System

Abstract

An object of a system according to an embodiment is to appropriately manage expiration dates and inventory statuses of foods and reduce food waste.SOLUTION: A system according to an embodiment includes a use-by date management unit, a stock management unit, and a consumption plan proposal unit. The use-by date management unit manages a use-by date of a food. The inventory management unit manages an inventory status of food products. A consumption plan proposal part proposes an optimum consumption plan on the basis of the data managed by the consumption time limit management part and the stock management part.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

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

[0004] Conventional technologies do not adequately manage food expiration dates and inventory status and reduce food waste, so there is room for improvement.

[0005] The system according to the embodiment aims to appropriately manage food expiration dates and inventory status, thereby reducing food waste. [Means for solving the problem]

[0006] The system according to the embodiment includes a use-by date management unit, an inventory management unit, and a consumption plan proposal unit. The use-by date management unit manages the use-by dates of food products. The inventory management unit manages the food product inventory status. The consumption plan proposal unit proposes an optimal consumption plan based on the data managed by the use-by date management unit and the inventory management unit. [Effects of the Invention]

[0007] The system according to the embodiment can appropriately manage food expiration dates and inventory status, thereby reducing food waste. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate 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 food waste reduction system according to an embodiment of the present invention is a system in which a generation AI manages food expiration dates and inventory status and proposes optimal consumption plans. As a result, the food waste reduction system can efficiently manage food expiration dates and inventory status and reduce food waste.

[0029] The food waste reduction system according to the embodiment includes a use-by date management unit, an inventory management unit, and a consumption plan proposal unit. The use-by date management unit manages the use-by dates of food products. For example, the use-by date management unit scans the barcodes of food products and registers the use-by dates in a database. The use-by date management unit can also identify food products that are close to their use-by dates. For example, the use-by date management unit calculates the use-by date based on the number of days elapsed since the date of manufacture. The inventory management unit manages the inventory status of food products. For example, the inventory management unit registers the inventory quantity of food products in a database. The inventory management unit can also identify food products with excess inventory. For example, the inventory management unit sets an inventory quantity threshold and issues an alert when the threshold is exceeded. The consumption plan proposal unit proposes an optimal consumption plan based on the data managed by the use-by date management unit and the inventory management unit. For example, the consumption plan proposal unit proposes recipes that prioritize the use-by date of food products. The consumption plan proposal unit can also propose dishes that use food products with excess inventory. For example, the consumption plan proposal unit proposes recipes taking into account the user's dietary preferences and allergy information. As a result, the food waste reduction system according to the embodiment can reduce food waste by managing food expiration dates and inventory status and proposing optimal consumption plans.

[0030] The expiration date management unit can register expiration dates and inventory information in a database by scanning food barcodes. For example, the expiration date management unit reads food barcodes with a scanner and registers expiration dates and inventory information in a database. The expiration date management unit can also scan food barcodes with a smartphone camera and register expiration dates and inventory information in a database using a dedicated app. For example, the app automatically reads the barcode and registers expiration dates and inventory information in a database. The expiration date management unit can also read food barcodes with a dedicated scanner and register expiration dates and inventory information in a database in real time. For example, the scanner reads the barcode and immediately registers the information in a database. This allows for efficient management of food expiration dates and inventory information.

[0031] The consumption plan proposal unit can propose recipes that prioritize the use of foods that are close to their expiration date. The consumption plan proposal unit, for example, proposes recipes that use foods that are close to their expiration date. For example, the generation AI identifies foods that are close to their expiration date and generates recipes that use those foods. The consumption plan proposal unit can also propose recipes that use foods that are close to their expiration date while taking into account the user's dietary preferences and allergy information. For example, the generation AI analyzes the user's dietary preferences and allergy information and generates recipes based on that. The consumption plan proposal unit can also propose recipes that use foods that are close to their expiration date while taking into account the user's nutritional balance. For example, the generation AI analyzes the user's nutritional balance and generates recipes based on that. This allows for the use of foods that are close to their expiration date to be prioritized, thereby reducing food waste.

[0032] The consumption plan proposal unit can propose dishes that use foods that are in excess inventory. The consumption plan proposal unit, for example, proposes dishes that use foods that are in excess inventory. For example, the generation AI identifies foods that are in excess inventory and generates dishes that use those foods. The consumption plan proposal unit can also propose dishes that use foods that are in excess inventory while taking into account the user's dietary preferences and allergy information. For example, the generation AI analyzes the user's dietary preferences and allergy information and generates dishes based on that. The consumption plan proposal unit can also propose dishes that use foods that are in excess inventory while taking into account the user's nutritional balance. For example, the generation AI analyzes the user's nutritional balance and generates dishes based on that. This makes it possible to effectively utilize foods that are in excess inventory, thereby reducing food waste.

[0033] The consumption plan proposal unit can suggest ways to donate to a local food bank. The consumption plan proposal unit, for example, suggests ways to donate to a local food bank. For example, the generation AI identifies food that is close to its expiration date but cannot be consumed, and suggests ways to donate that food to a local food bank. The consumption plan proposal unit can also suggest ways to donate to a local food bank while taking into consideration the user's convenience. For example, the generation AI analyzes the user's location and suggests ways to donate to the nearest food bank. The consumption plan proposal unit can also suggest ways to donate to a local food bank while taking into consideration the user's donation history. For example, the generation AI analyzes the user's donation history and suggests ways to donate based on that. In this way, food that cannot be consumed can be donated to a local food bank, thereby reducing food waste.

[0034] The consumption plan proposal unit can propose new recipes that reuse food. The consumption plan proposal unit, for example, proposes new recipes that reuse food. For example, the generation AI identifies foods that are close to their expiration date and generates new recipes that reuse those foods. The consumption plan proposal unit can also propose new recipes that reuse food taking into account the user's dietary preferences and allergy information. For example, the generation AI analyzes the user's dietary preferences and allergy information and generates new recipes based on that. The consumption plan proposal unit can also propose new recipes that reuse food taking into account the user's nutritional balance. For example, the generation AI analyzes the user's nutritional balance and generates new recipes based on that. In this way, food waste can be reduced by reusing food.

[0035] The consumption plan proposal unit can provide information about the current state of food waste and the importance of reducing it, and introduce specific reduction methods. The consumption plan proposal unit, for example, provides information about the current state of food waste and the importance of reducing it. For example, the generation AI analyzes statistical data on food waste and visualizes the current state of food waste for the user. The consumption plan proposal unit also introduces specific reduction methods. For example, the generation AI analyzes successful cases of food waste reduction and introduces them to the user. The consumption plan proposal unit can also provide educational content for reducing food waste. For example, the generation AI generates a video tutorial for reducing food waste and provides it to the user. This allows the user to understand the current state of food waste and the importance of reducing it, and to reduce food waste by practicing specific reduction methods.

[0036] The consumption plan proposal unit can regularly measure the amount of food waste at home and in stores and evaluate the reduction effect. The consumption plan proposal unit, for example, regularly measures the amount of food waste at home and in stores. For example, the generation AI regularly collects food waste data at home and evaluates the reduction effect. The consumption plan proposal unit can also regularly collect food waste data at stores and evaluate the reduction effect. For example, the generation AI regularly collects food waste data at stores and evaluates the reduction effect. The consumption plan proposal unit can also integrate food waste data at home and in stores and specifically evaluate the reduction effect. For example, the generation AI integrates food waste data at home and in stores and calculates the annual amount of food waste reduction. In this way, by regularly measuring the amount of food waste and evaluating the reduction effect, continuous improvement is possible.

[0037] The consumption plan proposal unit can manage expiration dates and inventory status not only at homes and stores, but also at farms and food processing plants, thereby reducing food waste throughout the entire supply chain. For example, the consumption plan proposal unit analyzes harvest data from farms and manages the expiration dates and inventory status of harvested food. For example, the generation AI sets expiration dates based on the number of days elapsed since the harvest date. The consumption plan proposal unit can also analyze production data from food processing plants and manage the expiration dates and inventory status of processed food. For example, the generation AI sets expiration dates based on the number of days elapsed since the processing date. The consumption plan proposal unit can also integrate data from the entire supply chain and manage the expiration dates and inventory status of food at each stage in real time. For example, the generation AI updates expiration dates based on temperature data during transportation. This makes it possible to reduce food waste throughout the entire supply chain.

[0038] The consumption plan proposal unit can propose eco-friendly consumption plans by taking into account information on food production locations and production methods. For example, the consumption plan proposal unit can analyze data on food production locations and propose plans that prioritize the consumption of locally produced foods. For example, the generation AI can propose recipes using local agricultural products. The consumption plan proposal unit can also analyze data on food production methods and propose plans that prioritize the consumption of foods produced using environmentally friendly production methods. For example, the generation AI can propose recipes using organically grown foods. The consumption plan proposal unit can also integrate data on food production locations and production methods to propose eco-friendly consumption plans. For example, the generation AI can propose plans that prioritize the consumption of foods that require short transportation distances. In this way, proposing eco-friendly consumption plans makes it possible to reduce food waste in an environmentally friendly manner.

[0039] The consumption plan proposal unit can monitor the food storage environment (temperature, humidity) in real time and suggest optimal storage conditions. For example, the generation AI can monitor the temperature and humidity inside a refrigerator in real time using sensors and suggest optimal storage conditions for each food item. The consumption plan proposal unit can also analyze temperature fluctuations inside the refrigerator and notify the user of alerts to prevent food deterioration. For example, the generation AI can issue a warning if the refrigerator door has been left open for a long period of time. The consumption plan proposal unit can also provide specific advice to extend food storage periods based on temperature and humidity data inside the refrigerator. For example, the generation AI can suggest freezing certain foods. This can help maintain food quality and reduce food waste by suggesting optimal storage conditions.

