-shopping unit can input the social media activity of the family into the generation AI, which then lists appropriate ingredients.

[0106] The re-shopping unit can customize the re-shopping method by reflecting past feedback. For example, the re-shopping unit prioritizes a list of preferred ingredients based on feedback from family members. For example, the re-shopping unit lists ingredients that family members like. The re-shopping unit can also exclude ingredients that should be avoided from the list based on feedback from family members. For example, the re-shopping unit excludes ingredients that family members dislike from the list. The re-shopping unit can also periodically review the list method based on feedback from family members. For example, the re-shopping unit periodically updates family member preferences and allergy information and reviews the list method. This makes it possible to customize the re-shopping method based on past feedback. Some or all of the above-described processing in the re-shopping unit may be performed using or without the generation AI. For example, the re-shopping unit can input feedback from family members into the generation AI, which can then customize an appropriate list method. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned shopping unit, management unit, menu unit, and re-shopping unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the shopping 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, the management 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, the menu 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, the re-shopping 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. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned shopping unit, management unit, menu unit, and re-shopping unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the shopping 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, the management 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, the menu 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, the re-shopping 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. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned shopping unit, management unit, menu unit, and re-shopping unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the shopping unit is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. For example, the management unit is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. For example, the menu unit is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. For example, the re-shopping unit is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned shopping unit, management unit, menu unit, and re-shopping unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the shopping 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, the management 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, the menu 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, the re-shopping unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12.

[0107] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0108] The shopping unit can estimate the emotions of family members and adjust the list of ingredients based on the estimated emotions of the family members. For example, if a family member is feeling stressed, it can prioritize ingredients with a relaxing effect on the list. For example, it can list chamomile tea and lavender. If a family member is having fun, it can list party ingredients. For example, it can list cheese and crackers. Furthermore, if a family member is tired, it can list ingredients that are easy to prepare. For example, it can list frozen foods and instant foods. This enables the list of ingredients to be tailored to the emotions of the family members. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the shopping unit can be performed using the generative AI, or can be performed without using the generative AI. For example, the shopping unit can input the emotions of the family members into the generative AI, which can then list appropriate ingredients.

[0109] The management unit can estimate the emotions of family members and adjust the expiration date management method based on the estimated emotions of the family members. For example, if a family member is feeling stressed, the management unit can suggest that food items with an approaching expiration date be prioritized. For example, the management unit can list food items with an approaching expiration date to encourage consumption. If a family member is enjoying themselves, the management unit can suggest dishes using fresh ingredients. For example, the management unit can suggest dishes using fresh vegetables and fruits. If a family member is tired, the management unit can suggest that food items that are easy to prepare be prioritized. For example, the management unit can suggest that frozen foods and instant foods be prioritized. This makes it possible to adjust the expiration date management method according to the emotions of the family members. The estimation of emotions is realized using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the management unit can be performed using the generative AI, or can be performed without the generative AI. For example, the management unit can input the emotions of family members into the generative AI, which can then adjust the expiration date management method appropriately.

[0110] The menu planning unit can estimate the emotions of family members and adjust the menu suggestion method based on the estimated emotions of the family members. For example, if a family member is feeling stressed, it can suggest dishes with a relaxing effect. For example, it can suggest dishes using chamomile tea or lavender. If a family member is having fun, it can also suggest party dishes. For example, it can suggest dishes using cheese and crackers. If a family member is tired, it can also suggest dishes that are easy to prepare. For example, it can suggest dishes using frozen foods or instant foods. This makes it possible to adjust the menu suggestion method according to the emotions of the family members. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the menu planning unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the menu planning unit can input the emotions of the family members into the generation AI, and the generation AI can adjust the menu suggestion method to suit the emotions of the family members.

[0111] The re-shopping unit can estimate the emotions of family members and adjust the re-shopping list based on the estimated emotions of the family members. For example, if a family member is feeling stressed, it can prioritize ingredients with a relaxing effect on the list. For example, it can list chamomile tea and lavender. If a family member is having fun, it can prioritize party ingredients on the list. For example, it can list cheese and crackers. If a family member is tired, it can prioritize easy-to-prepare ingredients on the list. For example, it can list frozen foods and instant foods. This enables the re-shopping list to be tailored to the emotions of the family members. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the re-shopping unit can be performed using the generative AI, or can be performed without using the generative AI. For example, the re-shopping unit can input the emotions of the family members into the generative AI, which can then list appropriate ingredients.

