System

Abstract

An object of a system according to an embodiment is to automatically create a daily menu by utilizing information on food ingredients in a refrigerator and shopping receipts to reduce waste of food ingredients.SOLUTION: A system includes a food material data generation part, a menu creation part, and a waste reduction part. The food ingredient data generation unit converts information on the food ingredients in the refrigerator and the shopping receipt into data. A menu preparation part automatically prepares a daily menu on the basis of the information of the food materials in the refrigerator and the shopping receipt converted into data by the food material data conversion part. A waste reduction part proposes a recipe for reducing the waste of food materials on the basis of the menu prepared by the menu preparation 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] Conventional technology has had the problem of not effectively utilizing information from ingredients in the refrigerator and shopping receipts to create daily menus and reduce food waste.

[0005] The system of the embodiment aims to reduce food waste by automatically creating daily menus using information on ingredients in the refrigerator and shopping receipts. [Means for solving the problem]

[0006] The system according to the embodiment includes an ingredient data conversion unit, a menu creation unit, and a waste reduction unit. The ingredient data conversion unit converts information about ingredients in the refrigerator and shopping receipts into data. The menu creation unit automatically creates a daily menu based on the information about ingredients in the refrigerator and shopping receipts that has been converted into data by the ingredient data conversion unit. The waste reduction unit suggests recipes that reduce ingredient waste based on the menu created by the menu creation unit. [Effects of the Invention]

[0007] The system according to the embodiment utilizes information on ingredients in the refrigerator and shopping receipts to automatically create daily menus, reducing food waste. [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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the 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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

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

[0028] (Example 1) The automatic menu planning system according to an embodiment of the present invention uses data on ingredients in the refrigerator and shopping receipts to automatically create daily menus using AI. This allows the automatic menu planning system to reduce food waste and prepare meals efficiently.

[0029] An automatic menu creation system according to an embodiment includes an ingredient data conversion unit, a menu creation unit, and a waste reduction unit. The ingredient data conversion unit digitizes information about ingredients in the refrigerator and shopping receipts. For example, it uses scanning technology to create a list of ingredients in the refrigerator and inputs information from the shopping receipts (such as the amount of meat packaged and the purchase date). This allows the current inventory status of ingredients to be ascertained. The menu creation unit automatically creates daily menus based on the information about ingredients in the refrigerator and shopping receipts digitized by the ingredient data conversion unit. For example, the generation AI generates an optimal menu based on ingredient information and prompts regarding the number of people and preferences. The waste reduction unit suggests recipes that reduce ingredient waste based on the menu created by the menu creation unit. For example, the recipes suggested by the generation AI are designed to use up all ingredients in the refrigerator without waste. This allows the automatic menu creation system according to an embodiment to reduce ingredient waste and prepare meals efficiently.

[0030] The food data conversion unit can scan food items in the refrigerator and automatically determine the freshness and expiration date of the food items using the generation AI and digitize the information. For example, when the food data conversion unit scans food items in the refrigerator, the generation AI determines the freshness of the food items. For example, it analyzes images taken with a camera and evaluates the freshness from the color and shape of the vegetables. The food data conversion unit also uses the generation AI to determine the expiration date of the food items. For example, it analyzes the information printed on the package and automatically digitizes the expiration date. This allows the freshness and expiration date of food items to be automatically determined and digitized.

[0031] When receipt information is input, the generating AI performs character recognition on the receipt, allowing it to automatically obtain detailed information about the purchased items. For example, to perform character recognition on a shopping receipt, the generating AI uses OCR technology to digitize the contents of the receipt. For example, it scans the image of the receipt and extracts the text information. The generating AI also automatically obtains detailed information about the purchased items. For example, it analyzes the origin and nutritional content from the receipt contents and digitizes them. This allows it to perform character recognition on the receipt and automatically obtain detailed information about the purchased items.

[0032] The food ingredient data conversion unit can link food ingredient data in the refrigerator with a smartphone app, allowing users to check inventory status even when they are out. For example, the food ingredient data conversion unit synchronizes data using a cloud server to link food ingredient data in the refrigerator with the smartphone app. For example, the data in the refrigerator can be uploaded to the cloud in real time and made accessible from the app. The food ingredient data conversion unit also allows users to check inventory status even when they are out using the smartphone app. For example, the app displays a list of food ingredients in the refrigerator and updates the inventory status in real time. This allows users to check inventory status in the refrigerator even when they are out.