[0040] The consumption plan proposal unit can predict changes in the nutritional value and quality of food and reevaluate the expiration date. For example, the consumption plan proposal unit analyzes nutritional value data of food and predicts changes in nutritional value during the storage period. For example, the generation AI calculates the rate of decrease in vitamin C and reevaluates the expiration date. The consumption plan proposal unit can also predict changes in quality during the storage period based on food quality data and reevaluate the expiration date. For example, the generation AI measures the freshness of meat with a sensor and updates the expiration date. The consumption plan proposal unit can also analyze data on the food's storage environment and predict changes in nutritional value and quality due to storage conditions. For example, the generation AI shortens the expiration date of food stored in a high-temperature environment. In this way, food waste can be reduced by predicting changes in nutritional value and quality and reevaluating the expiration date.

[0041] The consumption plan proposal unit can propose a health-conscious consumption plan by taking into account food allergen information and health risks. The consumption plan proposal unit, for example, analyzes food allergen information and proposes a consumption plan that avoids allergy risks. For example, the generation AI proposes recipes that do not contain allergens. The consumption plan proposal unit can also propose a health-conscious consumption plan based on food health risk data. For example, the generation AI proposes a plan that prioritizes the consumption of low-calorie foods. The consumption plan proposal unit can also integrate food allergen information and health risk data to propose a specific health-conscious consumption plan. For example, the generation AI proposes nutritionally balanced recipes. In this way, by proposing a health-conscious consumption plan, it is possible to reduce food waste while protecting the user's health.

[0042] The consumption plan proposal unit can propose an economically advantageous consumption plan by taking into account food price fluctuations and market trends. The consumption plan proposal unit, for example, analyzes food price fluctuation data and proposes an economically advantageous consumption plan. For example, the generation AI proposes a plan to consume before prices rise. The consumption plan proposal unit can also identify foods with excess inventory based on market trend data and propose an economically advantageous consumption plan. For example, the generation AI proposes a plan to consume during a sale period. The consumption plan proposal unit can also integrate food price fluctuations and market trend data to propose a specific economically advantageous consumption plan. For example, the generation AI proposes a plan to consume when prices are stable. In this way, by proposing an economically advantageous consumption plan, food waste can be reduced while easing the user's financial burden.

[0043] The consumption plan proposal unit can propose a consumption plan that takes into account not only the user's dietary preferences and allergy information, but also nutritional balance and health condition. For example, the consumption plan proposal unit analyzes the user's dietary preferences and allergy information and proposes a consumption plan that takes nutritional balance into consideration. For example, the generation AI proposes nutritionally balanced recipes that do not contain allergens. The consumption plan proposal unit can also propose a health-conscious consumption plan based on the user's health condition data. For example, the generation AI proposes recipes using low-calorie foods. The consumption plan proposal unit can also integrate the user's dietary preferences, allergy information, nutritional balance, and health condition data to propose an optimal consumption plan. For example, the generation AI proposes recipes to supplement specific nutrients. In this way, by proposing a consumption plan that takes nutritional balance and health condition into consideration, it is possible to reduce food waste while protecting the user's health.

[0044] The consumption plan proposal unit can propose a consumption plan that minimizes time and effort by taking into account the user's lifestyle and schedule. The consumption plan proposal unit, for example, analyzes the user's lifestyle data and proposes a consumption plan that minimizes time and effort. For example, the generation AI proposes recipes that can be easily made on busy days. The consumption plan proposal unit can also propose a consumption plan that shortens cooking time based on the user's schedule data. For example, the generation AI proposes dishes that can be prepared the day before. The consumption plan proposal unit can also integrate the user's lifestyle and schedule data and propose a specific consumption plan that minimizes time and effort. For example, the generation AI proposes recipes that can be made in bulk on the weekend. In this way, by proposing a consumption plan that minimizes time and effort, food waste can be reduced while reducing the burden on the user.

[0045] The consumption plan proposal unit can propose consumption plans not only for homes and stores, but also for large-scale facilities such as schools and hospitals. For example, the consumption plan proposal unit analyzes school lunch data and proposes a consumption plan that prioritizes the use of foods with upcoming expiration dates. For example, the generation AI incorporates foods with upcoming expiration dates into school lunch menus. The consumption plan proposal unit can also propose a consumption plan that prioritizes the use of foods with upcoming expiration dates based on hospital meal data. For example, the generation AI incorporates foods with upcoming expiration dates into patient meal menus. The consumption plan proposal unit can also integrate meal data from large-scale facilities and propose a specific consumption plan that prioritizes the use of foods with upcoming expiration dates. For example, the generation AI optimizes the meal menu for the entire facility. This makes it possible to propose consumption plans for large-scale facilities, thereby reducing food waste on a wider scale.

[0046] The consumption plan proposal unit makes proposals that utilize local and seasonal ingredients, taking into consideration the local economy and the environment. The consumption plan proposal unit, for example, analyzes local ingredient data and proposes a consumption plan that prioritizes the use of locally produced ingredients. For example, the generation AI proposes recipes using local agricultural products. The consumption plan proposal unit can also propose a consumption plan that utilizes seasonal ingredients based on seasonal ingredient data. For example, the generation AI proposes recipes using seasonal vegetables. The consumption plan proposal unit can also integrate local ingredient and seasonal ingredient data to propose a specific consumption plan that takes into consideration the local economy and the environment. For example, the generation AI proposes eco-friendly recipes using local ingredients. In this way, by making proposals that utilize local and seasonal ingredients, food waste can be reduced while taking into consideration the local economy and the environment.

[0047] When proposing an optimal consumption plan based on expiration dates and inventory status, the consumption plan proposal unit can also suggest food cooking and storage methods to maintain food quality. For example, the consumption plan proposal unit proposes the optimal cooking method for food that is close to its expiration date. For example, the generation AI proposes a cooking method that maintains nutritional value. The consumption plan proposal unit can also propose the optimal storage method for food that is in excess stock. For example, the generation AI proposes freezing or vacuum packing. The consumption plan proposal unit can also propose specific cooking and storage methods to maintain food quality based on expiration dates and inventory status data. For example, the generation AI suggests freezing and storing specific foods. In this way, by suggesting cooking and storage methods, food quality can be maintained and food waste can be reduced.

[0048] The consumption plan proposal unit can learn the user's past consumption history and propose a more personalized consumption plan. For example, the consumption plan proposal unit analyzes the user's past consumption history data and proposes a personalized consumption plan. For example, the generation AI re-proposes recipes that were popular in the past. The consumption plan proposal unit can also learn the user's consumption patterns and propose a personalized consumption plan that prioritizes the use of foods with an approaching expiration date. For example, the generation AI prioritizes the proposal of frequently used foods. The consumption plan proposal unit can also integrate the user's past consumption history and consumption pattern data to propose a more personalized and specific consumption plan. For example, the generation AI proposes recipes to supplement specific nutrients. In this way, by learning past consumption history and proposing personalized consumption plans, user satisfaction can be improved and food waste can be reduced.

[0049] The consumption plan proposal unit can propose consumption plans not only for homes and stores, but also for food service establishments such as restaurants and cafes. For example, the consumption plan proposal unit analyzes restaurant menu data and proposes a consumption plan that prioritizes the use of foods with an approaching expiration date. For example, the generation AI incorporates foods with an approaching expiration date into the menu. The consumption plan proposal unit can also propose a consumption plan that prioritizes the use of foods with an approaching expiration date based on cafe menu data. For example, the generation AI incorporates foods with an approaching expiration date into the drink menu. The consumption plan proposal unit can also integrate restaurant menu and inventory data and propose a specific consumption plan that prioritizes the use of foods with an approaching expiration date. For example, the generation AI creates a special menu. In this way, by proposing consumption plans for restaurants, it becomes possible to reduce food waste on a wider scale.

[0050] The consumption plan proposal unit can propose a sustainable consumption plan by taking into account the energy efficiency and environmental impact of food. The consumption plan proposal unit, for example, analyzes food energy efficiency data and proposes a highly energy-efficient consumption plan. For example, the generation AI proposes recipes with short cooking times. The consumption plan proposal unit can also propose an environmentally friendly consumption plan based on food environmental impact data. For example, the generation AI proposes a plan that prioritizes the use of foods with low carbon footprints. The consumption plan proposal unit can also integrate food energy efficiency and environmental impact data to propose a specific sustainable consumption plan. For example, the generation AI proposes highly energy-efficient cooking methods. In this way, by proposing a sustainable consumption plan, it is possible to reduce food waste while being considerate of the environment.

[0051] When proposing ways to donate or reuse food that is nearing its expiration date but cannot be consumed, the consumption plan proposal unit can update information about local food banks and donation destinations in real time and suggest the most appropriate donation destination. For example, the consumption plan proposal unit collects data on local food banks in real time and suggests the most appropriate donation destination for food that is nearing its expiration date. For example, the generation AI suggests a way to donate to a nearby food bank. The consumption plan proposal unit can also update donation destination information in real time to optimize donation destinations for food that is nearing its expiration date. For example, the generation AI will prioritize suggesting donation destinations with high demand. The consumption plan proposal unit can also integrate data on local food banks and donation destinations and specifically suggest the most appropriate donation destination for food that is nearing its expiration date. For example, the generation AI will make suggestions based on the donation destination's acceptance conditions. By suggesting the most appropriate donation destination, food that cannot be consumed can be effectively utilized and food waste can be reduced.

