System

Abstract

An object of a system according to an embodiment is to efficiently manage the contents of a refrigerator and reduce food waste while maintaining the balance of meals.SOLUTION: A system according to an embodiment includes an ingredient recognition unit, a recipe suggestion unit, a nutrient analysis unit, an ingredient ordering unit, and a food waste reduction unit. The food recognition unit recognizes the contents of the refrigerator. The recipe proposal unit proposes a recipe suitable for the mood and health goal of the day on the basis of the ingredient recognized by the ingredient recognition unit. The nutrition analysis unit analyzes the nutritional components of the recipe proposed by the recipe proposal unit and checks the balance of the meal. The ingredient ordering unit orders necessary ingredients based on the result checked by the nutrition analysis unit. The food waste reduction unit manages the use-by date of the ingredient recognized by the ingredient recognition unit and makes a proposal for reducing the food waste.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 made it difficult to efficiently manage the contents of a refrigerator and reduce food waste while maintaining a balanced diet.

[0005] The system according to the embodiment aims to efficiently manage the contents of a refrigerator and reduce food waste while maintaining a balanced diet. [Means for solving the problem]

[0006] The system according to the embodiment includes an ingredient recognition unit, a recipe suggestion unit, a nutritional analysis unit, an ingredient ordering unit, and a food waste reduction unit. The ingredient recognition unit recognizes the contents of the refrigerator. The recipe suggestion unit suggests recipes that match the mood and health goals of the day based on the ingredients recognized by the ingredient recognition unit. The nutritional analysis unit analyzes the nutritional components of the recipes suggested by the recipe suggestion unit and checks the balance of the meal. The ingredient ordering unit orders the necessary ingredients based on the results of the check by the nutritional analysis unit. The food waste reduction unit manages the expiration dates of the ingredients recognized by the ingredient recognition unit and makes suggestions for reducing food waste. [Effects of the Invention]

[0007] The system according to the embodiment efficiently manages the contents of the refrigerator, reducing food waste while maintaining a balanced diet. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The one-stop app according to an embodiment of the present invention is a system that automatically recognizes the contents of the refrigerator, and uses a generation AI to suggest recipes that match the mood and health goals of the day, conducts nutritional analysis, orders the necessary ingredients, and reduces food waste. This makes daily meal management easier and contributes to reducing food waste.

[0029] The one-stop app according to the embodiment includes an ingredient recognition unit, a recipe suggestion unit, a nutritional analysis unit, an ingredient ordering unit, and a food waste reduction unit. The ingredient recognition unit recognizes the contents of the refrigerator. For example, when a user takes a photo of the contents of the refrigerator with their smartphone, the generation AI analyzes the image and recognizes the ingredients. The generation AI is fine-tuned in advance using ingredient image data, allowing it to recognize various ingredients with high accuracy. For example, it automatically identifies and lists the tomatoes, lettuce, cheese, and other ingredients in the refrigerator. The recipe suggestion unit suggests recipes tailored to the user's mood and health goals based on the ingredients recognized by the ingredient recognition unit. For example, a user can input a prompt such as "I want a healthy salad today" to the generation AI, and the generation AI generates a recipe based on that instruction. The generation AI suggests a salad using tomatoes and lettuce or a healthy dish using cheese. The nutritional analysis unit analyzes the nutritional content of the recipes suggested by the recipe suggestion unit to check the balance of the meal. For example, the generation AI calculates the calorie, protein, fat, and carbohydrate content of the recipe and provides advice to the user, such as "This recipe is well-balanced" or "You might want to add a little more protein." The ingredient ordering unit orders the necessary ingredients based on the results of the check by the nutritional analysis unit. For example, if an ingredient needed for a suggested recipe is not in the refrigerator, the user can select the ingredient within the app and order it online. This reduces the effort required for shopping. The food waste reduction unit manages the expiration dates of the ingredients recognized by the ingredient recognition unit and makes suggestions for reducing food waste. For example, food waste can be reduced by suggesting recipes that prioritize ingredients with close expiration dates. This allows the one-stop app according to the embodiment to simplify daily dietary management and contribute to reducing food waste. For example, by simply taking a photo of the contents of the refrigerator, the app can consistently perform everything from ingredient recognition to recipe suggestions, nutritional analysis, and ingredient ordering. This also contributes to reducing food waste and realizes environmentally friendly dietary management.

[0030] The ingredient recognition unit can evaluate the freshness of ingredients through image analysis and suggest usage priorities based on freshness. For example, the generation AI analyzes images of ingredients in the refrigerator and evaluates freshness based on changes in color and shape. For example, if a tomato starts to change color, it determines that its freshness is declining and suggests using it sooner. To evaluate the freshness of ingredients, the generation AI tracks changes in freshness by comparing it with past image data. For example, if lettuce leaves begin to wilt, it determines that its freshness is declining and suggests using it first. The ingredient recognition unit also takes environmental data such as temperature and humidity into account when evaluating the freshness of ingredients. For example, if the temperature inside the refrigerator is high, the freshness of ingredients will decline quickly, so it suggests using them sooner. This allows for further reduction in food waste by suggesting usage priorities based on the freshness of ingredients.

[0031] The ingredient recognition unit can identify the origin and cultivation method of ingredients and provide this information to the user. For example, the generation AI analyzes images of ingredients to identify the origin and cultivation method of the ingredients. For example, it reads the label of a tomato and provides the user with the origin and whether it is organically grown. In addition to recognizing ingredients, the ingredient recognition unit also retrieves information about the cultivation method of ingredients from a database and provides this to the user. For example, it displays whether lettuce was grown without pesticides. The ingredient recognition unit also adds a function to read barcodes and QR codes when the generation AI identifies the origin and cultivation method of ingredients. For example, it scans the QR code on a cheese package to display the origin and production method. This allows the generation AI to identify the origin and cultivation method of ingredients and provide more detailed information to the user.

[0032] The ingredient recognition unit can simultaneously recognize ingredients in storage locations other than the refrigerator, enabling comprehensive ingredient management. For example, the ingredient recognition unit adds a function that enables the generation AI to simultaneously recognize ingredients in storage locations other than the refrigerator (such as the pantry or freezer). For example, it recognizes canned goods in the pantry and frozen foods in the freezer and creates a comprehensive ingredient list. The ingredient recognition unit also builds a system in which the generation AI centrally manages ingredients in multiple storage locations. For example, it compiles ingredients in the refrigerator, pantry, and freezer into a single list and provides it to the user. The ingredient recognition unit also takes into account the characteristics of each storage location when the generation AI recognizes ingredients in storage locations other than the refrigerator. For example, since ingredients in the freezer can be stored for a long time, it adjusts the priority of their use. This enables comprehensive ingredient management by recognizing ingredients in storage locations other than the refrigerator.

[0033] The ingredient recognition unit can also be applied to selecting ingredients in home gardens or at markets, allowing the quality of ingredients to be evaluated before purchase. The ingredient recognition unit enables the generation AI to be applied to selecting ingredients in home gardens or at markets. For example, it can analyze images of vegetables grown in home gardens and evaluate the harvest time and quality. The ingredient recognition unit also adds a function to the generation AI to evaluate the quality of ingredients when selecting ingredients at markets. For example, it can analyze images of tomatoes that are planned to be purchased at a market and evaluate their freshness and quality. The ingredient recognition unit also takes into account past data and environmental information when the generation AI evaluates the quality of ingredients when selecting ingredients at home gardens or at markets. For example, it evaluates quality based on weather and soil conditions. This allows the generation AI to be applied to selecting ingredients in home gardens or at markets, allowing the quality of ingredients to be evaluated before purchase.