[0033] The food ingredient digitization unit can simplify the digitization of shopping receipts by using voice input or barcode scanning. For example, to digitize shopping receipts by voice input, the generation AI uses voice recognition technology to input the contents of the receipt. For example, when a user reads the contents of the receipt aloud, the AI ​​converts the contents into text. The food ingredient digitization unit also digitizes receipts by using barcode scanning. For example, it uses a barcode reader or smartphone camera to scan the barcode on the receipt and obtain the data. This simplifies the digitization of shopping receipts.

[0034] The menu creation unit can use the generation AI to provide the optimal nutritional balance by taking into account the user's past eating history and health condition. For example, the menu creation unit stores the user's past eating history in a database, and the generation AI proposes the optimal menu based on that data. For example, the nutritional balance is adjusted based on information about dishes and ingredients eaten in the past. The menu creation unit also uses the generation AI to propose menus taking into account the user's health condition. For example, it proposes healthy recipes based on weight and blood pressure data. This allows the user's past eating history and health condition to be considered and the optimal nutritional balance to be provided.

[0035] The menu creation unit uses generation AI to suggest recipes that suit the season and weather, making it possible to make use of ingredients that are in season. For example, in order to suggest recipes that suit the season and weather, the generation AI analyzes weather data and suggests menus that make use of seasonal ingredients. For example, it suggests cold dishes in the summer and hot dishes in the winter. The menu creation unit also suggests recipes that take into account the optimal harvest time and nutritional value of ingredients for each season. For example, it suggests dishes using fresh vegetables in the spring and desserts using just-harvested fruits in the fall. This makes it possible to suggest recipes that suit the season and weather, making use of ingredients that are in season.

[0036] The menu creation unit can customize menus taking into account the preferences and allergy information of all family members. For example, the menu creation unit stores the preferences and allergy information of all family members in a database, and the generation AI customizes menus based on that data. For example, it suggests recipes that avoid specific ingredients. The menu creation unit also allows the generation AI to suggest menus taking into account the preferences of all family members. For example, it suggests recipes that take into account dishes that children like and seasonings that adults prefer. This makes it possible to customize menus taking into account the preferences and allergy information of all family members.

[0037] The menu creation unit can optimize menus to suit specific diet plans and fitness goals. In the menu creation unit, for example, the generation AI analyzes diet data and suggests optimal recipes to suggest menus that suit a specific diet plan. For example, it suggests low-calorie and high-protein dishes. In addition, the generation AI in the menu creation unit suggests menus that suit fitness goals. For example, it suggests high-protein dishes for a user who is aiming to increase muscle strength, and low-calorie dishes for a user who is aiming to reduce body fat. This makes it possible to optimize menus to suit specific diet plans and fitness goals.

[0038] The waste reduction unit can use the generative AI to minimize food waste, including food storage and cooking methods. The waste reduction unit minimizes food waste, for example, by including food storage methods in recipes suggested by the generative AI. For example, it suggests methods for storing vegetables and freezing meat. The waste reduction unit also includes food cooking methods in recipes suggested by the generative AI. For example, it suggests cooking methods to use up ingredients without waste. This makes it possible to minimize food waste, including food storage and cooking methods.

[0039] The waste reduction unit uses the generative AI to suggest substitutes for ingredients and provide flexible recipes. For example, the waste reduction unit builds a system in which the generative AI suggests substitutes for ingredients. For example, if a specific ingredient is unavailable, the system suggests a substitute. The waste reduction unit also uses the generative AI to provide flexible recipes. For example, the system suggests recipes that use substitutes for ingredients. This makes it possible to suggest substitutes for ingredients and provide flexible recipes.

[0040] The waste reduction unit uses the generation AI to suggest recipes that use up ingredients and can provide menu plans on a weekly basis. For example, the waste reduction unit builds a system in which the generation AI suggests recipes that use up ingredients. For example, it suggests recipes that use up all the ingredients in the refrigerator. The waste reduction unit also uses the generation AI to provide menu plans on a weekly basis. For example, it creates a weekly ingredient purchasing list and suggests menus that use up all the ingredients without waste. This makes it possible to suggest recipes that use up ingredients and provide menu plans on a weekly basis.

[0041] The waste reduction unit can use the generation AI to predict the shelf life of ingredients and send reminders to use them up early. For example, the waste reduction unit builds a system in which the generation AI predicts the shelf life of ingredients. For example, the shelf life is predicted based on the type of ingredient and the storage method. The waste reduction unit also sends reminders to use ingredients up early. For example, the generation AI sends a notification for ingredients whose shelf life is approaching. This makes it possible to predict the shelf life of ingredients and send reminders to use them up early.