[0052] When suggesting ways to donate or reuse food that is nearing its expiration date but cannot be consumed, the consumption plan suggestion unit can learn how to reuse food and suggest new recipes and storage methods. For example, the consumption plan suggestion unit can learn how to reuse food and suggest new recipes using food that is nearing its expiration date. For example, the generative AI can suggest creative dishes using leftovers. The consumption plan suggestion unit can also learn how to store food and suggest ways to extend the shelf life of food that is nearing its expiration date. For example, the generative AI can suggest freezing or vacuum packing. The consumption plan suggestion unit can also integrate food reuse and storage methods to suggest specific ways to reuse food that is nearing its expiration date. For example, the generative AI can suggest DIY projects using food. In this way, food waste can be reduced by learning how to reuse food and suggesting new recipes and storage methods.

[0053] The consumption plan proposal unit can propose methods for donation and reuse not only for households and stores, but also for large-scale facilities such as schools and hospitals. For example, the consumption plan proposal unit analyzes school lunch data and proposes methods for donating or reusing food that is close to its expiration date. For example, the generation AI incorporates foods that are close to their expiration date into the school lunch menu. The consumption plan proposal unit can also propose methods for donating or reusing food that is close to its expiration date based on hospital meal data. For example, the generation AI incorporates foods that are close to their expiration date into patient meal menus. The consumption plan proposal unit can also integrate meal data from large-scale facilities and propose specific methods for donating or reusing food that is close to its expiration date. For example, the generation AI optimizes the meal menu for the entire facility. This makes it possible to propose methods for donation and reuse at large-scale facilities, thereby reducing food waste on a wider scale.

[0054] The consumption plan proposal unit can propose methods for donation and reuse that utilize local and seasonal ingredients, taking into consideration the local economy and the environment. The consumption plan proposal unit, for example, analyzes local ingredient data and proposes methods for donation and reuse that prioritize the use of locally produced ingredients. For example, the generation AI proposes recipes using local agricultural products. The consumption plan proposal unit can also propose methods for donation and reuse that utilize seasonal ingredients based on seasonal ingredient data. For example, the generation AI proposes recipes using seasonal vegetables. The consumption plan proposal unit can also integrate local ingredient and seasonal ingredient data to propose specific methods for donation and reuse that take into consideration the local economy and the environment. For example, the generation AI proposes eco-friendly recipes using local ingredients. In this way, by proposing methods for donation and reuse that utilize local and seasonal ingredients, food waste can be reduced while taking into consideration the local economy and the environment.

[0055] When supporting education and awareness-raising activities for reducing food waste, the consumption plan proposal unit can provide information on the current state of food waste and the importance of reducing it, and introduce specific methods for reducing it. For example, the consumption plan proposal unit provides information on the current state of food waste and the importance of reducing it. For example, the generation AI analyzes statistical data on food waste and visualizes the current state of food waste for the user. The consumption plan proposal unit also introduces specific reduction methods. For example, the generation AI analyzes successful cases of food waste reduction and introduces them to the user. The consumption plan proposal unit can also provide educational content for reducing food waste. For example, the generation AI generates a video tutorial for reducing food waste and provides it to the user. This allows users to understand the current state of food waste and the importance of reducing it, and to reduce food waste by practicing specific reduction methods.

[0056] The consumption plan proposal unit can provide personalized educational content according to the user's interests and level of understanding. The consumption plan proposal unit, for example, analyzes the user's interest data and provides personalized educational content. For example, the generation AI provides information on reducing food waste according to the user's interests. The consumption plan proposal unit can also provide personalized educational content based on the user's understanding data. For example, the generation AI provides educational content with a level of difficulty according to the user's understanding. The consumption plan proposal unit can also integrate the user's interest and understanding data and provide specific personalized educational content. For example, the generation AI provides a video tutorial on reducing food waste according to the user's interests. In this way, providing personalized educational content can deepen the user's understanding and raise awareness of reducing food waste.

[0057] The consumption plan proposal unit can support education and awareness activities not only in households and stores, but also in large-scale facilities such as schools and hospitals. For example, the consumption plan proposal unit analyzes educational data from schools to support education and awareness activities for reducing food waste. For example, the generation AI provides teaching materials to be used in school classes. The consumption plan proposal unit can also support education and awareness activities for reducing food waste based on educational data from hospitals. For example, the generation AI provides educational content for hospital patients. The consumption plan proposal unit can also integrate educational data from large-scale facilities to support specific education and awareness activities for reducing food waste. For example, the generation AI plans an awareness event for the entire facility. By supporting education and awareness activities in large-scale facilities, it is possible to raise awareness of food waste reduction on a wider scale.

[0058] The consumption plan proposal unit can provide educational content that uses local and seasonal ingredients, taking into consideration the local economy and the environment. The consumption plan proposal unit, for example, analyzes local ingredient data and provides educational content that uses locally produced ingredients. For example, the generation AI introduces recipes using local agricultural products. The consumption plan proposal unit can also provide educational content that uses seasonal ingredients based on seasonal ingredient data. For example, the generation AI introduces cooking methods using seasonal vegetables. The consumption plan proposal unit can also integrate local ingredient and seasonal ingredient data to provide specific educational content that takes into consideration the local economy and the environment. For example, the generation AI introduces eco-friendly recipes using local ingredients. In this way, by providing educational content that uses local and seasonal ingredients, it is possible to raise awareness of reducing food waste while taking into consideration the local economy and the environment.

[0059] The consumption plan proposal unit can regularly measure the amount of food waste at home and in stores and evaluate the reduction effect. The consumption plan proposal unit, for example, regularly measures the amount of food waste at home and in stores. For example, the generation AI regularly collects food waste data at home and evaluates the reduction effect. The consumption plan proposal unit can also regularly collect food waste data at stores and evaluate the reduction effect. For example, the generation AI regularly collects food waste data at stores and evaluates the reduction effect. The consumption plan proposal unit can also integrate food waste data at home and in stores and specifically evaluate the reduction effect. For example, the generation AI integrates food waste data at home and in stores and calculates the annual amount of food waste reduction. In this way, by regularly measuring the amount of food waste and evaluating the reduction effect, continuous improvement is possible.

[0060] The consumption plan proposal unit can analyze the user's consumption patterns and behavioral data and propose effective reduction methods. The consumption plan proposal unit, for example, analyzes the user's consumption pattern data and proposes effective methods for reducing food waste. For example, the generation AI identifies foods that are frequently discarded and proposes alternatives. The consumption plan proposal unit can also provide specific advice for reducing food waste based on the user's behavioral data. For example, the generation AI proposes optimizing shopping lists. The consumption plan proposal unit can also integrate the user's consumption patterns and behavioral data and propose effective methods for reducing food waste. For example, the generation AI proposes a plan that prioritizes the use of foods with an approaching expiration date. In this way, food waste can be reduced by analyzing consumption patterns and behavioral data and proposing effective reduction methods.

[0061] The consumption plan proposal unit can monitor the effectiveness of food waste reduction not only in households and stores, but also in large-scale facilities such as schools and hospitals. For example, the consumption plan proposal unit periodically collects food waste data from schools and evaluates the reduction effectiveness. For example, the generation AI displays the amount of school lunch waste in a graph. The consumption plan proposal unit can also evaluate the reduction effectiveness based on food waste data from hospitals. For example, the generation AI provides a report on the amount of food waste thrown away by patients. The consumption plan proposal unit can also integrate food waste data from large-scale facilities and specifically evaluate the reduction effectiveness. For example, the generation AI calculates the annual food waste reduction amount for the entire facility. In this way, by monitoring the effectiveness of food waste reduction in large-scale facilities, it becomes possible to reduce food waste on a wider scale.

[0062] The consumption plan proposal unit can propose food waste reduction methods that utilize local and seasonal ingredients, taking into consideration the local economy and the environment. The consumption plan proposal unit, for example, analyzes local ingredient data and proposes food waste reduction methods that utilize locally produced ingredients. For example, the generation AI proposes recipes using local agricultural products. The consumption plan proposal unit can also propose food waste reduction methods that utilize seasonal ingredients based on seasonal ingredient data. For example, the generation AI proposes cooking methods using seasonal vegetables. The consumption plan proposal unit can also integrate local ingredient and seasonal ingredient data to propose specific food waste reduction methods that take into consideration the local economy and the environment. For example, the generation AI proposes eco-friendly recipes using local ingredients. In this way, by proposing reduction methods that utilize local and seasonal ingredients, food waste can be reduced while taking into consideration the local economy and the environment.

[0063] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0064] The consumption plan proposal unit can regularly measure the amount of food waste in households and stores and evaluate the reduction effect. For example, the generation AI can regularly collect food waste data in households and evaluate the reduction effect. The consumption plan proposal unit can also regularly collect food waste data in stores and evaluate the reduction effect. For example, the generation AI can regularly collect food waste data in stores and evaluate the reduction effect. The consumption plan proposal unit can also integrate food waste data from households and stores and specifically evaluate the reduction effect. For example, the generation AI can integrate food waste data from households and stores and calculate the annual amount of food waste reduction. This enables continuous improvement by regularly measuring the amount of food waste and evaluating the reduction effect.