[0034] The recipe suggestion unit can learn the user's past eating history and suggest recipes optimized for individual preferences. For example, the recipe suggestion unit uses a generation AI to learn the user's past eating history and suggest recipes optimized for individual preferences. For example, it suggests new recipes based on dishes the user has liked to eat in the past. The recipe suggestion unit also analyzes the user's eating history and suggests recipes that take into account preferences and allergy information. For example, it suggests recipes that avoid ingredients to which the user is allergic. The recipe suggestion unit also uses a generation AI to suggest recipes tailored to the season or event based on the user's eating history. For example, it suggests a recipe for this year's Christmas dinner based on the history of past Christmas dinners. In this way, by learning the user's past eating history, it is possible to suggest recipes optimized for individual preferences.

[0035] When suggesting a recipe, the recipe suggestion unit also provides cooking and storage methods for ingredients, thereby minimizing food waste. For example, when the generation AI suggests a recipe, the recipe suggestion unit also provides cooking and storage methods for ingredients. For example, it suggests ways to store tomatoes and ways to cook lettuce. The recipe suggestion unit also provides cooking methods to minimize food waste when suggesting a recipe. For example, it suggests another recipe using leftover ingredients. The recipe suggestion unit also takes environmental data into consideration when the generation AI suggests food storage methods. For example, it suggests storage methods based on the temperature and humidity of the refrigerator. In this way, by providing cooking and storage methods for ingredients, food waste can be minimized.

[0036] The recipe suggestion unit can be expanded to suggest special recipes tailored to specific events or seasons. For example, the recipe suggestion unit adds a function whereby the generative AI suggests recipes tailored to specific events or seasons. For example, special recipes for Christmas or Halloween are suggested. The recipe suggestion unit also suggests recipes that take into account seasonal ingredients. For example, recipes using fresh vegetables are suggested in spring, and cold dishes are suggested in summer. The recipe suggestion unit also takes into account the user's past history when suggesting recipes tailored to events or seasons. For example, a recipe for this year's Christmas dinner is suggested based on the history of past Christmas dinners. This enriches the user's dining experience by suggesting recipes tailored to specific events or seasons.

[0037] The recipe suggestion unit can suggest recipes that take into account the preferences and allergy information of all family members. For example, the generation AI of the recipe suggestion unit suggests recipes that take into account the preferences and allergy information of all family members. For example, if there is someone in the family with an allergy, it will suggest recipes that avoid those ingredients. The recipe suggestion unit also suggests recipes that take into account the preferences of all family members. For example, it will suggest recipes that use ingredients that all family members like. The recipe suggestion unit also learns the eating history of all family members and suggests recipes that will satisfy everyone. For example, it will suggest new recipes based on dishes that all family members have liked in the past. In this way, by taking into account the preferences and allergy information of all family members, it is possible to suggest meals that will satisfy everyone.

[0038] The nutritional analysis unit can acquire a user's health data in real time and adjust the nutritional balance based on that. For example, the generation AI acquires a user's health data in real time and adjusts the nutritional balance based on that. For example, if a user has high blood pressure, it will suggest low-salt recipes. The nutritional analysis unit also builds a system in which the generation AI adjusts the nutritional balance based on the health data. For example, it will suggest low-carbohydrate recipes to a user with high blood sugar levels. The nutritional analysis unit also uses the generation AI to analyze a user's health data and suggest a nutritional balance that matches their long-term health goals. For example, it will suggest calorie-controlled recipes to a user who is aiming to manage their weight. In this way, by adjusting the nutritional balance based on the user's health data, it is possible to provide meals that are suitable for each individual's health condition.

[0039] The nutritional analysis unit can customize the nutritional analysis results to match the user's long-term health goals and support continuous health management. For example, the generative AI in the nutritional analysis unit customizes the nutritional analysis results to match the user's long-term health goals. For example, it would suggest high-protein recipes to a user aiming to build muscle. The nutritional analysis unit also builds a system in which the generative AI adjusts the nutritional balance based on the user's health goals. For example, it would suggest low-calorie recipes to a user aiming to lose weight. The nutritional analysis unit also uses the generative AI to analyze the user's health data and suggest a nutritional balance to support long-term health management. For example, it would suggest low-fat recipes to a user aiming to prevent heart disease. In this way, continuous health management can be supported by customizing the nutritional analysis results to match the user's long-term health goals.

[0040] The nutritional analysis unit can be expanded to suggest meals that cater to specific illnesses or allergies. For example, the nutritional analysis unit adds a function that enables the generation AI to suggest meals that cater to specific illnesses or allergies. For example, it could suggest low-carb recipes for diabetic patients. The nutritional analysis unit also builds a system in which the generation AI suggests safe meals based on allergy information. For example, it could suggest nut-free recipes to a user with a nut allergy. The nutritional analysis unit also enables the generation AI to suggest nutritional balances that cater to specific illnesses. For example, it could suggest low-salt recipes to a user with high blood pressure. This allows the generation AI to support health management by suggesting meals that cater to specific illnesses or allergies.

[0041] The nutritional analysis unit can link the nutritional analysis results with other health management apps and devices to achieve comprehensive health management. For example, the nutritional analysis unit adds a function that allows the generation AI to link the nutritional analysis results with other health management apps. For example, it can link with a fitness app to make meal suggestions based on exercise data. The nutritional analysis unit can also link with health management devices, and the generation AI can build a system that supports comprehensive health management. For example, it can adjust nutritional balance based on data from a smartwatch. The nutritional analysis unit can also share data with other health management apps and devices to achieve comprehensive health management. For example, it can make meal suggestions based on sleep data. This allows the system to link with other health management apps and devices to achieve comprehensive health management.

[0042] The ingredient ordering unit can learn the user's past purchase history and automatically generate an optimal shopping list. For example, the ingredient ordering unit uses a generation AI to learn the user's past purchase history and automatically generate an optimal shopping list. For example, ingredients that the user frequently purchases are added to the list. The ingredient ordering unit also analyzes the user's purchase history and builds a system to predict what ingredients will be needed. For example, it suggests the next shopping list based on past purchase patterns. The ingredient ordering unit also uses a generation AI to automatically generate a shopping list tailored to the season or event based on the user's purchase history. For example, it adds ingredients needed before Christmas to the list. In this way, the optimal shopping list can be automatically generated by learning the user's past purchase history.

[0043] The food ordering unit can customize food ordering destinations to suit the user's preferences, such as local farms or organic shops. In the food ordering unit, for example, the generation AI customizes food ordering destinations to suit the user's preferences. For example, it prioritizes orders from local farms and organic shops. The food ordering unit also builds a system in which the generation AI takes user preferences into consideration and suggests the optimal ordering destination. For example, if the user prefers organic food, it suggests ordering from an organic shop. The food ordering unit also customizes ordering destinations to suit the user's preferences based on the user's past ordering history. For example, it prioritizes suggesting food from farms that the user has ordered from in the past. In this way, satisfaction is improved by customizing food ordering destinations to suit the user's preferences.

[0044] The food ordering unit can expand the food ordering function to a periodic food subscription service. For example, the food ordering unit adds a function in which the generation AI suggests a periodic food subscription service. For example, it automatically orders the ingredients needed every week. The food ordering unit also builds a system in which the generation AI suggests the most suitable subscription plan based on the user's purchase history. For example, it suggests a plan that includes ingredients that the user frequently purchases. The food ordering unit also suggests subscription services that match the season or event. For example, it suggests a plan that includes special ingredients before Christmas. This improves user convenience by providing a periodic food subscription service.