[0042] The waste reduction unit can use the generation AI to automatically determine the type of food waste and suggest the optimal composting method. For example, the waste reduction unit constructs a system in which the generation AI automatically determines the type of food waste. For example, it analyzes images taken with a camera and identifies the type of food waste. The waste reduction unit also uses the generation AI to suggest the optimal composting method. For example, it suggests a composting method depending on the type of food waste. This makes it possible to automatically determine the type of food waste and suggest the optimal composting method.

[0043] The waste reduction unit uses the generation AI to monitor the progress of the compost and can suggest maintenance or additional materials at the appropriate time. For example, the waste reduction unit builds a system in which the generation AI monitors the progress of the compost. For example, sensors are used to measure temperature and humidity to understand the progress. The waste reduction unit also uses the generation AI to suggest maintenance or additional materials at the appropriate time. For example, it notifies the system of the timing of stirring and humidity adjustment. This allows the progress of the compost to be monitored and can suggest maintenance or additional materials at the appropriate time.

[0044] The waste reduction unit can use the generation AI to provide a guide for using composted soil in home gardens and for houseplants. For example, the waste reduction unit constructs a system in which the generation AI provides a guide for using composted soil in home gardens and for houseplants. For example, it suggests which soil is suitable for which plants. The waste reduction unit also suggests ways to use the composted soil produced by the generation AI. For example, it suggests ways to improve the soil in home gardens and provide nutrients to houseplants. This makes it possible to provide a guide for using composted soil in home gardens and for houseplants.

[0045] The waste reduction department can use the generative AI to propose a program for working with the local community to compost food waste in collaboration with the local community. For example, the waste reduction department can build a system to propose a program for working with the local community to compost food waste. For example, it can plan an event where local residents can compost together. The waste reduction department can also use the generative AI to propose a program in collaboration with the local community. For example, it can propose a schedule for joint work and the division of roles. This makes it possible to propose a program for working with the local community to compost food waste in collaboration with the local community.

[0046] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0047] The automatic menu creation system can further include a nutritional analysis unit that analyzes the nutritional value of ingredients. The nutritional analysis unit, for example, retrieves the nutritional components of ingredients from a database and calculates the nutritional value of each ingredient. This allows the user to create a nutritionally balanced menu. The nutritional analysis unit can also preferentially suggest ingredients that are rich in specific nutrients. For example, if a user is deficient in vitamin C, it will suggest recipes using ingredients that are rich in vitamin C. This makes it possible to provide a nutritionally balanced meal that suits the user's health condition.

[0048] The automatic menu creation system can further include a preference learning unit that learns the user's food preferences. The preference learning unit, for example, collects data on menus and ingredients selected by the user in the past and analyzes the user's preferences. This makes it possible to preferentially suggest ingredients and dishes that the user prefers. The preference learning unit can also suggest new recipes based on the user's preferences. For example, if the user likes a particular dish, the system can suggest new recipes similar to that dish. This makes it possible to provide menus that match the user's preferences.

[0049] The automatic menu creation system can further include a storage suggestion unit that suggests how to store ingredients. The storage suggestion unit suggests the optimal storage method depending on the type and condition of the ingredients, for example. This allows ingredients to stay fresh for a long time. The storage suggestion unit can also predict the shelf life of ingredients and send reminders to use them up early. For example, it can send a notification when ingredients are nearing the end of their shelf life. This reduces food waste and allows for efficient meal preparation.

[0050] The automatic menu creation system may further include a health monitoring unit that monitors the user's health condition and proposes a menu based on the health condition. The health monitoring unit collects data such as the user's weight, blood pressure, and blood sugar level, and analyzes the health condition. This makes it possible to propose an optimal menu based on the user's health condition. The health monitoring unit may also propose a menu tailored to a specific health goal. For example, it may propose low-calorie dishes to a user on a diet, and high-protein dishes to a user aiming to build muscle. This makes it possible to provide a menu tailored to the user's health condition.

[0051] The automatic menu creation system can further include a preference learning unit that learns the user's food preferences and suggests new recipes based on the preferences. The preference learning unit, for example, collects data on menus and ingredients selected by the user in the past and analyzes the user's preferences. This makes it possible to preferentially suggest ingredients and dishes that the user likes. The preference learning unit can also suggest new recipes based on the user's preferences. For example, if the user likes a particular dish, the system can suggest new recipes similar to that dish. This makes it possible to provide new menus tailored to the user's preferences.