[0065] The consumption plan proposal unit can monitor food storage environments (temperature, humidity) in real time and suggest optimal storage conditions. For example, the generation AI can monitor the temperature and humidity inside a refrigerator in real time using sensors and suggest optimal storage conditions for each food item. The consumption plan proposal unit can also analyze temperature fluctuations inside the refrigerator and send alerts to the user to prevent food deterioration. For example, the generation AI can issue a warning if the refrigerator door has been left open for a long period of time. The consumption plan proposal unit can also provide specific advice to extend food storage periods based on temperature and humidity data inside the refrigerator. For example, the generation AI can suggest freezing certain foods. This can help maintain food quality and reduce food waste by suggesting optimal storage conditions.

[0066] The consumption plan proposal unit can predict changes in the nutritional value and quality of food and reevaluate the expiration date. For example, the generation AI calculates the rate of vitamin C loss and reevaluates the expiration date. The consumption plan proposal unit can also predict changes in quality during storage based on food quality data and reevaluate the expiration date. For example, the generation AI measures the freshness of meat with a sensor and updates the expiration date. The consumption plan proposal unit can also analyze data on food storage environments and predict changes in nutritional value and quality due to storage conditions. For example, the generation AI shortens the expiration date of food stored in a high-temperature environment. In this way, food waste can be reduced by predicting changes in nutritional value and quality and reevaluating the expiration date.

[0067] The consumption plan proposal unit can propose economically advantageous consumption plans by taking into account food price fluctuations and market trends. For example, the generation AI can propose a plan to consume before prices rise. The consumption plan proposal unit can also identify foods with excess inventory based on market trend data and propose economically advantageous consumption plans. For example, the generation AI can propose a plan to consume during a sale period. The consumption plan proposal unit can also integrate food price fluctuations and market trend data to propose specific economically advantageous consumption plans. For example, the generation AI can propose a plan to consume when prices are stable. In this way, by proposing economically advantageous consumption plans, food waste can be reduced while easing the user's financial burden.

[0068] The consumption plan suggestion unit can propose a consumption plan that minimizes time and effort, taking into account the user's lifestyle and schedule. For example, the generation AI can propose recipes that are easy to make on busy days. The consumption plan suggestion unit can also propose a consumption plan that shortens cooking time based on the user's schedule data. For example, the generation AI can propose dishes that can be prepared the day before. The consumption plan suggestion unit can also integrate the user's lifestyle and schedule data to propose a specific consumption plan that minimizes time and effort. For example, the generation AI can propose recipes that can be prepared in advance on the weekend. This allows for the proposal of a consumption plan that minimizes time and effort, reducing food waste while easing the burden on the user.

[0069] The processing flow of the first embodiment will be briefly explained below.

[0070] Step 1: The expiration date management unit manages the expiration dates of food products. For example, the expiration date management unit scans the barcode of the food product and registers the expiration date in a database. The expiration date management unit can also identify food products that are close to their expiration date. For example, it calculates the expiration date based on the number of days that have passed since the date of production. Step 2: The inventory management unit manages the food inventory status. For example, the inventory management unit registers the food inventory quantity in a database. The inventory management unit can also identify foods with excess inventory. For example, it can set a threshold for the inventory quantity and issue an alert if the threshold is exceeded. Step 3: The consumption plan proposal unit proposes an optimal consumption plan based on the data managed by the expiration date management unit and the inventory management unit. For example, the consumption plan proposal unit proposes recipes that prioritize the use of foods with an approaching expiration date. It can also propose dishes that use foods with excess inventory. Furthermore, it proposes recipes taking into account the user's dietary preferences and allergy information.

[0071] (Example 2) The food waste reduction system according to an embodiment of the present invention is a system in which a generation AI manages food expiration dates and inventory status and proposes optimal consumption plans. As a result, the food waste reduction system can efficiently manage food expiration dates and inventory status and reduce food waste.

[0072] The food waste reduction system according to the embodiment includes a use-by date management unit, an inventory management unit, and a consumption plan proposal unit. The use-by date management unit manages the use-by dates of food products. For example, the use-by date management unit scans the barcodes of food products and registers the use-by dates in a database. The use-by date management unit can also identify food products that are close to their use-by dates. For example, the use-by date management unit calculates the use-by date based on the number of days elapsed since the date of manufacture. The inventory management unit manages the inventory status of food products. For example, the inventory management unit registers the inventory quantity of food products in a database. The inventory management unit can also identify food products with excess inventory. For example, the inventory management unit sets an inventory quantity threshold and issues an alert when the threshold is exceeded. The consumption plan proposal unit proposes an optimal consumption plan based on the data managed by the use-by date management unit and the inventory management unit. For example, the consumption plan proposal unit proposes recipes that prioritize the use-by date of food products. The consumption plan proposal unit can also propose dishes that use food products with excess inventory. For example, the consumption plan proposal unit proposes recipes taking into account the user's dietary preferences and allergy information. As a result, the food waste reduction system according to the embodiment can reduce food waste by managing food expiration dates and inventory status and proposing optimal consumption plans.

[0073] The expiration date management unit can register expiration dates and inventory information in a database by scanning food barcodes. For example, the expiration date management unit reads food barcodes with a scanner and registers expiration dates and inventory information in a database. The expiration date management unit can also scan food barcodes with a smartphone camera and register expiration dates and inventory information in a database using a dedicated app. For example, the app automatically reads the barcode and registers expiration dates and inventory information in a database. The expiration date management unit can also read food barcodes with a dedicated scanner and register expiration dates and inventory information in a database in real time. For example, the scanner reads the barcode and immediately registers the information in a database. This allows for efficient management of food expiration dates and inventory information.

[0074] The consumption plan proposal unit can propose recipes that prioritize the use of foods that are close to their expiration date. The consumption plan proposal unit, for example, proposes recipes that use foods that are close to their expiration date. For example, the generation AI identifies foods that are close to their expiration date and generates recipes that use those foods. The consumption plan proposal unit can also propose recipes that use foods that are close to their expiration date while taking into account the user's dietary preferences and allergy information. For example, the generation AI analyzes the user's dietary preferences and allergy information and generates recipes based on that. The consumption plan proposal unit can also propose recipes that use foods that are close to their expiration date while taking into account the user's nutritional balance. For example, the generation AI analyzes the user's nutritional balance and generates recipes based on that. This allows for the use of foods that are close to their expiration date to be prioritized, thereby reducing food waste.

[0075] The consumption plan proposal unit can propose dishes that use foods that are in excess inventory. The consumption plan proposal unit, for example, proposes dishes that use foods that are in excess inventory. For example, the generation AI identifies foods that are in excess inventory and generates dishes that use those foods. The consumption plan proposal unit can also propose dishes that use foods that are in excess inventory while taking into account the user's dietary preferences and allergy information. For example, the generation AI analyzes the user's dietary preferences and allergy information and generates dishes based on that. The consumption plan proposal unit can also propose dishes that use foods that are in excess inventory while taking into account the user's nutritional balance. For example, the generation AI analyzes the user's nutritional balance and generates dishes based on that. This makes it possible to effectively utilize foods that are in excess inventory, thereby reducing food waste.

[0076] The consumption plan proposal unit can suggest ways to donate to a local food bank. The consumption plan proposal unit, for example, suggests ways to donate to a local food bank. For example, the generation AI identifies food that is close to its expiration date but cannot be consumed, and suggests ways to donate that food to a local food bank. The consumption plan proposal unit can also suggest ways to donate to a local food bank while taking into consideration the user's convenience. For example, the generation AI analyzes the user's location and suggests ways to donate to the nearest food bank. The consumption plan proposal unit can also suggest ways to donate to a local food bank while taking into consideration the user's donation history. For example, the generation AI analyzes the user's donation history and suggests ways to donate based on that. In this way, food that cannot be consumed can be donated to a local food bank, thereby reducing food waste.

[0077] The consumption plan proposal unit can propose new recipes that reuse food. The consumption plan proposal unit, for example, proposes new recipes that reuse food. For example, the generation AI identifies foods that are close to their expiration date and generates new recipes that reuse those foods. The consumption plan proposal unit can also propose new recipes that reuse food taking into account the user's dietary preferences and allergy information. For example, the generation AI analyzes the user's dietary preferences and allergy information and generates new recipes based on that. The consumption plan proposal unit can also propose new recipes that reuse food taking into account the user's nutritional balance. For example, the generation AI analyzes the user's nutritional balance and generates new recipes based on that. In this way, food waste can be reduced by reusing food.

[0078] The consumption plan proposal unit can provide information about the current state of food waste and the importance of reducing it, and introduce specific reduction methods. The consumption plan proposal unit, for example, provides information about the current state of food waste and the importance of reducing it. For example, the generation AI analyzes statistical data on food waste and visualizes the current state of food waste for the user. The consumption plan proposal unit also introduces specific reduction methods. For example, the generation AI analyzes successful cases of food waste reduction and introduces them to the user. The consumption plan proposal unit can also provide educational content for reducing food waste. For example, the generation AI generates a video tutorial for reducing food waste and provides it to the user. This allows the user to understand the current state of food waste and the importance of reducing it, and to reduce food waste by practicing specific reduction methods.