[0045] The food ordering unit can provide eco-friendly packaging and delivery options when ordering food, encouraging environmentally conscious choices. For example, the food ordering unit adds a function where the generation AI suggests eco-friendly packaging and delivery options. For example, recyclable packaging materials are used. The food ordering unit also builds a system that encourages environmentally conscious choices when a user places an order. For example, delivery options with a low carbon footprint are suggested. The food ordering unit also has the generation AI suggest eco-friendly choices based on the user's environmental awareness. For example, direct delivery from local farmers is prioritized. This makes it possible to encourage environmentally conscious choices by providing eco-friendly packaging and delivery options.

[0046] The food waste reduction unit can track the expiration dates of ingredients in real time and suggest recipes that prioritize using ingredients with upcoming expiration dates. For example, the food waste reduction unit's generation AI tracks the expiration dates of ingredients in real time and suggests recipes that prioritize using ingredients with upcoming expiration dates. For example, it suggests a recipe that uses tomatoes with an approaching expiration date. The food waste reduction unit also builds a system in which the generation AI suggests optimal recipes based on the expiration dates of ingredients. For example, it suggests recipes that prioritize using ingredients with an approaching expiration date. The food waste reduction unit also takes environmental data into account when the generation AI tracks the expiration dates of ingredients. For example, it adjusts the expiration date according to the temperature and humidity of the refrigerator. In this way, food waste can be reduced by tracking the expiration dates of ingredients in real time.

[0047] The food waste reduction unit can minimize waste by suggesting ways to store and reuse ingredients to reduce food waste. For example, the generation AI in the food waste reduction unit suggests ways to store ingredients to reduce food waste. For example, it provides users with ways to store tomatoes and ways to store lettuce. The food waste reduction unit also builds a system that suggests ways to reuse ingredients. For example, it suggests different recipes using leftover ingredients. The food waste reduction unit also takes environmental data into consideration when the generation AI suggests ways to store and reuse ingredients. For example, it suggests storage methods based on the temperature and humidity of the refrigerator. In this way, food waste can be minimized by suggesting ways to store and reuse ingredients.

[0048] The food waste reduction unit can link the food waste reduction function with a local food sharing platform to provide a mechanism for sharing surplus ingredients. For example, the generation AI links with a local food sharing platform to provide a mechanism for sharing surplus ingredients. For example, surplus ingredients are shared with neighbors. The food waste reduction unit also links with a food sharing platform to build a system where the generation AI efficiently shares surplus ingredients. For example, surplus ingredients are registered and notified to neighbors. The food waste reduction unit also takes into account user preferences and allergy information when the generation AI links with a local food sharing platform. For example, it prevents the sharing of ingredients that people with allergies should avoid. In this way, surplus ingredients can be shared efficiently by linking with a local food sharing platform.

[0049] The Food Loss Reduction Department can make suggestions to promote the use of home gardens and composting in order to reduce food waste. For example, the generation AI in the Food Loss Reduction Department makes suggestions to promote the use of home gardens. For example, it proposes ways to reuse leftover ingredients in home gardens. The Food Loss Reduction Department also builds a system that makes suggestions to promote the use of composting. For example, it proposes ways to compost leftover ingredients. The Food Loss Reduction Department also takes environmental data into consideration when the generation AI promotes the use of home gardens and composting. For example, it proposes the optimal reuse method according to the home garden environment. This can promote the use of home gardens and composting, thereby reducing food waste.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The ingredient recognition unit can evaluate the freshness of ingredients through image analysis and suggest usage priorities based on freshness. For example, the generation AI analyzes images of ingredients in the refrigerator and evaluates freshness based on changes in color and shape. For example, if a tomato starts to change color, it will determine that its freshness is declining and recommend using it sooner. To evaluate the freshness of ingredients, the generation AI compares it with past image data and tracks changes in freshness. For example, if lettuce leaves begin to wilt, it will determine that its freshness is declining and recommend using it first. The ingredient recognition unit also takes environmental data such as temperature and humidity into account when evaluating the freshness of ingredients. For example, if the temperature inside the refrigerator is high, the freshness of ingredients will decline quickly, so it will recommend using them sooner. This allows the generation AI to suggest usage priorities based on the freshness of ingredients, further reducing food waste.

[0052] The ingredient recognition unit can identify the origin and cultivation method of ingredients and provide this information to the user. For example, the generation AI analyzes images of ingredients to identify the origin and cultivation method of the ingredients. For example, it reads the label of a tomato and provides the user with the origin and whether it is organically grown. In addition to recognizing ingredients, the ingredient recognition unit also retrieves information about the cultivation method of ingredients from a database and provides this to the user. For example, it displays whether lettuce was grown without pesticides. The ingredient recognition unit also adds the ability to read barcodes and QR codes when the generation AI identifies the origin and cultivation method of ingredients. For example, it scans the QR code on a cheese package to display the origin and production method. This allows the generation AI to identify the origin and cultivation method of ingredients and provide more detailed information to the user.

[0053] The ingredient recognition unit can simultaneously recognize ingredients in storage locations other than the refrigerator, enabling comprehensive ingredient management. For example, the generation AI can add a function to simultaneously recognize ingredients in storage locations other than the refrigerator (such as the pantry or freezer). For example, it can recognize canned goods in the pantry and frozen foods in the freezer and create a comprehensive ingredient list. The ingredient recognition unit also builds a system in which the generation AI centrally manages ingredients in multiple storage locations. For example, it can consolidate ingredients in the refrigerator, pantry, and freezer into a single list and provide it to the user. The ingredient recognition unit also takes into account the characteristics of each storage location when the generation AI recognizes ingredients in storage locations other than the refrigerator. For example, since ingredients in the freezer can be stored for a long time, it can adjust the priority of their use. This enables comprehensive ingredient management by recognizing ingredients in storage locations other than the refrigerator.

[0054] The ingredient recognition unit can also be applied to selecting ingredients in home gardens or at markets, allowing the quality of ingredients to be evaluated before purchase. For example, the generative AI can be applied to selecting ingredients in home gardens or at markets. For example, it can analyze images of vegetables grown in home gardens and evaluate the harvest time and quality. The ingredient recognition unit also adds a function that allows the generative AI to evaluate the quality of ingredients when selecting ingredients at markets. For example, it can analyze images of tomatoes that are planned to be purchased at the market and evaluate their freshness and quality. The ingredient recognition unit also takes into account past data and environmental information when the generative AI evaluates the quality of ingredients when selecting ingredients at home gardens or at markets. For example, it evaluates quality based on weather and soil conditions. This can be applied to selecting ingredients in home gardens or at markets, allowing the quality of ingredients to be evaluated before purchase.

[0055] The recipe suggestion unit can learn the user's past eating history and suggest recipes optimized for individual preferences. For example, the generation AI learns the user's past eating history and suggests recipes optimized for individual preferences. For example, it suggests new recipes based on the user's favorite dishes in the past. The recipe suggestion unit also analyzes the user's eating history and suggests recipes that take into account preferences and allergy information. For example, it suggests recipes that avoid ingredients to which the user is allergic. The recipe suggestion unit also suggests recipes that match the season or event based on the user's eating history. For example, it suggests a recipe for this year's Christmas dinner based on the history of past Christmas dinners. In this way, by learning the user's past eating history, it is possible to suggest recipes optimized for individual preferences.

[0056] The recipe suggestion unit can be expanded to suggest special recipes tailored to specific events or seasons. For example, a function could be added whereby the generative AI suggests recipes tailored to specific events or seasons. For example, special recipes for Christmas or Halloween could be suggested. The recipe suggestion unit could also suggest recipes that take into account seasonal ingredients. For example, recipes using fresh vegetables could be suggested in spring, and cold dishes in summer. The recipe suggestion unit could also take into account the user's past history when suggesting recipes tailored to events or seasons. For example, a recipe for this year's Christmas dinner could be suggested based on the user's history of past Christmas dinners. This enriches the user's dining experience by suggesting recipes tailored to specific events or seasons.