[0052] The automatic menu creation system can further include a health monitoring unit that monitors the user's health condition and suggests how to select ingredients based on the health condition. The health monitoring unit collects data such as the user's weight, blood pressure, and blood sugar level, and analyzes the health condition. This makes it possible to suggest ingredients that are optimal for the user's health condition. The health monitoring unit can also suggest how to select ingredients that are tailored to a specific health goal. For example, it can suggest low-calorie ingredients to a user on a diet, and high-protein ingredients to a user who is trying to build muscle. This makes it possible to provide a way to select ingredients that is tailored to the user's health condition.

[0053] The processing flow of the first embodiment will be briefly explained below.

[0054] Step 1: The food data conversion unit converts information about the food in the refrigerator and shopping receipts into data. For example, it uses scanning technology to create a list of the food in the refrigerator and inputs information from the shopping receipt (such as the amount of meat packed and the purchase date). This allows the current food inventory status to be determined. Step 2: The menu creation unit automatically creates daily menus based on the information on ingredients in the refrigerator and shopping receipts digitized by the ingredient data conversion unit. For example, the generation AI generates optimal menus based on ingredient information and prompts regarding the number of people and preferences. Step 3: The waste reduction unit proposes recipes that reduce food waste based on the menu created by the menu creation unit. For example, the recipes proposed by the generation AI are designed to use up all the ingredients in the refrigerator without waste.

[0055] (Example 2) The automatic menu planning system according to an embodiment of the present invention uses data on ingredients in the refrigerator and shopping receipts to automatically create daily menus using AI. This allows the automatic menu planning system to reduce food waste and prepare meals efficiently.

[0056] An automatic menu creation system according to an embodiment includes an ingredient data conversion unit, a menu creation unit, and a waste reduction unit. The ingredient data conversion unit digitizes information about ingredients in the refrigerator and shopping receipts. For example, it uses scanning technology to create a list of ingredients in the refrigerator and inputs information from the shopping receipts (such as the amount of meat packaged and the purchase date). This allows the current inventory status of ingredients to be ascertained. The menu creation unit automatically creates daily menus based on the information about ingredients in the refrigerator and shopping receipts digitized by the ingredient data conversion unit. For example, the generation AI generates an optimal menu based on ingredient information and prompts regarding the number of people and preferences. The waste reduction unit suggests recipes that reduce ingredient waste based on the menu created by the menu creation unit. For example, the recipes suggested by the generation AI are designed to use up all ingredients in the refrigerator without waste. This allows the automatic menu creation system according to an embodiment to reduce ingredient waste and prepare meals efficiently.

[0057] The food data conversion unit can scan food items in the refrigerator and automatically determine the freshness and expiration date of the food items using the generation AI and digitize the information. For example, when the food data conversion unit scans food items in the refrigerator, the generation AI determines the freshness of the food items. For example, it analyzes images taken with a camera and evaluates the freshness from the color and shape of the vegetables. The food data conversion unit also uses the generation AI to determine the expiration date of the food items. For example, it analyzes the information printed on the package and automatically digitizes the expiration date. This allows the freshness and expiration date of food items to be automatically determined and digitized.

[0058] When receipt information is input, the generating AI performs character recognition on the receipt, allowing it to automatically obtain detailed information about the purchased items. For example, to perform character recognition on a shopping receipt, the generating AI uses OCR technology to digitize the contents of the receipt. For example, it scans the image of the receipt and extracts the text information. The generating AI also automatically obtains detailed information about the purchased items. For example, it analyzes the origin and nutritional content from the receipt contents and digitizes them. This allows it to perform character recognition on the receipt and automatically obtain detailed information about the purchased items.

[0059] The ingredient data conversion unit uses the emotion estimation function to analyze the user's emotions toward the ingredients they have purchased and can prioritize the conversion of their favorite ingredients into data. For example, to analyze the user's emotions toward the ingredients they have purchased, the generation AI analyzes the user's facial expressions and voice. For example, the generation AI detects the user's emotions in real time using a camera or microphone. The ingredient data conversion unit also prioritizes the conversion of their favorite ingredients into data based on the user's emotions. For example, the generation AI displays the user's favorite ingredients at the top of the list. This allows the user's favorite ingredients to be prioritized into data.

[0060] The food ingredient data conversion unit can link food ingredient data in the refrigerator with a smartphone app, allowing users to check inventory status even when they are out. For example, the food ingredient data conversion unit synchronizes data using a cloud server to link food ingredient data in the refrigerator with the smartphone app. For example, the data in the refrigerator can be uploaded to the cloud in real time and made accessible from the app. The food ingredient data conversion unit also allows users to check inventory status even when they are out using the smartphone app. For example, the app displays a list of food ingredients in the refrigerator and updates the inventory status in real time. This allows users to check inventory status in the refrigerator even when they are out.