[0112] The re-shopping unit can estimate the emotions of the family members and determine re-shopping priorities based on the estimated emotions of the family members. For example, if the family members are feeling stressed, priority can be given to purchasing ingredients that have a relaxing effect. For example, priority can be given to purchasing chamomile tea and lavender. Also, if the family members are having fun, priority can be given to purchasing ingredients for a party. For example, priority can be given to purchasing cheese and crackers. Also, if the family members are tired, priority can be given to purchasing ingredients that are easy to prepare. For example, priority can be given to purchasing frozen foods and instant foods. This makes it possible to determine re-shopping priorities according to the emotions of the family members. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the re-shopping unit can be performed using the generation AI, or can be performed without using the generation AI. For example, the re-shopping unit can input the emotions of the family members into the generation AI, which can then determine the appropriate priorities for ingredients.

[0113] The purchasing unit can create a list based on the geographical availability of ingredients. For example, it can list ingredients available at a local market. For example, it can list fresh vegetables and fruits at a local market. It can also list ingredients harvested locally by season. For example, it can list bamboo shoots and strawberries in spring and sweet potatoes and persimmons in autumn. It can also list ingredients produced in a nearby geographical location. For example, it can list vegetables and fruits produced by local farms. This makes it possible to create a list that takes into account the geographical availability of ingredients. Some or all of the above-mentioned processing in the purchasing unit may be performed using or without the generation AI. For example, the purchasing unit can input the geographical availability of ingredients into the generation AI, which can then list appropriate ingredients.

[0114] The management unit can manage expiration dates based on the nutritional value of ingredients. For example, it can suggest that nutritious ingredients be consumed first. For example, it can list ingredients rich in vitamins and minerals and encourage their consumption. It can also suggest that ingredients whose nutritional value is likely to deteriorate be consumed early. For example, it can list vegetables and fruits whose nutritional value is likely to deteriorate and encourage their consumption. It can also manage expiration dates by taking nutritional balance into consideration. For example, it can take into consideration the nutritional balance of a family and suggest that ingredients containing large amounts of specific nutrients be consumed first. This makes it possible to manage expiration dates by taking nutritional value into consideration. Some or all of the above-mentioned processing in the management unit may be performed using or without the generation AI. For example, the management unit can input the nutritional value of ingredients into the generation AI, which can then manage expiration dates appropriately.

[0115] The menu section can analyze the family's past meal history and suggest menus. For example, it can suggest dishes that were popular in the past again. For example, it can analyze the family's favorite dishes from the past meal history and suggest them. It can also suggest seasonal dishes based on the past meal history. For example, it can suggest cold dishes in the summer and hot dishes in the winter. This makes it possible to suggest optimal menus based on the past meal history. Some or all of the above-mentioned processing in the menu section may be performed using a generation AI, or may be performed without using a generation AI. For example, the menu section can input the family's past meal history into the generation AI, which then suggests an appropriate menu.

[0116] The re-shopping unit can analyze the consumption status of ingredients purchased in the previous shopping trip and select a re-shopping method. For example, it can list necessary ingredients based on the consumption status of ingredients purchased in the previous shopping trip. For example, it can list consumed ingredients and then list necessary ingredients. It can also analyze the consumption frequency of ingredients purchased in the previous shopping trip and determine the priority of re-shopping. For example, it can prioritize and list ingredients that are consumed frequently. Furthermore, it can plan a re-shopping trip to ensure that ingredients are consumed without waste based on the consumption status of ingredients purchased in the previous shopping trip. For example, it can review the storage method for ingredients that are consumed infrequently. This makes it possible to select the optimal re-shopping method based on the previous consumption status. Some or all of the above-mentioned processing in the re-shopping unit can be performed using or without using the generation AI. For example, the consumption status of ingredients purchased in the previous shopping trip can be input into the generation AI, and the generation AI can select an appropriate re-shopping method.

[0117] The re-shopping unit can adjust the re-shopping list according to the season and weather. For example, in summer, it can list ingredients suitable for cold dishes, such as watermelon and tomatoes. It can also list ingredients suitable for hot dishes in winter, such as radishes and Chinese cabbage. It can also list ingredients that can be stored well in rainy weather, such as canned goods and dried foods. This makes it possible to create a re-shopping list according to the season and weather. Some or all of the above-mentioned processes in the re-shopping unit may be performed using or without the generation AI. For example, information about the season and weather can be input into the generation AI, which can then list appropriate ingredients.

[0118] The re-shopping unit can learn the health conditions of family members and add ingredients suitable for specific health conditions to the re-shopping list. For example, if a family member has high blood pressure, it can list low-salt ingredients. For example, it can list low-salt soy sauce and unsalted butter. Also, if a family member has diabetes, it can list low-carb ingredients. For example, it can list low-carb bread and zero-carb drinks. Also, if a family member has allergies, it can list ingredients that do not contain allergens. For example, it can list gluten-free pasta and dairy-free ice cream. This makes it possible to create a re-shopping list according to the family member's health conditions. Some or all of the above-mentioned processes in the re-shopping unit may be performed using or without the generation AI. For example, the re-shopping unit can input the health conditions of family members into the generation AI, which can then list appropriate ingredients.