[0057] The recipe suggestion unit can suggest recipes that take into account the preferences and allergy information of all family members. For example, the generation AI will suggest recipes that take into account the preferences and allergy information of all family members. For example, if there is someone in the family with an allergy, it will suggest recipes that avoid those ingredients. The recipe suggestion unit also suggests recipes that take into account the preferences of all family members. For example, it will suggest recipes that use ingredients that all family members like. The recipe suggestion unit also learns the eating history of all family members and suggests recipes that will satisfy everyone. For example, it will suggest new recipes based on dishes that all family members have liked in the past. In this way, by taking into account the preferences and allergy information of all family members, it is possible to suggest meals that will satisfy everyone.

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

[0059] Step 1: The ingredient recognition unit recognizes the contents of the refrigerator. For example, when a user takes a photo of the contents of the refrigerator with their smartphone, the generation AI analyzes the image and recognizes the ingredients. The generation AI has been fine-tuned in advance using image data of ingredients, allowing it to recognize a variety of ingredients with high accuracy. For example, it can automatically identify and list tomatoes, lettuce, cheese, etc. in the refrigerator. Step 2: The recipe suggestion unit suggests recipes that match the user's mood and health goals for the day based on the ingredients recognized by the ingredient recognition unit. For example, the user can input a prompt such as "I want to eat a healthy salad today" into the generation AI, and the generation AI will generate a recipe based on that instruction. The generation AI will suggest a salad using tomato and lettuce, or a healthy dish using cheese. Step 3: The nutritional analysis unit analyzes the nutritional content of the recipes suggested by the recipe suggestion unit and checks the balance of the meal. For example, the generative AI calculates the amount of calories, protein, fat, and carbohydrates in the recipe and provides advice to the user, such as "This recipe is well-balanced" or "You might want to add a little more protein." Step 4: The ingredient ordering unit orders the necessary ingredients based on the results checked by the nutritional analysis unit. For example, if an ingredient required for a suggested recipe is not in the refrigerator, the user can select it within the app and order it online. This saves the user the trouble of having to go shopping. Step 5: The food waste reduction unit manages the expiration dates of the ingredients recognized by the ingredient recognition unit and makes suggestions for reducing food waste. For example, food waste can be reduced by suggesting recipes that prioritize the use of ingredients with a short expiration date.

[0060] (Example 2) The one-stop app according to an embodiment of the present invention is a system that automatically recognizes the contents of the refrigerator, and uses a generation AI to suggest recipes that match the mood and health goals of the day, conducts nutritional analysis, orders the necessary ingredients, and reduces food waste. This makes daily meal management easier and contributes to reducing food waste.

[0061] The one-stop app according to the embodiment includes an ingredient recognition unit, a recipe suggestion unit, a nutritional analysis unit, an ingredient ordering unit, and a food waste reduction unit. The ingredient recognition unit recognizes the contents of the refrigerator. For example, when a user takes a photo of the contents of the refrigerator with their smartphone, the generation AI analyzes the image and recognizes the ingredients. The generation AI is fine-tuned in advance using ingredient image data, allowing it to recognize various ingredients with high accuracy. For example, it automatically identifies and lists the tomatoes, lettuce, cheese, and other ingredients in the refrigerator. The recipe suggestion unit suggests recipes tailored to the user's mood and health goals based on the ingredients recognized by the ingredient recognition unit. For example, a user can input a prompt such as "I want a healthy salad today" to the generation AI, and the generation AI generates a recipe based on that instruction. The generation AI suggests a salad using tomatoes and lettuce or a healthy dish using cheese. The nutritional analysis unit analyzes the nutritional content of the recipes suggested by the recipe suggestion unit to check the balance of the meal. For example, the generation AI calculates the calorie, protein, fat, and carbohydrate content of the recipe and provides advice to the user, such as "This recipe is well-balanced" or "You might want to add a little more protein." The ingredient ordering unit orders the necessary ingredients based on the results of the check by the nutritional analysis unit. For example, if an ingredient needed for a suggested recipe is not in the refrigerator, the user can select the ingredient within the app and order it online. This reduces the effort required for shopping. The food waste reduction unit manages the expiration dates of the ingredients recognized by the ingredient recognition unit and makes suggestions for reducing food waste. For example, food waste can be reduced by suggesting recipes that prioritize ingredients with close expiration dates. This allows the one-stop app according to the embodiment to simplify daily dietary management and contribute to reducing food waste. For example, by simply taking a photo of the contents of the refrigerator, the app can consistently perform everything from ingredient recognition to recipe suggestions, nutritional analysis, and ingredient ordering. This also contributes to reducing food waste and realizes environmentally friendly dietary management.

[0062] The ingredient recognition unit can evaluate the freshness of ingredients through image analysis and suggest usage priorities based on freshness. For example, the generation AI analyzes images of ingredients in the refrigerator and evaluates freshness based on changes in color and shape. For example, if a tomato starts to change color, it determines that its freshness is declining and suggests using it sooner. To evaluate the freshness of ingredients, the generation AI tracks changes in freshness by comparing it with past image data. For example, if lettuce leaves begin to wilt, it determines that its freshness is declining and suggests using it first. The ingredient recognition unit also takes environmental data such as temperature and humidity into account when evaluating the freshness of ingredients. For example, if the temperature inside the refrigerator is high, the freshness of ingredients will decline quickly, so it suggests using them sooner. This allows for further reduction in food waste by suggesting usage priorities based on the freshness of ingredients.

[0063] The ingredient recognition unit can identify the origin and cultivation method of ingredients and provide this information to the user. For example, the generation AI analyzes images of ingredients to identify the origin and cultivation method of the ingredients. For example, it reads the label of a tomato and provides the user with the origin and whether it is organically grown. In addition to recognizing ingredients, the ingredient recognition unit also retrieves information about the cultivation method of ingredients from a database and provides this to the user. For example, it displays whether lettuce was grown without pesticides. The ingredient recognition unit also adds a function to read barcodes and QR codes when the generation AI identifies the origin and cultivation method of ingredients. For example, it scans the QR code on a cheese package to display the origin and production method. This allows the generation AI to identify the origin and cultivation method of ingredients and provide more detailed information to the user.

[0064] The ingredient recognition unit uses an emotion estimation function to analyze the user's emotions toward specific ingredients and prioritize suggesting preferred ingredients. For example, the generation AI of the ingredient recognition unit analyzes the user's facial expressions and voice to estimate emotions toward specific ingredients. For example, if the user smiles when they see tomatoes, it determines that the user likes tomatoes and prioritizes suggesting them. The ingredient recognition unit also uses the emotion estimation function to analyze the user's emotions toward ingredients they have previously selected and recommends preferred ingredients. For example, if the user showed positive emotions when selecting lettuce, it prioritizes suggesting lettuce. The ingredient recognition unit also accumulates user emotion data and analyzes emotional trends toward specific ingredients. For example, if the user always shows positive emotions toward cheese, it prioritizes suggesting recipes that include cheese. This improves meal satisfaction by suggesting preferred ingredients based on the user's emotions.

[0065] The ingredient recognition unit can simultaneously recognize ingredients in storage locations other than the refrigerator, enabling comprehensive ingredient management. For example, the ingredient recognition unit adds a function that enables the generation AI to simultaneously recognize ingredients in storage locations other than the refrigerator (such as the pantry or freezer). For example, it recognizes canned goods in the pantry and frozen foods in the freezer and creates a comprehensive ingredient list. The ingredient recognition unit also builds a system in which the generation AI centrally manages ingredients in multiple storage locations. For example, it compiles ingredients in the refrigerator, pantry, and freezer into a single list and provides it to the user. The ingredient recognition unit also takes into account the characteristics of each storage location when the generation AI recognizes ingredients in storage locations other than the refrigerator. For example, since ingredients in the freezer can be stored for a long time, it adjusts the priority of their use. This enables comprehensive ingredient management by recognizing ingredients in storage locations other than the refrigerator.