[0061] The food ingredient digitization unit can simplify the digitization of shopping receipts by using voice input or barcode scanning. For example, to digitize shopping receipts by voice input, the generation AI uses voice recognition technology to input the contents of the receipt. For example, when a user reads the contents of the receipt aloud, the AI ​​converts the contents into text. The food ingredient digitization unit also digitizes receipts by using barcode scanning. For example, it uses a barcode reader or smartphone camera to scan the barcode on the receipt and obtain the data. This simplifies the digitization of shopping receipts.

[0062] The ingredient data conversion unit uses the emotion estimation function to analyze the emotion a user feels when scanning ingredients in real time and make suggestions that elicit positive emotions. For example, to analyze the emotion a user feels when scanning ingredients in real time, the ingredient data conversion unit uses a generation AI to analyze the user's facial expressions and voice using a camera or microphone. For example, it detects whether the user is smiling. The ingredient data conversion unit also uses the generation AI to make suggestions that elicit positive emotions based on the user's emotions. For example, it suggests ingredients that the user likes. This allows the ingredient data conversion unit to make suggestions that elicit positive emotions when the user scans ingredients.

[0063] The menu creation unit can use the generation AI to provide the optimal nutritional balance by taking into account the user's past eating history and health condition. For example, the menu creation unit stores the user's past eating history in a database, and the generation AI proposes the optimal menu based on that data. For example, the nutritional balance is adjusted based on information about dishes and ingredients eaten in the past. The menu creation unit also uses the generation AI to propose menus taking into account the user's health condition. For example, it proposes healthy recipes based on weight and blood pressure data. This allows the user's past eating history and health condition to be considered and the optimal nutritional balance to be provided.

[0064] The menu creation unit uses generation AI to suggest recipes that suit the season and weather, making it possible to make use of ingredients that are in season. For example, in order to suggest recipes that suit the season and weather, the generation AI analyzes weather data and suggests menus that make use of seasonal ingredients. For example, it suggests cold dishes in the summer and hot dishes in the winter. The menu creation unit also suggests recipes that take into account the optimal harvest time and nutritional value of ingredients for each season. For example, it suggests dishes using fresh vegetables in the spring and desserts using just-harvested fruits in the fall. This makes it possible to suggest recipes that suit the season and weather, making use of ingredients that are in season.

[0065] The menu creation unit can use the emotion estimation function to suggest a menu that matches the user's current mood. For example, the menu creation unit builds a system that analyzes the user's current mood using the emotion estimation function and suggests a menu based on the results. For example, when the user wants to relax, it suggests lighter dishes. In addition, the menu creation unit uses a generation AI to suggest a menu based on the user's emotions. For example, when the user is feeling stressed, it suggests dishes that use ingredients that have a relaxing effect. This makes it possible to suggest a menu that matches the user's current mood.

[0066] The menu creation unit can customize menus taking into account the preferences and allergy information of all family members. For example, the menu creation unit stores the preferences and allergy information of all family members in a database, and the generation AI customizes menus based on that data. For example, it suggests recipes that avoid specific ingredients. The menu creation unit also allows the generation AI to suggest menus taking into account the preferences of all family members. For example, it suggests recipes that take into account dishes that children like and seasonings that adults prefer. This makes it possible to customize menus taking into account the preferences and allergy information of all family members.

[0067] The menu creation unit can optimize menus to suit specific diet plans and fitness goals. In the menu creation unit, for example, the generation AI analyzes diet data and suggests optimal recipes to suggest menus that suit a specific diet plan. For example, it suggests low-calorie and high-protein dishes. In addition, the generation AI in the menu creation unit suggests menus that suit fitness goals. For example, it suggests high-protein dishes for a user who is aiming to increase muscle strength, and low-calorie dishes for a user who is aiming to reduce body fat. This makes it possible to optimize menus to suit specific diet plans and fitness goals.

[0068] The menu creation unit uses the emotion estimation function to analyze the emotions of the user when selecting a menu in real time and make suggestions that elicit positive emotions. For example, in order to analyze the emotions of the user when selecting a menu in real time, the menu creation unit uses the generation AI to analyze the user's facial expressions and voice using a camera and microphone. For example, it detects whether the user is smiling. Furthermore, the menu creation unit uses the generation AI to make suggestions that elicit positive emotions based on the user's emotions. For example, it suggests dishes that the user likes. This makes it possible to make suggestions that elicit positive emotions when the user is selecting a menu.