[0119] The re-shopping unit can analyze the social media activity of the family and add related ingredients to the re-shopping list. For example, it can list ingredients based on recipes shared by the family on social media. For example, it can list ingredients needed for recipes shared by the family. It can also list ingredients based on dishes that the family has "liked" on social media. For example, it can list ingredients used in dishes that the family has "liked." It can also list ingredients based on posts of cooking accounts that the family follows on social media. For example, it can list ingredients used in posts of cooking accounts that the family follows. This makes it possible to create a re-shopping list based on the family's social media activity. Some or all of the above-mentioned processing in the re-shopping unit may be performed using or without the generation AI. For example, the re-shopping unit can input the family's social media activity into the generation AI, which can then list appropriate ingredients.

[0120] The re-shopping unit can customize the re-shopping method by reflecting past feedback. For example, based on feedback from family members, it can prioritize and list preferred ingredients. For example, it can list ingredients that family members like. Also, based on feedback from family members, it can exclude ingredients that should be avoided from the list. For example, it can exclude ingredients that family members dislike from the list. Also, it can periodically review the list method based on feedback from family members. For example, it can periodically update family members' preferences and allergy information and review the list method. This makes it possible to customize the re-shopping method based on past feedback. Some or all of the above-mentioned processes in the re-shopping unit may be performed using or without using the generation AI. For example, the re-shopping unit can input feedback from family members into the generation AI, which can then customize an appropriate list method.

[0121] The processing flow of the second embodiment will be briefly explained below.

[0122] Step 1: The shopping section purchases ingredients. For example, ingredients can be purchased online or in-store. It is also possible to create a list of ingredients taking into account family preferences and allergy information. It is possible to learn family preferences and prioritize the purchase of specific ingredients, or to create a list of allergen-free ingredients based on allergy information. Step 2: The management department manages the expiration dates of ingredients purchased by the purchasing department. For example, it registers the expiration dates of purchased ingredients in a database and notifies customers of ingredients that are approaching their expiration date. It can also plan food consumption taking into account family schedules. It keeps track of family schedules and suggests easy-to-cook menus for busy days. Step 3: The menu department creates a menu based on the information managed by the management department. For example, it can propose a healthy menu that takes into account the nutritional balance of the family. It can learn about the family's health status and suggest menus that use ingredients that are rich in specific nutrients. Step 4: The re-shopping department goes out to buy ingredients again based on the menu devised by the menu planning department. For example, they check the consumption status of ingredients purchased in the previous shopping trip and make a list of the ingredients that will be needed.

[0123] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0124] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0125] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0126] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

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

[0128] 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.

[0129] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0130] 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.

[0131] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0132] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0133] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0134] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0135] 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.

[0136] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0137] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0138] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0139] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0140] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0141] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0142] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

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

[0144] 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.

[0145] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0146] 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.

[0147] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0148] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0149] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0150] 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.

[0151] 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.

[0152] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0153] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0154] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0155] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0156] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0157] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0158] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

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

[0160] 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.

[0161] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0162] 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.

[0163] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0164] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0165] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0166] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0167] 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.

[0168] 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.

[0169] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0170] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0171] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0172] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0173] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0174] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0175] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0176] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0177] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0178] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0179] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0180] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0181] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0182] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0183] 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.

[0184] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0185] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0186] 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.

[0187] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0188] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0189] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0190] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0191] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0192] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0193] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0194] [Explanation of symbols]

[0195] 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 grocery shopping department that buys ingredients, a management unit that manages the expiration dates of ingredients purchased by the purchasing unit; A menu section that designs menus based on the information managed by the management section; a re-purchasing unit that purchases ingredients again based on the menu devised by the menu unit; Equipped with A system characterized by:

2. The purchasing section includes: Create an ingredient list based on your family's preferences and allergies The system of claim 1 .

3. The management unit Manage expiration dates on purchased ingredients and plan your consumption so that nothing goes to waste. The system of claim 1 .

4. The menu section includes: Proposing healthy menus based on the nutritional balance of the family The system of claim 1 .

5. The repurchasing unit Check the consumption status of ingredients purchased on the previous shopping trip and make a list of ingredients you need. The system of claim 1 .

6. The purchasing section includes: Estimate family members' emotions and adjust the list of ingredients based on the estimated family members' emotions The system of claim 1 .

7. The purchasing section includes: Analyze past purchase history and select a method for listing ingredients The system of claim 1 .

8. The purchasing section includes: Adjust your ingredient list depending on the season and weather. The system of claim 1 .

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