[0079] The consumption plan proposal unit can regularly measure the amount of food waste at home and in stores and evaluate the reduction effect. The consumption plan proposal unit, for example, regularly measures the amount of food waste at home and in stores. For example, the generation AI regularly collects food waste data at home and evaluates the reduction effect. The consumption plan proposal unit can also regularly collect food waste data at stores and evaluate the reduction effect. For example, the generation AI regularly collects food waste data at stores and evaluates the reduction effect. The consumption plan proposal unit can also integrate food waste data at home and in stores and specifically evaluate the reduction effect. For example, the generation AI integrates food waste data at home and in stores and calculates the annual amount of food waste reduction. In this way, by regularly measuring the amount of food waste and evaluating the reduction effect, continuous improvement is possible.

[0080] The consumption plan suggestion unit can analyze the user's emotional data and make suggestions that elicit positive emotions toward foods that are close to their expiration date. The consumption plan suggestion unit, for example, analyzes the user's emotional data and makes suggestions that elicit positive emotions toward foods that are close to their expiration date. For example, the generation AI analyzes the user's emotional data and provides advice to elicit positive emotions toward foods that are close to their expiration date. The consumption plan suggestion unit can also monitor the user's emotional reactions in real time and make suggestions to reduce negative emotions toward foods that are close to their expiration date. For example, the generation AI monitors the user's emotional reactions in real time and suggests easy cooking methods to reduce stress. The consumption plan suggestion unit can also provide specific advice based on the user's emotional data and their emotions toward foods that are close to their expiration date. For example, the generation AI displays a message of gratitude based on the user's emotional data. This elicits positive emotions toward foods that are close to their expiration date, thereby reducing food waste.

[0081] The consumption plan proposal unit can monitor the user's emotional data in real time and provide advice to reduce stress associated with inventory management. The consumption plan proposal unit, for example, monitors the user's emotional data in real time and provides advice to reduce stress associated with inventory management. For example, the generation AI monitors the user's emotional data in real time and makes suggestions to reduce negative emotions associated with inventory management. The consumption plan proposal unit can also analyze the user's emotional responses and provide an interface to elicit positive emotions associated with inventory management. For example, the generation AI analyzes the user's emotional responses and displays a message of gratitude. The consumption plan proposal unit can also identify areas for improvement in inventory management based on the user's emotional data and provide specific advice based on the emotions. For example, the generation AI can suggest recipes using foods that are in high stock based on the user's emotional data. This reduces stress associated with inventory management, thereby reducing the user's burden and reducing food waste.

[0082] The consumption plan proposal unit can manage expiration dates and inventory status not only at homes and stores, but also at farms and food processing plants, thereby reducing food waste throughout the entire supply chain. For example, the consumption plan proposal unit analyzes harvest data from farms and manages the expiration dates and inventory status of harvested food. For example, the generation AI sets expiration dates based on the number of days elapsed since the harvest date. The consumption plan proposal unit can also analyze production data from food processing plants and manage the expiration dates and inventory status of processed food. For example, the generation AI sets expiration dates based on the number of days elapsed since the processing date. The consumption plan proposal unit can also integrate data from the entire supply chain and manage the expiration dates and inventory status of food at each stage in real time. For example, the generation AI updates expiration dates based on temperature data during transportation. This makes it possible to reduce food waste throughout the entire supply chain.

[0083] The consumption plan proposal unit can propose eco-friendly consumption plans by taking into account information on food production locations and production methods. For example, the consumption plan proposal unit can analyze data on food production locations and propose plans that prioritize the consumption of locally produced foods. For example, the generation AI can propose recipes using local agricultural products. The consumption plan proposal unit can also analyze data on food production methods and propose plans that prioritize the consumption of foods produced using environmentally friendly production methods. For example, the generation AI can propose recipes using organically grown foods. The consumption plan proposal unit can also integrate data on food production locations and production methods to propose eco-friendly consumption plans. For example, the generation AI can propose plans that prioritize the consumption of foods that require short transportation distances. In this way, proposing eco-friendly consumption plans makes it possible to reduce food waste in an environmentally friendly manner.

[0084] The consumption plan proposal unit can monitor the user's emotional data in real time and make suggestions for improving inventory management based on the user's emotions. For example, the consumption plan proposal unit can monitor the user's emotional data in real time and make suggestions to reduce negative emotions toward inventory management. For example, the generation AI can monitor the user's emotional data in real time and suggest simple cooking methods to reduce stress. The consumption plan proposal unit can also analyze the user's emotional responses and provide an interface to elicit positive emotions toward inventory management. For example, the generation AI can analyze the user's emotional responses and display a message of gratitude. The consumption plan proposal unit can also identify areas for improvement in inventory management based on the user's emotional data and provide specific advice based on the user's emotions. For example, the generation AI can suggest recipes using foods that are in high stock based on the user's emotional data. In this way, by making suggestions for improving inventory management based on emotions, user satisfaction can be improved and food waste can be reduced.

[0085] The consumption plan proposal unit can monitor the food storage environment (temperature, humidity) in real time and suggest optimal storage conditions. For example, the generation AI can monitor the temperature and humidity inside a refrigerator in real time using sensors and suggest optimal storage conditions for each food item. The consumption plan proposal unit can also analyze temperature fluctuations inside the refrigerator and notify the user of alerts to prevent food deterioration. For example, the generation AI can issue a warning if the refrigerator door has been left open for a long period of time. The consumption plan proposal unit can also provide specific advice to extend food storage periods based on temperature and humidity data inside the refrigerator. For example, the generation AI can suggest freezing certain foods. This can help maintain food quality and reduce food waste by suggesting optimal storage conditions.

[0086] The consumption plan proposal unit can predict changes in the nutritional value and quality of food and reevaluate the expiration date. For example, the consumption plan proposal unit analyzes nutritional value data of food and predicts changes in nutritional value during the storage period. For example, the generation AI calculates the rate of decrease in vitamin C and reevaluates the expiration date. The consumption plan proposal unit can also predict changes in quality during the storage period based on food quality data and reevaluate the expiration date. For example, the generation AI measures the freshness of meat with a sensor and updates the expiration date. The consumption plan proposal unit can also analyze data on the food's storage environment and predict changes in nutritional value and quality due to storage conditions. For example, the generation AI shortens the expiration date of food stored in a high-temperature environment. In this way, food waste can be reduced by predicting changes in nutritional value and quality and reevaluating the expiration date.

[0087] The consumption plan suggestion unit can monitor the user's emotional data in real time, analyze the emotions felt toward food that is close to its expiration date or food with excess inventory, and propose a consumption plan based on the emotions. For example, the consumption plan suggestion unit can monitor the user's emotional data in real time and make suggestions that elicit positive emotions toward food that is close to its expiration date. For example, the generation AI can analyze the user's emotional data and provide advice to elicit positive emotions toward food that is close to its expiration date. The consumption plan suggestion unit can also monitor the user's emotional responses in real time and make suggestions to reduce negative emotions toward inventory management. For example, the generation AI can monitor the user's emotional responses in real time and suggest simple cooking methods to reduce stress. The consumption plan suggestion unit can also provide an interface to elicit positive emotions toward inventory status based on the user's emotional data. For example, the generation AI can display a message of gratitude based on the user's emotional data. In this way, by proposing a consumption plan based on emotions, user satisfaction can be improved and food waste can be reduced.

[0088] The consumption plan proposal unit can propose a health-conscious consumption plan by taking into account food allergen information and health risks. The consumption plan proposal unit, for example, analyzes food allergen information and proposes a consumption plan that avoids allergy risks. For example, the generation AI proposes recipes that do not contain allergens. The consumption plan proposal unit can also propose a health-conscious consumption plan based on food health risk data. For example, the generation AI proposes a plan that prioritizes the consumption of low-calorie foods. The consumption plan proposal unit can also integrate food allergen information and health risk data to propose a specific health-conscious consumption plan. For example, the generation AI proposes nutritionally balanced recipes. In this way, by proposing a health-conscious consumption plan, it is possible to reduce food waste while protecting the user's health.

[0089] The consumption plan proposal unit can propose an economically advantageous consumption plan by taking into account food price fluctuations and market trends. The consumption plan proposal unit, for example, analyzes food price fluctuation data and proposes an economically advantageous consumption plan. For example, the generation AI proposes a plan to consume before prices rise. The consumption plan proposal unit can also identify foods with excess inventory based on market trend data and propose an economically advantageous consumption plan. For example, the generation AI proposes a plan to consume during a sale period. The consumption plan proposal unit can also integrate food price fluctuations and market trend data to propose a specific economically advantageous consumption plan. For example, the generation AI proposes a plan to consume when prices are stable. In this way, by proposing an economically advantageous consumption plan, food waste can be reduced while easing the user's financial burden.

[0090] The consumption plan suggestion unit can monitor the user's emotional data in real time and make suggestions to reduce negative emotions regarding the consumption plan. The consumption plan suggestion unit, for example, monitors the user's emotional data in real time and makes suggestions to reduce negative emotions regarding the consumption plan. For example, the generation AI monitors the user's emotional data in real time and suggests easy cooking methods to reduce stress. The consumption plan suggestion unit can also analyze the user's emotional responses and provide an interface to elicit positive emotions regarding the consumption plan. For example, the generation AI analyzes the user's emotional responses and displays a message of gratitude. The consumption plan suggestion unit can also identify areas for improvement in the consumption plan based on the user's emotional data and provide specific advice based on the emotions. For example, the generation AI can suggest recipes using foods that are in high stock based on the user's emotional data. This can improve user satisfaction and reduce food waste by reducing negative emotions regarding the consumption plan.