[0069] The waste reduction unit can use the generative AI to minimize food waste, including food storage and cooking methods. The waste reduction unit minimizes food waste, for example, by including food storage methods in recipes suggested by the generative AI. For example, it suggests methods for storing vegetables and freezing meat. The waste reduction unit also includes food cooking methods in recipes suggested by the generative AI. For example, it suggests cooking methods to use up ingredients without waste. This makes it possible to minimize food waste, including food storage and cooking methods.

[0070] The waste reduction unit uses the generative AI to suggest substitutes for ingredients and provide flexible recipes. For example, the waste reduction unit builds a system in which the generative AI suggests substitutes for ingredients. For example, if a specific ingredient is unavailable, the system suggests a substitute. The waste reduction unit also uses the generative AI to provide flexible recipes. For example, the system suggests recipes that use substitutes for ingredients. This makes it possible to suggest substitutes for ingredients and provide flexible recipes.

[0071] The waste reduction unit can use the emotion estimation function to make suggestions to increase the user's motivation to avoid wasting food ingredients. For example, the waste reduction unit uses the emotion estimation function to build a system that makes suggestions to increase the user's motivation to avoid wasting food ingredients. For example, it displays a message that makes the user feel positive. Furthermore, the waste reduction unit uses a generation AI to make suggestions to increase motivation based on the user's emotions. For example, it emphasizes the benefits that can be obtained by not wasting food ingredients. This makes it possible to make suggestions to increase the user's motivation to avoid wasting food ingredients.

[0072] The waste reduction unit uses the generation AI to suggest recipes that use up ingredients and can provide menu plans on a weekly basis. For example, the waste reduction unit builds a system in which the generation AI suggests recipes that use up ingredients. For example, it suggests recipes that use up all the ingredients in the refrigerator. The waste reduction unit also uses the generation AI to provide menu plans on a weekly basis. For example, it creates a weekly ingredient purchasing list and suggests menus that use up all the ingredients without waste. This makes it possible to suggest recipes that use up ingredients and provide menu plans on a weekly basis.

[0073] The waste reduction unit can use the generation AI to predict the shelf life of ingredients and send reminders to use them up early. For example, the waste reduction unit builds a system in which the generation AI predicts the shelf life of ingredients. For example, the shelf life is predicted based on the type of ingredient and the storage method. The waste reduction unit also sends reminders to use ingredients up early. For example, the generation AI sends a notification for ingredients whose shelf life is approaching. This makes it possible to predict the shelf life of ingredients and send reminders to use them up early.

[0074] The waste reduction unit can use the emotion estimation function to provide emotional support to help the user not waste food. For example, the waste reduction unit uses the emotion estimation function to build a system that provides emotional support to help the user not waste food. For example, the waste reduction unit displays a message that makes the user feel positive. The waste reduction unit also provides emotional support based on the user's emotions using the generation AI. For example, the waste reduction unit emphasizes the benefits that can be obtained by not wasting food. This makes it possible to provide emotional support to help the user not waste food.

[0075] The waste reduction unit can use the generation AI to automatically determine the type of food waste and suggest the optimal composting method. For example, the waste reduction unit constructs a system in which the generation AI automatically determines the type of food waste. For example, it analyzes images taken with a camera and identifies the type of food waste. The waste reduction unit also uses the generation AI to suggest the optimal composting method. For example, it suggests a composting method depending on the type of food waste. This makes it possible to automatically determine the type of food waste and suggest the optimal composting method.

[0076] The waste reduction unit uses the generation AI to monitor the progress of the compost and can suggest maintenance or additional materials at the appropriate time. For example, the waste reduction unit builds a system in which the generation AI monitors the progress of the compost. For example, sensors are used to measure temperature and humidity to understand the progress. The waste reduction unit also uses the generation AI to suggest maintenance or additional materials at the appropriate time. For example, it notifies the system of the timing of stirring and humidity adjustment. This allows the progress of the compost to be monitored and can suggest maintenance or additional materials at the appropriate time.

[0077] The waste reduction unit can use the emotion estimation function to make suggestions to help the user enjoy composting. For example, the waste reduction unit uses the emotion estimation function to build a system that makes suggestions to help the user enjoy composting. For example, it displays a message that makes the user feel positive emotions. The waste reduction unit also makes enjoyable suggestions based on the user's emotions using a generation AI. For example, it makes a suggestion to turn composting into a game. This makes it possible to make suggestions to help the user enjoy composting.