[0066] The ingredient recognition unit can also be applied to selecting ingredients in home gardens or at markets, allowing the quality of ingredients to be evaluated before purchase. The ingredient recognition unit enables the generation AI to be applied to selecting ingredients in home gardens or at markets. For example, it can analyze images of vegetables grown in home gardens and evaluate the harvest time and quality. The ingredient recognition unit also adds a function to the generation AI to evaluate the quality of ingredients when selecting ingredients at markets. For example, it can analyze images of tomatoes that are planned to be purchased at a market and evaluate their freshness and quality. The ingredient recognition unit also takes into account past data and environmental information when the generation AI evaluates the quality of ingredients when selecting ingredients at home gardens or at markets. For example, it evaluates quality based on weather and soil conditions. This allows the generation AI to be applied to selecting ingredients in home gardens or at markets, allowing the quality of ingredients to be evaluated before purchase.

[0067] The ingredient recognition unit uses the emotion estimation function to monitor the user's emotions in real time when selecting ingredients and can suggest ingredients that elicit positive emotions. For example, the ingredient recognition unit uses the generation AI to analyze the user's facial expressions and voice in real time and monitor the user's emotions when selecting ingredients. For example, it prioritizes suggesting ingredients that make the user smile. The ingredient recognition unit also uses the emotion estimation function to analyze the user's emotions when selecting ingredients and suggests ingredients that elicit positive emotions. For example, it suggests recipes that include ingredients that the user likes. The ingredient recognition unit also accumulates the user's emotion data and analyzes their ingredient selection tendencies. For example, if the user expresses positive emotions toward a particular ingredient, it prioritizes suggesting that ingredient. This improves meal satisfaction by suggesting ingredients based on the user's emotions.

[0068] The recipe suggestion unit can learn the user's past eating history and suggest recipes optimized for individual preferences. For example, the recipe suggestion unit uses a generation AI to learn the user's past eating history and suggest recipes optimized for individual preferences. For example, it suggests new recipes based on dishes the user has liked to eat in the past. The recipe suggestion unit also analyzes the user's eating history and suggests recipes that take into account preferences and allergy information. For example, it suggests recipes that avoid ingredients to which the user is allergic. The recipe suggestion unit also uses a generation AI to suggest recipes tailored to the season or event based on the user's eating history. For example, it suggests a recipe for this year's Christmas dinner based on the history of past Christmas dinners. In this way, by learning the user's past eating history, it is possible to suggest recipes optimized for individual preferences.

[0069] When suggesting a recipe, the recipe suggestion unit also provides cooking and storage methods for ingredients, thereby minimizing food waste. For example, when the generation AI suggests a recipe, the recipe suggestion unit also provides cooking and storage methods for ingredients. For example, it suggests ways to store tomatoes and ways to cook lettuce. The recipe suggestion unit also provides cooking methods to minimize food waste when suggesting a recipe. For example, it suggests another recipe using leftover ingredients. The recipe suggestion unit also takes environmental data into consideration when the generation AI suggests food storage methods. For example, it suggests storage methods based on the temperature and humidity of the refrigerator. In this way, by providing cooking and storage methods for ingredients, food waste can be minimized.

[0070] The recipe suggestion unit uses the emotion estimation function to analyze the user's mood of the day in real time and suggest recipes that match the mood. For example, the generation AI of the recipe suggestion unit analyzes the user's facial expressions and voice in real time to analyze the user's mood of the day. For example, if the user is relaxed, it will suggest easy and relaxing recipes. The recipe suggestion unit also uses the emotion estimation function to suggest recipes that match the user's mood. For example, if the user is feeling stressed, it will suggest recipes that use ingredients that help relieve stress. The recipe suggestion unit also accumulates the user's mood data and analyzes the relationship between past moods and meals. For example, it will suggest recipes that the user liked when they wanted to relax in the past. This improves meal satisfaction by suggesting recipes that match the user's mood.

[0071] The recipe suggestion unit can be expanded to suggest special recipes tailored to specific events or seasons. For example, the recipe suggestion unit adds a function whereby the generative AI suggests recipes tailored to specific events or seasons. For example, special recipes for Christmas or Halloween are suggested. The recipe suggestion unit also suggests recipes that take into account seasonal ingredients. For example, recipes using fresh vegetables are suggested in spring, and cold dishes are suggested in summer. The recipe suggestion unit also takes into account the user's past history when suggesting recipes tailored to events or seasons. For example, a recipe for this year's Christmas dinner is suggested based on the history of past Christmas dinners. This enriches the user's dining experience by suggesting recipes tailored to specific events or seasons.

[0072] The recipe suggestion unit can suggest recipes that take into account the preferences and allergy information of all family members. For example, the generation AI of the recipe suggestion unit suggests recipes that take into account the preferences and allergy information of all family members. For example, if there is someone in the family with an allergy, it will suggest recipes that avoid those ingredients. The recipe suggestion unit also suggests recipes that take into account the preferences of all family members. For example, it will suggest recipes that use ingredients that all family members like. The recipe suggestion unit also learns the eating history of all family members and suggests recipes that will satisfy everyone. For example, it will suggest new recipes based on dishes that all family members have liked in the past. In this way, by taking into account the preferences and allergy information of all family members, it is possible to suggest meals that will satisfy everyone.

[0073] The recipe suggestion unit uses the emotion estimation function to analyze the emotions of all family members and can suggest recipes that will make everyone feel positive. For example, the generation AI of the recipe suggestion unit analyzes the facial expressions and voices of all family members to analyze their emotions. For example, it will prioritize suggesting recipes that will make all family members smile. The recipe suggestion unit also uses the emotion estimation function to analyze the emotions of all family members and suggest recipes that will make everyone feel positive. For example, it will suggest recipes that use ingredients that all family members like. The recipe suggestion unit also accumulates emotional data of all family members and analyzes the relationship between past emotions and meals. For example, it will suggest recipes that all family members have previously expressed positive emotions about. In this way, by analyzing the emotions of all family members, it can suggest recipes that will make everyone feel positive.

[0074] The nutritional analysis unit can acquire a user's health data in real time and adjust the nutritional balance based on that. For example, the generation AI acquires a user's health data in real time and adjusts the nutritional balance based on that. For example, if a user has high blood pressure, it will suggest low-salt recipes. The nutritional analysis unit also builds a system in which the generation AI adjusts the nutritional balance based on the health data. For example, it will suggest low-carbohydrate recipes to a user with high blood sugar levels. The nutritional analysis unit also uses the generation AI to analyze a user's health data and suggest a nutritional balance that matches their long-term health goals. For example, it will suggest calorie-controlled recipes to a user who is aiming to manage their weight. In this way, by adjusting the nutritional balance based on the user's health data, it is possible to provide meals that are suitable for each individual's health condition.

[0075] The nutritional analysis unit can customize the nutritional analysis results to match the user's long-term health goals and support continuous health management. For example, the generative AI in the nutritional analysis unit customizes the nutritional analysis results to match the user's long-term health goals. For example, it would suggest high-protein recipes to a user aiming to build muscle. The nutritional analysis unit also builds a system in which the generative AI adjusts the nutritional balance based on the user's health goals. For example, it would suggest low-calorie recipes to a user aiming to lose weight. The nutritional analysis unit also uses the generative AI to analyze the user's health data and suggest a nutritional balance to support long-term health management. For example, it would suggest low-fat recipes to a user aiming to prevent heart disease. In this way, continuous health management can be supported by customizing the nutritional analysis results to match the user's long-term health goals.