[0091] The consumption plan proposal unit can propose a consumption plan that takes into account not only the user's dietary preferences and allergy information, but also nutritional balance and health condition. For example, the consumption plan proposal unit analyzes the user's dietary preferences and allergy information and proposes a consumption plan that takes nutritional balance into consideration. For example, the generation AI proposes nutritionally balanced recipes that do not contain allergens. The consumption plan proposal unit can also propose a health-conscious consumption plan based on the user's health condition data. For example, the generation AI proposes recipes using low-calorie foods. The consumption plan proposal unit can also integrate the user's dietary preferences, allergy information, nutritional balance, and health condition data to propose an optimal consumption plan. For example, the generation AI proposes recipes to supplement specific nutrients. In this way, by proposing a consumption plan that takes nutritional balance and health condition into consideration, it is possible to reduce food waste while protecting the user's health.

[0092] The consumption plan proposal unit can propose a consumption plan that minimizes time and effort by taking into account the user's lifestyle and schedule. The consumption plan proposal unit, for example, analyzes the user's lifestyle data and proposes a consumption plan that minimizes time and effort. For example, the generation AI proposes recipes that can be easily made on busy days. The consumption plan proposal unit can also propose a consumption plan that shortens cooking time based on the user's schedule data. For example, the generation AI proposes dishes that can be prepared the day before. The consumption plan proposal unit can also integrate the user's lifestyle and schedule data and propose a specific consumption plan that minimizes time and effort. For example, the generation AI proposes recipes that can be made in bulk on the weekend. In this way, by proposing a consumption plan that minimizes time and effort, food waste can be reduced while reducing the burden on the user.

[0093] The consumption plan suggestion unit can monitor the user's emotional data in real time, analyze the emotions they have toward the proposed consumption plan, and make suggestions to elicit positive emotions. The consumption plan suggestion unit, for example, analyzes the user's emotional data and provides advice to elicit positive emotions toward the proposed consumption plan. For example, the generation AI suggests recipes tailored to special events. The consumption plan suggestion unit can also monitor the user's emotional responses in real time and make suggestions to reduce negative emotions toward the consumption plan. For example, the generation AI suggests simple cooking methods to reduce stress. The consumption plan suggestion unit can also provide specific advice based on the user's emotions toward the proposed consumption plan, based on the user's emotional data. For example, the generation AI displays a message of gratitude. This elicits positive emotions toward the proposed consumption plan, thereby improving user satisfaction and reducing food waste.

[0094] The consumption plan proposal unit can propose consumption plans not only for homes and stores, but also for large-scale facilities such as schools and hospitals. For example, the consumption plan proposal unit analyzes school lunch data and proposes a consumption plan that prioritizes the use of foods with upcoming expiration dates. For example, the generation AI incorporates foods with upcoming expiration dates into school lunch menus. The consumption plan proposal unit can also propose a consumption plan that prioritizes the use of foods with upcoming expiration dates based on hospital meal data. For example, the generation AI incorporates foods with upcoming expiration dates into patient meal menus. The consumption plan proposal unit can also integrate meal data from large-scale facilities and propose a specific consumption plan that prioritizes the use of foods with upcoming expiration dates. For example, the generation AI optimizes the meal menu for the entire facility. This makes it possible to propose consumption plans for large-scale facilities, thereby reducing food waste on a wider scale.

[0095] The consumption plan proposal unit makes proposals that utilize local and seasonal ingredients, taking into consideration the local economy and the environment. The consumption plan proposal unit, for example, analyzes local ingredient data and proposes a consumption plan that prioritizes the use of locally produced ingredients. For example, the generation AI proposes recipes using local agricultural products. The consumption plan proposal unit can also propose a consumption plan that utilizes seasonal ingredients based on seasonal ingredient data. For example, the generation AI proposes recipes using seasonal vegetables. The consumption plan proposal unit can also integrate local ingredient and seasonal ingredient data to propose a specific consumption plan that takes into consideration the local economy and the environment. For example, the generation AI proposes eco-friendly recipes using local ingredients. In this way, by making proposals that utilize local and seasonal ingredients, food waste can be reduced while taking into consideration the local economy and the environment.

[0096] The consumption plan suggestion unit can monitor the user's emotional data in real time, analyze the user's emotions toward the proposed consumption plan, and make suggestions for improving the consumption plan based on the emotions. For example, the consumption plan suggestion unit can monitor the user's emotional data in real time and make suggestions to reduce negative emotions toward the consumption plan. For example, the generation AI can monitor the user's emotional data in real time and suggest simple cooking methods to reduce stress. The consumption plan suggestion unit can also analyze the user's emotional responses and provide an interface to elicit positive emotions toward the consumption plan. For example, the generation AI can analyze the user's emotional responses and display a message of gratitude. The consumption plan suggestion unit can also identify areas for improvement in the consumption plan based on the user's emotional data and provide specific advice based on the emotions. For example, the generation AI can suggest recipes tailored to special events based on the user's emotional data. In this way, by making suggestions for improving the consumption plan based on emotions, it is possible to improve user satisfaction and reduce food waste.

[0097] When proposing an optimal consumption plan based on expiration dates and inventory status, the consumption plan proposal unit can also suggest food cooking and storage methods to maintain food quality. For example, the consumption plan proposal unit proposes the optimal cooking method for food that is close to its expiration date. For example, the generation AI proposes a cooking method that maintains nutritional value. The consumption plan proposal unit can also propose the optimal storage method for food that is in excess stock. For example, the generation AI proposes freezing or vacuum packing. The consumption plan proposal unit can also propose specific cooking and storage methods to maintain food quality based on expiration dates and inventory status data. For example, the generation AI suggests freezing and storing specific foods. In this way, by suggesting cooking and storage methods, food quality can be maintained and food waste can be reduced.

[0098] The consumption plan proposal unit can learn the user's past consumption history and propose a more personalized consumption plan. For example, the consumption plan proposal unit analyzes the user's past consumption history data and proposes a personalized consumption plan. For example, the generation AI re-proposes recipes that were popular in the past. The consumption plan proposal unit can also learn the user's consumption patterns and propose a personalized consumption plan that prioritizes the use of foods with an approaching expiration date. For example, the generation AI prioritizes the proposal of frequently used foods. The consumption plan proposal unit can also integrate the user's past consumption history and consumption pattern data to propose a more personalized and specific consumption plan. For example, the generation AI proposes recipes to supplement specific nutrients. In this way, by learning past consumption history and proposing personalized consumption plans, user satisfaction can be improved and food waste can be reduced.

[0099] The consumption plan suggestion unit can monitor the user's emotional data in real time, analyze the emotions they have toward the proposed consumption plan, and make suggestions to elicit positive emotions. The consumption plan suggestion unit, for example, analyzes the user's emotional data and provides advice to elicit positive emotions toward the proposed consumption plan. For example, the generation AI suggests recipes tailored to special events. The consumption plan suggestion unit can also monitor the user's emotional responses in real time and make suggestions to reduce negative emotions toward the consumption plan. For example, the generation AI suggests simple cooking methods to reduce stress. The consumption plan suggestion unit can also provide specific advice based on the user's emotions toward the proposed consumption plan, based on the user's emotional data. For example, the generation AI displays a message of gratitude. This elicits positive emotions toward the proposed consumption plan, thereby improving user satisfaction and reducing food waste.

[0100] The consumption plan proposal unit can propose consumption plans not only for homes and stores, but also for food service establishments such as restaurants and cafes. For example, the consumption plan proposal unit analyzes restaurant menu data and proposes a consumption plan that prioritizes the use of foods with an approaching expiration date. For example, the generation AI incorporates foods with an approaching expiration date into the menu. The consumption plan proposal unit can also propose a consumption plan that prioritizes the use of foods with an approaching expiration date based on cafe menu data. For example, the generation AI incorporates foods with an approaching expiration date into the drink menu. The consumption plan proposal unit can also integrate restaurant menu and inventory data and propose a specific consumption plan that prioritizes the use of foods with an approaching expiration date. For example, the generation AI creates a special menu. In this way, by proposing consumption plans for restaurants, it becomes possible to reduce food waste on a wider scale.

[0101] The consumption plan proposal unit can propose a sustainable consumption plan by taking into account the energy efficiency and environmental impact of food. The consumption plan proposal unit, for example, analyzes food energy efficiency data and proposes a highly energy-efficient consumption plan. For example, the generation AI proposes recipes with short cooking times. The consumption plan proposal unit can also propose an environmentally friendly consumption plan based on food environmental impact data. For example, the generation AI proposes a plan that prioritizes the use of foods with low carbon footprints. The consumption plan proposal unit can also integrate food energy efficiency and environmental impact data to propose a specific sustainable consumption plan. For example, the generation AI proposes highly energy-efficient cooking methods. In this way, by proposing a sustainable consumption plan, it is possible to reduce food waste while being considerate of the environment.

[0102] The consumption plan suggestion unit can monitor the user's emotional data in real time, analyze the user's emotions toward the proposed consumption plan, and make suggestions for improving the consumption plan based on the emotions. For example, the consumption plan suggestion unit can monitor the user's emotional data in real time and make suggestions to reduce negative emotions toward the consumption plan. For example, the generation AI can monitor the user's emotional data in real time and suggest simple cooking methods to reduce stress. The consumption plan suggestion unit can also analyze the user's emotional responses and provide an interface to elicit positive emotions toward the consumption plan. For example, the generation AI can analyze the user's emotional responses and display a message of gratitude. The consumption plan suggestion unit can also identify areas for improvement in the consumption plan based on the user's emotional data and provide specific advice based on the emotions. For example, the generation AI can suggest recipes tailored to special events based on the user's emotional data. In this way, by making suggestions for improving the consumption plan based on emotions, it is possible to improve user satisfaction and reduce food waste.