[0078] The waste reduction unit can use the generation AI to provide a guide for using composted soil in home gardens and for houseplants. For example, the waste reduction unit constructs a system in which the generation AI provides a guide for using composted soil in home gardens and for houseplants. For example, it suggests which soil is suitable for which plants. The waste reduction unit also suggests ways to use the composted soil produced by the generation AI. For example, it suggests ways to improve the soil in home gardens and provide nutrients to houseplants. This makes it possible to provide a guide for using composted soil in home gardens and for houseplants.

[0079] The waste reduction department can use the generative AI to propose a program for working with the local community to compost food waste in collaboration with the local community. For example, the waste reduction department can build a system to propose a program for working with the local community to compost food waste. For example, it can plan an event where local residents can compost together. The waste reduction department can also use the generative AI to propose a program in collaboration with the local community. For example, it can propose a schedule for joint work and the division of roles. This makes it possible to propose a program for working with the local community to compost food waste in collaboration with the local community.

[0080] The waste reduction unit uses the emotion estimation function to analyze the emotions of the user when composting in real time and make suggestions that will elicit positive emotions. The waste reduction unit, for example, uses the emotion estimation function to build a system that analyzes the emotions of the user when composting in real time. For example, it analyzes the user's facial expressions and voice using a camera or microphone. The waste reduction unit also uses a generative AI to make suggestions that will elicit positive emotions based on the user's emotions. For example, it detects whether the user is smiling and displays a positive message. This makes it possible to make suggestions that will elicit positive emotions when the user is composting.

[0081] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0082] The automatic menu creation system can further include a nutritional analysis unit that analyzes the nutritional value of ingredients. The nutritional analysis unit, for example, retrieves the nutritional components of ingredients from a database and calculates the nutritional value of each ingredient. This allows the user to create a nutritionally balanced menu. The nutritional analysis unit can also preferentially suggest ingredients that are rich in specific nutrients. For example, if a user is deficient in vitamin C, it will suggest recipes using ingredients that are rich in vitamin C. This makes it possible to provide a nutritionally balanced meal that suits the user's health condition.

[0083] The automatic menu creation system can further include a preference learning unit that learns the user's food preferences. The preference learning unit, for example, collects data on menus and ingredients selected by the user in the past and analyzes the user's preferences. This makes it possible to preferentially suggest ingredients and dishes that the user prefers. The preference learning unit can also suggest new recipes based on the user's preferences. For example, if the user likes a particular dish, the system can suggest new recipes similar to that dish. This makes it possible to provide menus that match the user's preferences.

[0084] The automatic menu creation system can further include a storage suggestion unit that suggests how to store ingredients. The storage suggestion unit suggests the optimal storage method depending on the type and condition of the ingredients, for example. This allows ingredients to stay fresh for a long time. The storage suggestion unit can also predict the shelf life of ingredients and send reminders to use them up early. For example, it can send a notification when ingredients are nearing the end of their shelf life. This reduces food waste and allows for efficient meal preparation.

[0085] The automatic menu creation system can further include an emotion estimation unit that estimates the user's emotion and suggests a menu based on the estimated emotion. The emotion estimation unit, for example, analyzes the user's facial expression and voice to estimate the user's current emotion. This makes it possible to suggest lighter dishes when the user wants to relax and nutritious dishes when the user needs energy. The emotion estimation unit can also suggest how to select ingredients and how to cook them based on the user's emotion. For example, when the user is feeling stressed, it can suggest dishes using ingredients that have a relaxing effect. This makes it possible to provide a menu that matches the user's emotion.

[0086] The automatic menu creation system may further include a health monitoring unit that monitors the user's health condition and proposes a menu based on the health condition. The health monitoring unit collects data such as the user's weight, blood pressure, and blood sugar level, and analyzes the health condition. This makes it possible to propose an optimal menu based on the user's health condition. The health monitoring unit may also propose a menu tailored to a specific health goal. For example, it may propose low-calorie dishes to a user on a diet, and high-protein dishes to a user aiming to build muscle. This makes it possible to provide a menu tailored to the user's health condition.

[0087] The automatic menu creation system can further include an emotion estimation unit that estimates the user's emotion and suggests how to select ingredients based on the estimated emotion. The emotion estimation unit, for example, analyzes the user's facial expression and voice to estimate the user's current emotion. This makes it possible to suggest ingredients that will evoke positive emotions in the user. The emotion estimation unit can also suggest how to store and cook ingredients based on the user's emotion. For example, when the user is feeling stressed, it can suggest dishes that use ingredients that have a relaxing effect. This makes it possible to provide an ingredient selection method that matches the user's emotion.