[0076] The nutrition analysis unit uses the emotion estimation function to analyze the user's emotions toward food and suggest nutritional balance that elicits positive emotions. For example, the generation AI in the nutrition analysis unit analyzes the user's facial expressions and voice to analyze their emotions toward food. For example, it prioritizes suggesting meals that make the user smile. The nutrition analysis unit also uses the emotion estimation function to analyze the user's emotions toward food and suggest nutritional balance that elicits positive emotions. For example, it suggests recipes using the user's favorite ingredients. The nutrition analysis unit also accumulates the user's emotional data and analyzes their emotional tendencies toward food. For example, it adjusts the nutritional balance based on meals that the user has previously felt positive about. In this way, suggesting nutritional balance based on the user's emotions improves meal satisfaction.

[0077] The nutritional analysis unit can be expanded to suggest meals that cater to specific illnesses or allergies. For example, the nutritional analysis unit adds a function that enables the generation AI to suggest meals that cater to specific illnesses or allergies. For example, it could suggest low-carb recipes for diabetic patients. The nutritional analysis unit also builds a system in which the generation AI suggests safe meals based on allergy information. For example, it could suggest nut-free recipes to a user with a nut allergy. The nutritional analysis unit also enables the generation AI to suggest nutritional balances that cater to specific illnesses. For example, it could suggest low-salt recipes to a user with high blood pressure. This allows the generation AI to support health management by suggesting meals that cater to specific illnesses or allergies.

[0078] The nutritional analysis unit can link the nutritional analysis results with other health management apps and devices to achieve comprehensive health management. For example, the nutritional analysis unit adds a function that allows the generation AI to link the nutritional analysis results with other health management apps. For example, it can link with a fitness app to make meal suggestions based on exercise data. The nutritional analysis unit can also link with health management devices, and the generation AI can build a system that supports comprehensive health management. For example, it can adjust nutritional balance based on data from a smartwatch. The nutritional analysis unit can also share data with other health management apps and devices to achieve comprehensive health management. For example, it can make meal suggestions based on sleep data. This allows the system to link with other health management apps and devices to achieve comprehensive health management.

[0079] The nutritional analysis unit uses the emotion estimation function to adjust the nutritional balance based on the user's emotions, thereby improving meal satisfaction. For example, the generation AI in the nutritional analysis unit analyzes the user's facial expressions and voice and adjusts the nutritional balance based on their emotions. For example, it suggests a nutritional balance that is suitable for when the user wants to relax. The nutritional analysis unit also uses the emotion estimation function to build a system that adjusts the nutritional balance based on the user's emotions. For example, it suggests a nutritional balance that will help relieve stress for a user who is feeling stressed. The nutritional analysis unit also accumulates the user's emotional data and adjusts the nutritional balance based on their emotions. For example, it adjusts the nutritional balance based on meals that the user has previously expressed positive emotions about. In this way, adjusting the nutritional balance based on the user's emotions improves meal satisfaction.

[0080] The ingredient ordering unit can learn the user's past purchase history and automatically generate an optimal shopping list. For example, the ingredient ordering unit uses a generation AI to learn the user's past purchase history and automatically generate an optimal shopping list. For example, ingredients that the user frequently purchases are added to the list. The ingredient ordering unit also analyzes the user's purchase history and builds a system to predict what ingredients will be needed. For example, it suggests the next shopping list based on past purchase patterns. The ingredient ordering unit also uses a generation AI to automatically generate a shopping list tailored to the season or event based on the user's purchase history. For example, it adds ingredients needed before Christmas to the list. In this way, the optimal shopping list can be automatically generated by learning the user's past purchase history.

[0081] The food ordering unit can customize food ordering destinations to suit the user's preferences, such as local farms or organic shops. In the food ordering unit, for example, the generation AI customizes food ordering destinations to suit the user's preferences. For example, it prioritizes orders from local farms and organic shops. The food ordering unit also builds a system in which the generation AI takes user preferences into consideration and suggests the optimal ordering destination. For example, if the user prefers organic food, it suggests ordering from an organic shop. The food ordering unit also customizes ordering destinations to suit the user's preferences based on the user's past ordering history. For example, it prioritizes suggesting food from farms that the user has ordered from in the past. In this way, satisfaction is improved by customizing food ordering destinations to suit the user's preferences.

[0082] The ingredient ordering unit uses the emotion estimation function to analyze the user's emotions when making a purchase and can suggest ingredients that elicit positive emotions. For example, the ingredient ordering unit uses a generation AI to analyze the user's facial expressions and voice to analyze their emotions at the time of purchase. For example, it prioritizes suggesting ingredients that make the user smile. The ingredient ordering unit also uses the emotion estimation function to analyze the user's emotions when making a purchase and suggests ingredients that elicit positive emotions. For example, it suggests a shopping list that includes ingredients that the user likes. The ingredient ordering unit also uses a generation AI to accumulate user emotion data and analyze emotional trends at the time of purchase. For example, it prioritizes suggesting ingredients that the user has previously expressed positive emotions about. In this way, suggesting ingredients based on the user's emotions improves satisfaction at the time of purchase.

[0083] The food ordering unit can expand the food ordering function to a periodic food subscription service. For example, the food ordering unit adds a function in which the generation AI suggests a periodic food subscription service. For example, it automatically orders the ingredients needed every week. The food ordering unit also builds a system in which the generation AI suggests the most suitable subscription plan based on the user's purchase history. For example, it suggests a plan that includes ingredients that the user frequently purchases. The food ordering unit also suggests subscription services that match the season or event. For example, it suggests a plan that includes special ingredients before Christmas. This improves user convenience by providing a periodic food subscription service.

[0084] The food ordering unit can provide eco-friendly packaging and delivery options when ordering food, encouraging environmentally conscious choices. For example, the food ordering unit adds a function where the generation AI suggests eco-friendly packaging and delivery options. For example, recyclable packaging materials are used. The food ordering unit also builds a system that encourages environmentally conscious choices when a user places an order. For example, delivery options with a low carbon footprint are suggested. The food ordering unit also has the generation AI suggest eco-friendly choices based on the user's environmental awareness. For example, direct delivery from local farmers is prioritized. This makes it possible to encourage environmentally conscious choices by providing eco-friendly packaging and delivery options.

[0085] The ingredient ordering unit uses the emotion estimation function to analyze the order history based on the user's emotions and make optimal suggestions for the next order. For example, the generation AI in the ingredient ordering unit analyzes the user's facial expressions and voice to analyze the emotions at the time of ordering. For example, it prioritizes suggesting ingredients that make the user smile. The ingredient ordering unit also uses the emotion estimation function to analyze the order history based on the user's emotions and make optimal suggestions for the next order. For example, it prioritizes suggesting ingredients that the user has previously expressed positive emotions about. The ingredient ordering unit also accumulates user emotion data and analyzes emotional trends at the time of ordering. For example, it prioritizes suggesting ingredients that the user has previously expressed positive emotions about. In this way, by analyzing the order history based on the user's emotions, it is possible to make optimal suggestions for the next order.