[0103] When proposing ways to donate or reuse food that is nearing its expiration date but cannot be consumed, the consumption plan proposal unit can update information about local food banks and donation destinations in real time and suggest the most appropriate donation destination. For example, the consumption plan proposal unit collects data on local food banks in real time and suggests the most appropriate donation destination for food that is nearing its expiration date. For example, the generation AI suggests a way to donate to a nearby food bank. The consumption plan proposal unit can also update donation destination information in real time to optimize donation destinations for food that is nearing its expiration date. For example, the generation AI will prioritize suggesting donation destinations with high demand. The consumption plan proposal unit can also integrate data on local food banks and donation destinations and specifically suggest the most appropriate donation destination for food that is nearing its expiration date. For example, the generation AI will make suggestions based on the donation destination's acceptance conditions. By suggesting the most appropriate donation destination, food that cannot be consumed can be effectively utilized and food waste can be reduced.

[0104] When suggesting ways to donate or reuse food that is nearing its expiration date but cannot be consumed, the consumption plan suggestion unit can learn how to reuse food and suggest new recipes and storage methods. For example, the consumption plan suggestion unit can learn how to reuse food and suggest new recipes using food that is nearing its expiration date. For example, the generative AI can suggest creative dishes using leftovers. The consumption plan suggestion unit can also learn how to store food and suggest ways to extend the shelf life of food that is nearing its expiration date. For example, the generative AI can suggest freezing or vacuum packing. The consumption plan suggestion unit can also integrate food reuse and storage methods to suggest specific ways to reuse food that is nearing its expiration date. For example, the generative AI can suggest DIY projects using food. In this way, food waste can be reduced by learning how to reuse food and suggesting new recipes and storage methods.

[0105] The consumption plan suggestion unit can analyze the user's emotional data and provide advice to elicit positive emotions toward suggestions for donating or reusing. The consumption plan suggestion unit can, for example, analyze the user's emotional data and provide advice to elicit positive emotions toward suggestions for donating or reusing. For example, the generation AI can introduce successful donation cases. The consumption plan suggestion unit can also monitor the user's emotional responses in real time and make suggestions to reduce negative emotions toward donating or reusing. For example, the generation AI can simplify the donation procedure. The consumption plan suggestion unit can also provide specific advice based on the user's emotions toward suggestions for donating or reusing, based on the user's emotional data. For example, the generation AI can display a message of gratitude. This can elicit positive emotions toward suggestions for donating or reusing, thereby improving user satisfaction and reducing food waste.

[0106] The consumption plan proposal unit can propose methods for donation and reuse not only for households and stores, but also for large-scale facilities such as schools and hospitals. For example, the consumption plan proposal unit analyzes school lunch data and proposes methods for donating or reusing food that is close to its expiration date. For example, the generation AI incorporates foods that are close to their expiration date into the school lunch menu. The consumption plan proposal unit can also propose methods for donating or reusing food that is close to its expiration date based on hospital meal data. For example, the generation AI incorporates foods that are close to their expiration date into patient meal menus. The consumption plan proposal unit can also integrate meal data from large-scale facilities and propose specific methods for donating or reusing food that is close to its expiration date. For example, the generation AI optimizes the meal menu for the entire facility. This makes it possible to propose methods for donation and reuse at large-scale facilities, thereby reducing food waste on a wider scale.

[0107] The consumption plan proposal unit can propose methods for donation and reuse that utilize local and seasonal ingredients, taking into consideration the local economy and the environment. The consumption plan proposal unit, for example, analyzes local ingredient data and proposes methods for donation and reuse that prioritize the use of locally produced ingredients. For example, the generation AI proposes recipes using local agricultural products. The consumption plan proposal unit can also propose methods for donation and reuse that utilize seasonal ingredients based on seasonal ingredient data. For example, the generation AI proposes recipes using seasonal vegetables. The consumption plan proposal unit can also integrate local ingredient and seasonal ingredient data to propose specific methods for donation and reuse that take into consideration the local economy and the environment. For example, the generation AI proposes eco-friendly recipes using local ingredients. In this way, by proposing methods for donation and reuse that utilize local and seasonal ingredients, food waste can be reduced while taking into consideration the local economy and the environment.

[0108] The consumption plan suggestion unit can monitor the user's emotional data in real time, analyze the emotions they have toward suggestions for donation or reuse, and make suggestions for improving donation or reuse based on their emotions. For example, the consumption plan suggestion unit can monitor the user's emotional data in real time and make suggestions to reduce negative emotions toward donation or reuse. For example, the generation AI can simplify the donation procedure. The consumption plan suggestion unit can also analyze the user's emotional responses and provide an interface to elicit positive emotions toward donation or reuse. For example, the generation AI can display a message of gratitude. The consumption plan suggestion unit can also identify areas for improvement in donation or reuse based on the user's emotional data and provide specific advice based on their emotions. For example, the generation AI can introduce successful donation examples. This can improve user satisfaction and reduce food waste by making suggestions for improving donation or reuse based on their emotions.

[0109] When supporting education and awareness-raising activities for reducing food waste, the consumption plan proposal unit can provide information on the current state of food waste and the importance of reducing it, and introduce specific methods for reducing it. For example, the consumption plan proposal unit provides information on the current state of food waste and the importance of reducing it. For example, the generation AI analyzes statistical data on food waste and visualizes the current state of food waste for the user. The consumption plan proposal unit also introduces specific reduction methods. For example, the generation AI analyzes successful cases of food waste reduction and introduces them to the user. The consumption plan proposal unit can also provide educational content for reducing food waste. For example, the generation AI generates a video tutorial for reducing food waste and provides it to the user. This allows users to understand the current state of food waste and the importance of reducing it, and to reduce food waste by practicing specific reduction methods.

[0110] The consumption plan proposal unit can provide personalized educational content according to the user's interests and level of understanding. The consumption plan proposal unit, for example, analyzes the user's interest data and provides personalized educational content. For example, the generation AI provides information on reducing food waste according to the user's interests. The consumption plan proposal unit can also provide personalized educational content based on the user's understanding data. For example, the generation AI provides educational content with a level of difficulty according to the user's understanding. The consumption plan proposal unit can also integrate the user's interest and understanding data and provide specific personalized educational content. For example, the generation AI provides a video tutorial on reducing food waste according to the user's interests. In this way, providing personalized educational content can deepen the user's understanding and raise awareness of reducing food waste.

[0111] The consumption plan suggestion unit can analyze the user's emotional data and provide advice to elicit positive emotions toward education and awareness activities. For example, the generation AI can introduce success stories. The consumption plan suggestion unit can also monitor the user's emotional responses in real time and make suggestions to reduce negative emotions toward education and awareness activities. For example, the generation AI can adjust the difficulty level of educational content. The consumption plan suggestion unit can also provide specific advice based on the user's emotions toward education and awareness activities, based on the user's emotional data. For example, the generation AI can display a message of gratitude. This can elicit positive emotions toward education and awareness activities, thereby increasing the user's understanding and interest and raising awareness of food waste reduction.

[0112] The consumption plan proposal unit can support education and awareness activities not only in households and stores, but also in large-scale facilities such as schools and hospitals. For example, the consumption plan proposal unit analyzes educational data from schools to support education and awareness activities for reducing food waste. For example, the generation AI provides teaching materials to be used in school classes. The consumption plan proposal unit can also support education and awareness activities for reducing food waste based on educational data from hospitals. For example, the generation AI provides educational content for hospital patients. The consumption plan proposal unit can also integrate educational data from large-scale facilities to support specific education and awareness activities for reducing food waste. For example, the generation AI plans an awareness event for the entire facility. By supporting education and awareness activities in large-scale facilities, it is possible to raise awareness of food waste reduction on a wider scale.

[0113] The consumption plan proposal unit can provide educational content that uses local and seasonal ingredients, taking into consideration the local economy and the environment. The consumption plan proposal unit, for example, analyzes local ingredient data and provides educational content that uses locally produced ingredients. For example, the generation AI introduces recipes using local agricultural products. The consumption plan proposal unit can also provide educational content that uses seasonal ingredients based on seasonal ingredient data. For example, the generation AI introduces cooking methods using seasonal vegetables. The consumption plan proposal unit can also integrate local ingredient and seasonal ingredient data to provide specific educational content that takes into consideration the local economy and the environment. For example, the generation AI introduces eco-friendly recipes using local ingredients. In this way, by providing educational content that uses local and seasonal ingredients, it is possible to raise awareness of reducing food waste while taking into consideration the local economy and the environment.

[0114] The consumption plan suggestion unit can monitor the user's emotional data in real time, analyze their feelings toward education and awareness activities, and make suggestions for improving education and awareness activities based on their emotions. For example, the consumption plan suggestion unit can monitor the user's emotional data in real time and make suggestions to reduce negative feelings toward education and awareness activities. For example, the generation AI can adjust the difficulty level of the educational content. The consumption plan suggestion unit can also analyze the user's emotional responses and provide an interface to elicit positive feelings toward education and awareness activities. For example, the generation AI can display a message of gratitude. The consumption plan suggestion unit can also identify areas for improvement in education and awareness activities based on the user's emotional data and provide specific advice based on their emotions. For example, the generation AI can introduce success stories. This can improve user satisfaction and raise awareness of food waste reduction by making suggestions for improving education and awareness activities based on their emotions.