[0088] The automatic menu creation system can further include a preference learning unit that learns the user's food preferences and suggests new recipes based on the preferences. The preference learning unit, for example, collects data on menus and ingredients selected by the user in the past and analyzes the user's preferences. This makes it possible to preferentially suggest ingredients and dishes that the user likes. The preference learning unit can also suggest new recipes based on the user's preferences. For example, if the user likes a particular dish, the system can suggest new recipes similar to that dish. This makes it possible to provide new menus tailored to the user's preferences.

[0089] The automatic menu creation system may further include an emotion estimation unit that estimates the user's emotions and suggests food storage methods based on the estimated emotions. The emotion estimation unit, for example, analyzes the user's facial expressions and voice to estimate the user's current emotions. This makes it possible to suggest food storage methods that will make the user feel positive emotions. The emotion estimation unit may also predict the shelf life of food ingredients based on the user's emotions and send reminders to use them up early. For example, it may send a notification when an ingredient's shelf life is approaching. This makes it possible to provide food storage methods that match the user's emotions.

[0090] The automatic menu creation system can further include a health monitoring unit that monitors the user's health condition and suggests how to select ingredients based on the health condition. The health monitoring unit collects data such as the user's weight, blood pressure, and blood sugar level, and analyzes the health condition. This makes it possible to suggest ingredients that are optimal for the user's health condition. The health monitoring unit can also suggest how to select ingredients that are tailored to a specific health goal. For example, it can suggest low-calorie ingredients to a user on a diet, and high-protein ingredients to a user who is trying to build muscle. This makes it possible to provide a way to select ingredients that is tailored to the user's health condition.

[0091] The automatic menu creation system may further include an emotion estimation unit that estimates the user's emotion and suggests cooking methods for ingredients based on the estimated emotion. The emotion estimation unit, for example, analyzes the user's facial expressions and voice to estimate the user's current emotion. This makes it possible to suggest cooking methods that will make the user feel positive. The emotion estimation unit can also suggest methods for storing and cooking ingredients based on the user's emotion. For example, when the user is feeling stressed, it can suggest dishes that use ingredients that have a relaxing effect. This makes it possible to provide cooking methods for ingredients that match the user's emotion.

[0092] The processing flow of the second embodiment will be briefly explained below.

[0093] Step 1: The food data conversion unit converts information about the food in the refrigerator and shopping receipts into data. For example, it uses scanning technology to create a list of the food in the refrigerator and inputs information from the shopping receipt (such as the amount of meat packed and the purchase date). This allows the current food inventory status to be determined. Step 2: The menu creation unit automatically creates daily menus based on the information on ingredients in the refrigerator and shopping receipts digitized by the ingredient data conversion unit. For example, the generation AI generates optimal menus based on ingredient information and prompts regarding the number of people and preferences. Step 3: The waste reduction unit proposes recipes that reduce food waste based on the menu created by the menu creation unit. For example, the recipes proposed by the generation AI are designed to use up all the ingredients in the refrigerator without waste.

[0094] 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.

[0095] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0096] 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.

[0097] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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).

[0103] 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.

[0104] 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.

[0105] 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.

[0106] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0107] 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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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).

[0118] 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.

[0119] 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.

[0120] 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.

[0121] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0122] 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 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0128] 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.

[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 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.

[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 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).

[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] 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.

[0135] 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.

[0136] 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.

[0137] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0138] In the robot 414, the processor 46 performs the identification process. 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. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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).

[0147] 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.

[0148] 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."

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0161] 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 food data conversion unit that converts information about food items in the refrigerator and shopping receipts into data; a menu creation unit that automatically creates a daily menu based on the food ingredients in the refrigerator and the shopping receipts that have been digitized by the food ingredient data creation unit; a waste reduction unit that proposes recipes that reduce waste of ingredients based on the menu created by the menu creation unit. A system characterized by:

2. The ingredient data conversion unit The food items in the refrigerator are scanned, and the freshness and expiration date of the food items are automatically determined and digitized using generation AI.

2. The system of claim 1.

3. The menu creation unit Using generative AI, the system takes into account the user's past dietary history and health status to provide optimal nutritional balance.

2. The system of claim 1.

4. The waste reduction unit Using generative AI to minimize waste of said ingredients, including how to store and cook them.

2. The system of claim 1.

5. The ingredient data conversion unit Analyze the feelings of users about the ingredients they have purchased and prioritize data on the ingredients they like 2. The system of claim 1.

6. The menu creation unit Suggesting the menu that matches the user's current mood 2. The system of claim 1.

7. The waste reduction unit Providing suggestions to increase motivation for users to avoid wasting the ingredients 2. The system of claim 1.

8. The waste reduction unit Propose the above to make composting more enjoyable for users.

2. The system of claim 1.

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