[0086] The food waste reduction unit can track the expiration dates of ingredients in real time and suggest recipes that prioritize using ingredients with upcoming expiration dates. For example, the food waste reduction unit's generation AI tracks the expiration dates of ingredients in real time and suggests recipes that prioritize using ingredients with upcoming expiration dates. For example, it suggests a recipe that uses tomatoes with an approaching expiration date. The food waste reduction unit also builds a system in which the generation AI suggests optimal recipes based on the expiration dates of ingredients. For example, it suggests recipes that prioritize using ingredients with an approaching expiration date. The food waste reduction unit also takes environmental data into account when the generation AI tracks the expiration dates of ingredients. For example, it adjusts the expiration date according to the temperature and humidity of the refrigerator. In this way, food waste can be reduced by tracking the expiration dates of ingredients in real time.

[0087] The food waste reduction unit can minimize waste by suggesting ways to store and reuse ingredients to reduce food waste. For example, the generation AI in the food waste reduction unit suggests ways to store ingredients to reduce food waste. For example, it provides users with ways to store tomatoes and ways to store lettuce. The food waste reduction unit also builds a system that suggests ways to reuse ingredients. For example, it suggests different recipes using leftover ingredients. The food waste reduction unit also takes environmental data into consideration when the generation AI suggests ways to store and reuse ingredients. For example, it suggests storage methods based on the temperature and humidity of the refrigerator. In this way, food waste can be minimized by suggesting ways to store and reuse ingredients.

[0088] The food waste reduction unit uses the emotion estimation function to analyze the user's emotions toward food waste and make suggestions that elicit positive emotions. For example, the generation AI in the food waste reduction unit analyzes the user's facial expressions and voice to analyze their emotions toward food waste. For example, it prioritizes suggestions that elicit positive emotions from the user. The food waste reduction unit also uses the emotion estimation function to analyze the user's emotions toward food waste and make suggestions that elicit positive emotions. For example, it suggests recipes using the user's favorite ingredients. The food waste reduction unit also accumulates user emotion data and analyzes emotional trends toward food waste. For example, it makes suggestions for reducing food waste based on suggestions that the user has previously expressed positive emotions about. In this way, it is possible to raise awareness by making suggestions for reducing food waste based on the user's emotions.

[0089] The food waste reduction unit can link the food waste reduction function with a local food sharing platform to provide a mechanism for sharing surplus ingredients. For example, the generation AI links with a local food sharing platform to provide a mechanism for sharing surplus ingredients. For example, surplus ingredients are shared with neighbors. The food waste reduction unit also links with a food sharing platform to build a system where the generation AI efficiently shares surplus ingredients. For example, surplus ingredients are registered and notified to neighbors. The food waste reduction unit also takes into account user preferences and allergy information when the generation AI links with a local food sharing platform. For example, it prevents the sharing of ingredients that people with allergies should avoid. In this way, surplus ingredients can be shared efficiently by linking with a local food sharing platform.

[0090] The Food Loss Reduction Department can make suggestions to promote the use of home gardens and composting in order to reduce food waste. For example, the generation AI in the Food Loss Reduction Department makes suggestions to promote the use of home gardens. For example, it proposes ways to reuse leftover ingredients in home gardens. The Food Loss Reduction Department also builds a system that makes suggestions to promote the use of composting. For example, it proposes ways to compost leftover ingredients. The Food Loss Reduction Department also takes environmental data into consideration when the generation AI promotes the use of home gardens and composting. For example, it proposes the optimal reuse method according to the home garden environment. This can promote the use of home gardens and composting, thereby reducing food waste.

[0091] The food waste reduction unit uses the emotion estimation function to make food waste reduction suggestions based on the user's emotions, thereby raising awareness. For example, the food waste reduction unit uses a generation AI to analyze the user's facial expressions and voice and make food waste reduction suggestions based on their emotions. For example, it prioritizes suggestions that the user has positive emotions about. The food waste reduction unit also uses the emotion estimation function to build a system that makes food waste reduction suggestions based on the user's emotions. For example, it suggests recipes using the user's favorite ingredients. The food waste reduction unit also uses a generation AI to accumulate user emotion data and make food waste reduction suggestions based on their emotions. For example, it makes food waste reduction suggestions based on suggestions that the user has previously made positive emotions about. In this way, it is possible to raise awareness by making food waste reduction suggestions based on the user's emotions.

[0092] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0093] The ingredient recognition unit can evaluate the freshness of ingredients through image analysis and suggest usage priorities based on freshness. For example, the generation AI analyzes images of ingredients in the refrigerator and evaluates freshness based on changes in color and shape. For example, if a tomato starts to change color, it will determine that its freshness is declining and recommend using it sooner. To evaluate the freshness of ingredients, the generation AI compares it with past image data and tracks changes in freshness. For example, if lettuce leaves begin to wilt, it will determine that its freshness is declining and recommend using it first. The ingredient recognition unit also takes environmental data such as temperature and humidity into account when evaluating the freshness of ingredients. For example, if the temperature inside the refrigerator is high, the freshness of ingredients will decline quickly, so it will recommend using them sooner. This allows the generation AI to suggest usage priorities based on the freshness of ingredients, further reducing food waste.

[0094] The ingredient recognition unit can identify the origin and cultivation method of ingredients and provide this information to the user. For example, the generation AI analyzes images of ingredients to identify the origin and cultivation method of the ingredients. For example, it reads the label of a tomato and provides the user with the origin and whether it is organically grown. In addition to recognizing ingredients, the ingredient recognition unit also retrieves information about the cultivation method of ingredients from a database and provides this to the user. For example, it displays whether lettuce was grown without pesticides. The ingredient recognition unit also adds the ability to read barcodes and QR codes when the generation AI identifies the origin and cultivation method of ingredients. For example, it scans the QR code on a cheese package to display the origin and production method. This allows the generation AI to identify the origin and cultivation method of ingredients and provide more detailed information to the user.

[0095] The ingredient recognition unit uses the emotion estimation function to analyze the user's emotions toward specific ingredients and prioritize suggesting their preferred ingredients. For example, the generation AI analyzes the user's facial expressions and voice to estimate their emotions toward specific ingredients. For example, if the user smiles when they see tomatoes, it determines that the user likes tomatoes and prioritizes suggesting them. The ingredient recognition unit also uses the emotion estimation function to analyze the user's emotions toward ingredients they have previously selected and recommends their preferred ingredients. For example, if the user showed positive emotions when selecting lettuce, it prioritizes suggesting lettuce. The ingredient recognition unit also accumulates the user's emotional data and analyzes their emotional tendencies toward specific ingredients. For example, if the user always shows positive emotions toward cheese, it prioritizes suggesting recipes that include cheese. This improves meal satisfaction by suggesting preferred ingredients based on the user's emotions.

[0096] The ingredient recognition unit can simultaneously recognize ingredients in storage locations other than the refrigerator, enabling comprehensive ingredient management. For example, the generation AI can add a function to simultaneously recognize ingredients in storage locations other than the refrigerator (such as the pantry or freezer). For example, it can recognize canned goods in the pantry and frozen foods in the freezer and create a comprehensive ingredient list. The ingredient recognition unit also builds a system in which the generation AI centrally manages ingredients in multiple storage locations. For example, it can consolidate ingredients in the refrigerator, pantry, and freezer into a single list and provide it to the user. The ingredient recognition unit also takes into account the characteristics of each storage location when the generation AI recognizes ingredients in storage locations other than the refrigerator. For example, since ingredients in the freezer can be stored for a long time, it can adjust the priority of their use. This enables comprehensive ingredient management by recognizing ingredients in storage locations other than the refrigerator.

[0097] The ingredient recognition unit can also be applied to selecting ingredients in home gardens or at markets, allowing the quality of ingredients to be evaluated before purchase. For example, the generative AI can be applied to selecting ingredients in home gardens or at markets. For example, it can analyze images of vegetables grown in home gardens and evaluate the harvest time and quality. The ingredient recognition unit also adds a function that allows the generative AI to evaluate the quality of ingredients when selecting ingredients at markets. For example, it can analyze images of tomatoes that are planned to be purchased at the market and evaluate their freshness and quality. The ingredient recognition unit also takes into account past data and environmental information when the generative AI evaluates the quality of ingredients when selecting ingredients at home gardens or at markets. For example, it evaluates quality based on weather and soil conditions. This can be applied to selecting ingredients in home gardens or at markets, allowing the quality of ingredients to be evaluated before purchase.

[0098] The recipe suggestion unit can learn the user's past eating history and suggest recipes optimized for individual preferences. For example, the generation AI learns the user's past eating history and suggests recipes optimized for individual preferences. For example, it suggests new recipes based on the user's favorite dishes in the past. The recipe suggestion unit also analyzes the user's eating history and suggests recipes that take into account preferences and allergy information. For example, it suggests recipes that avoid ingredients to which the user is allergic. The recipe suggestion unit also suggests recipes that match the season or event based on the user's eating history. For example, it suggests a recipe for this year's Christmas dinner based on the history of past Christmas dinners. In this way, by learning the user's past eating history, it is possible to suggest recipes optimized for individual preferences.

[0099] The recipe suggestion unit uses the emotion estimation function to analyze the user's mood of the day in real time and suggest recipes that match the mood. For example, the generation AI analyzes the user's facial expressions and voice in real time to analyze the user's mood of the day. For example, if the user is relaxed, it will suggest easy and relaxing recipes. The recipe suggestion unit also uses the emotion estimation function to suggest recipes that match the user's mood. For example, if the user is feeling stressed, it will suggest recipes that use ingredients that are useful for relieving stress. The recipe suggestion unit also uses the generation AI to accumulate user mood data and analyze the relationship between past moods and meals. For example, it will suggest recipes that the user liked when they wanted to relax in the past. This improves meal satisfaction by suggesting recipes that match the user's mood.

[0100] The recipe suggestion unit can be expanded to suggest special recipes tailored to specific events or seasons. For example, a function could be added whereby the generative AI suggests recipes tailored to specific events or seasons. For example, special recipes for Christmas or Halloween could be suggested. The recipe suggestion unit could also suggest recipes that take into account seasonal ingredients. For example, recipes using fresh vegetables could be suggested in spring, and cold dishes in summer. The recipe suggestion unit could also take into account the user's past history when suggesting recipes tailored to events or seasons. For example, a recipe for this year's Christmas dinner could be suggested based on the user's history of past Christmas dinners. This enriches the user's dining experience by suggesting recipes tailored to specific events or seasons.

[0101] The recipe suggestion unit can suggest recipes that take into account the preferences and allergy information of all family members. For example, the generation AI will suggest recipes that take into account the preferences and allergy information of all family members. For example, if there is someone in the family with an allergy, it will suggest recipes that avoid those ingredients. The recipe suggestion unit also suggests recipes that take into account the preferences of all family members. For example, it will suggest recipes that use ingredients that all family members like. The recipe suggestion unit also learns the eating history of all family members and suggests recipes that will satisfy everyone. For example, it will suggest new recipes based on dishes that all family members have liked in the past. In this way, by taking into account the preferences and allergy information of all family members, it is possible to suggest meals that will satisfy everyone.

[0102] The recipe suggestion unit uses the emotion estimation function to analyze the emotions of all family members and can suggest recipes that will make everyone feel positive. For example, the generation AI analyzes the facial expressions and voices of all family members to analyze their emotions. For example, it can prioritize suggesting recipes that will make all family members smile. The recipe suggestion unit also uses the emotion estimation function to analyze the emotions of all family members and suggest recipes that will make everyone feel positive. For example, it can suggest recipes that use ingredients that all family members like. The recipe suggestion unit also accumulates emotional data of all family members and analyzes the relationship between past emotions and meals. For example, it can suggest recipes that all family members have previously expressed positive emotions about. In this way, by analyzing the emotions of all family members, it can suggest recipes that will make everyone feel positive.

[0103] The processing flow of the second embodiment will be briefly explained below.

[0104] Step 1: The ingredient recognition unit recognizes the contents of the refrigerator. For example, when a user takes a photo of the contents of the refrigerator with their smartphone, the generation AI analyzes the image and recognizes the ingredients. The generation AI has been fine-tuned in advance using image data of ingredients, allowing it to recognize a variety of ingredients with high accuracy. For example, it can automatically identify and list tomatoes, lettuce, cheese, etc. in the refrigerator. Step 2: The recipe suggestion unit suggests recipes that match the user's mood and health goals for the day based on the ingredients recognized by the ingredient recognition unit. For example, the user can input a prompt such as "I want to eat a healthy salad today" into the generation AI, and the generation AI will generate a recipe based on that instruction. The generation AI will suggest a salad using tomato and lettuce, or a healthy dish using cheese. Step 3: The nutritional analysis unit analyzes the nutritional content of the recipes suggested by the recipe suggestion unit and checks the balance of the meal. For example, the generative AI calculates the amount of calories, protein, fat, and carbohydrates in the recipe and provides advice to the user, such as "This recipe is well-balanced" or "You might want to add a little more protein." Step 4: The ingredient ordering unit orders the necessary ingredients based on the results checked by the nutritional analysis unit. For example, if an ingredient required for a suggested recipe is not in the refrigerator, the user can select it within the app and order it online. This saves the user the trouble of having to go shopping. Step 5: The food waste reduction unit manages the expiration dates of the ingredients recognized by the ingredient recognition unit and makes suggestions for reducing food waste. For example, food waste can be reduced by suggesting recipes that prioritize the use of ingredients with a short expiration date.

[0105] 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.

[0106] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0107] 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.

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

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

[0110] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0111] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0112] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0113] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0114] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0115] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0116] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0117] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0118] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0119] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0120] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0121] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt 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 AI 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.

[0122] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0123] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0124] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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).

[0129] 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.

[0130] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[0131] 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.

[0132] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0133] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0134] 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.

[0135] 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.

[0136] 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 AI 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.

[0137] 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.

[0138] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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).

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0149] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0150] 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.

[0151] 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.

[0152] 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 AI 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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).

[0158] 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.

[0159] 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."

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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, in order to avoid confusion and to 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.

[0171] 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]

[0172] 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. an ingredient recognition unit that recognizes the contents of the refrigerator; a recipe suggestion unit that suggests recipes that match the mood and health goals of the day based on the ingredients recognized by the ingredient recognition unit; a nutritional analysis unit that analyzes the nutritional components of the recipes proposed by the recipe suggestion unit and checks the balance of the meal; an ingredient ordering unit that orders necessary ingredients based on the results checked by the nutritional analysis unit; a food waste reduction unit that manages the expiration dates of the ingredients recognized by the ingredient recognition unit and makes suggestions for reducing food waste. A system characterized by:

2. The ingredient recognition unit The freshness of the ingredients is evaluated using image analysis, and the priority of use is suggested based on the freshness.

2. The system of claim 1.

3. The ingredient recognition unit Identifying the origin and cultivation method of the foodstuff and providing it to the user 2. The system of claim 1.

4. The ingredient recognition unit Analyzes the user's feelings toward specific ingredients and prioritizes recommendations for preferred ingredients 2. The system of claim 1.

5. The ingredient recognition unit It also recognizes ingredients stored in places other than the refrigerator, allowing for comprehensive food management.

2. The system of claim 1.

6. The ingredient recognition unit Apply this to choosing ingredients in your home garden or at the market, and evaluate the quality of ingredients before purchasing.

2. The system of claim 1.

7. The ingredient recognition unit Monitors users' emotions in real time when choosing ingredients and suggests ingredients that evoke positive emotions 2. The system of claim 1.

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