[0115] The consumption plan proposal unit can regularly measure the amount of food waste at home and in stores and evaluate the reduction effect. The consumption plan proposal unit, for example, regularly measures the amount of food waste at home and in stores. For example, the generation AI regularly collects food waste data at home and evaluates the reduction effect. The consumption plan proposal unit can also regularly collect food waste data at stores and evaluate the reduction effect. For example, the generation AI regularly collects food waste data at stores and evaluates the reduction effect. The consumption plan proposal unit can also integrate food waste data at home and in stores and specifically evaluate the reduction effect. For example, the generation AI integrates food waste data at home and in stores and calculates the annual amount of food waste reduction. In this way, by regularly measuring the amount of food waste and evaluating the reduction effect, continuous improvement is possible.

[0116] The consumption plan proposal unit can analyze the user's consumption patterns and behavioral data and propose effective reduction methods. The consumption plan proposal unit, for example, analyzes the user's consumption pattern data and proposes effective methods for reducing food waste. For example, the generation AI identifies foods that are frequently discarded and proposes alternatives. The consumption plan proposal unit can also provide specific advice for reducing food waste based on the user's behavioral data. For example, the generation AI proposes optimizing shopping lists. The consumption plan proposal unit can also integrate the user's consumption patterns and behavioral data and propose effective methods for reducing food waste. For example, the generation AI proposes a plan that prioritizes the use of foods with an approaching expiration date. In this way, food waste can be reduced by analyzing consumption patterns and behavioral data and proposing effective reduction methods.

[0117] The consumption plan proposal unit can analyze the user's emotional data and provide advice to elicit positive emotions regarding the effects of food waste reduction. The consumption plan proposal unit can, for example, analyze the user's emotional data and provide advice to elicit positive emotions regarding the effects of food waste reduction. For example, the generation AI visualizes the reduction effects. The consumption plan proposal unit can also monitor the user's emotional responses in real time and make suggestions to alleviate negative emotions regarding food waste reduction. For example, the generation AI can introduce success stories. The consumption plan proposal unit can also provide specific advice based on the user's emotions regarding the effects of food waste reduction, based on the user's emotional data. For example, the generation AI can display a message of gratitude. This can elicit positive emotions regarding the effects of food waste reduction, improving user satisfaction and promoting continuous food waste reduction.

[0118] The consumption plan proposal unit can monitor the effectiveness of food waste reduction not only in households and stores, but also in large-scale facilities such as schools and hospitals. For example, the consumption plan proposal unit periodically collects food waste data from schools and evaluates the reduction effectiveness. For example, the generation AI displays the amount of school lunch waste in a graph. The consumption plan proposal unit can also evaluate the reduction effectiveness based on food waste data from hospitals. For example, the generation AI provides a report on the amount of food waste thrown away by patients. The consumption plan proposal unit can also integrate food waste data from large-scale facilities and specifically evaluate the reduction effectiveness. For example, the generation AI calculates the annual food waste reduction amount for the entire facility. In this way, by monitoring the effectiveness of food waste reduction in large-scale facilities, it becomes possible to reduce food waste on a wider scale.

[0119] The consumption plan proposal unit can propose food waste reduction methods that utilize local and seasonal ingredients, taking into consideration the local economy and the environment. The consumption plan proposal unit, for example, analyzes local ingredient data and proposes food waste reduction methods that utilize locally produced ingredients. For example, the generation AI proposes recipes using local agricultural products. The consumption plan proposal unit can also propose food waste reduction methods that utilize seasonal ingredients based on seasonal ingredient data. For example, the generation AI proposes cooking methods using seasonal vegetables. The consumption plan proposal unit can also integrate local ingredient and seasonal ingredient data to propose specific food waste reduction methods that take into consideration the local economy and the environment. For example, the generation AI proposes eco-friendly recipes using local ingredients. In this way, by proposing reduction methods that utilize local and seasonal ingredients, food waste can be reduced while taking into consideration the local economy and the environment.

[0120] The consumption plan proposal unit can monitor the user's emotional data in real time, analyze their feelings about the effects of food waste reduction, and make suggestions for improving reduction methods based on their emotions. The consumption plan proposal unit, for example, monitors the user's emotional data in real time and makes suggestions to alleviate negative feelings about food waste reduction. For example, the generation AI visualizes the reduction effects. The consumption plan proposal unit can also analyze the user's emotional responses and provide an interface to elicit positive feelings about food waste reduction. For example, the generation AI displays a message of gratitude. The consumption plan proposal unit can also provide specific advice based on the user's feelings about the effects of food waste reduction based on the user's emotional data. For example, the generation AI introduces success stories. In this way, by making suggestions for improving reduction methods based on emotions, it is possible to improve user satisfaction and promote continuous food waste reduction.

[0121] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0122] The consumption plan suggestion unit can analyze the user's emotional data and make suggestions to elicit positive emotions toward food that is close to its expiration date. For example, the generation AI can analyze the user's emotional data and provide advice to elicit positive emotions toward food that is close to its expiration date. The consumption plan suggestion unit can also monitor the user's emotional reactions in real time and make suggestions to reduce negative emotions toward food that is close to its expiration date. For example, the generation AI can monitor the user's emotional reactions in real time and suggest simple cooking methods to reduce stress. The consumption plan suggestion unit can also provide specific advice based on the user's emotional data regarding food that is close to its expiration date. For example, the generation AI can display a message of gratitude based on the user's emotional data. This can elicit positive emotions toward food that is close to its expiration date, thereby reducing food waste.

[0123] The consumption plan suggestion unit can monitor the user's emotional data in real time and provide advice to reduce stress regarding inventory management. For example, the generation AI can monitor the user's emotional data in real time and make suggestions to reduce negative emotions regarding inventory management. The consumption plan suggestion unit can also analyze the user's emotional responses and provide an interface to elicit positive emotions regarding inventory management. For example, the generation AI can analyze the user's emotional responses and display a message of gratitude. The consumption plan suggestion unit can also identify areas for improvement in inventory management based on the user's emotional data and provide specific advice based on the emotions. For example, the generation AI can suggest recipes using foods that have high inventory levels based on the user's emotional data. This reduces stress regarding inventory management, thereby reducing the user's burden and reducing food waste.

[0124] The consumption plan proposal unit can monitor the user's emotional data in real time and make suggestions for improving inventory management based on their emotions. For example, the generation AI can monitor the user's emotional data in real time and make suggestions to reduce negative emotions toward inventory management. The consumption plan proposal unit can also analyze the user's emotional responses and provide an interface to elicit positive emotions toward inventory management. For example, the generation AI can analyze the user's emotional responses and display a message of gratitude. The consumption plan proposal unit can also identify areas for improvement in inventory management based on the user's emotional data and provide specific advice based on their emotions. For example, the generation AI can suggest recipes using foods that have a high inventory based on the user's emotional data. In this way, by making suggestions for improving inventory management based on emotions, it is possible to improve user satisfaction and reduce food waste.

[0125] The consumption plan suggestion unit can monitor the user's emotional data in real time, analyze the emotions they have toward the proposed consumption plan, and make suggestions to elicit positive emotions. For example, the generation AI analyzes the user's emotional data and provides advice to elicit positive emotions toward the proposed consumption plan. The consumption plan suggestion unit can also monitor the user's emotional responses in real time and make suggestions to reduce negative emotions toward the consumption plan. For example, the generation AI can suggest simple cooking methods to reduce stress. The consumption plan suggestion unit can also provide specific advice based on the user's emotions toward the proposed consumption plan, based on the user's emotional data. For example, the generation AI can display a message of gratitude. This elicits positive emotions toward the proposed consumption plan, thereby improving user satisfaction and reducing food waste.

[0126] The consumption plan suggestion unit can monitor the user's emotional data in real time, analyze the emotions they have toward suggestions for donating or reusing, and make suggestions for improving donations or reuse based on their emotions. For example, the generation AI can monitor the user's emotional data in real time and make suggestions to reduce negative emotions toward donating or reusing. The consumption plan suggestion unit can also analyze the user's emotional responses and provide an interface to elicit positive emotions toward donating or reusing. For example, the generation AI can display a message of gratitude. The consumption plan suggestion unit can also identify areas for improvement in donations or reuse based on the user's emotional data and provide specific advice based on their emotions. For example, the generation AI can introduce successful donation examples. This can improve user satisfaction and reduce food waste by making suggestions for improving donations or reuse based on their emotions.

[0127] The consumption plan proposal unit can regularly measure the amount of food waste in households and stores and evaluate the reduction effect. For example, the generation AI can regularly collect food waste data in households and evaluate the reduction effect. The consumption plan proposal unit can also regularly collect food waste data in stores and evaluate the reduction effect. For example, the generation AI can regularly collect food waste data in stores and evaluate the reduction effect. The consumption plan proposal unit can also integrate food waste data from households and stores and specifically evaluate the reduction effect. For example, the generation AI can integrate food waste data from households and stores and calculate the annual amount of food waste reduction. This enables continuous improvement by regularly measuring the amount of food waste and evaluating the reduction effect.

[0128] The consumption plan proposal unit can monitor food storage environments (temperature, humidity) in real time and suggest optimal storage conditions. For example, the generation AI can monitor the temperature and humidity inside a refrigerator in real time using sensors and suggest optimal storage conditions for each food item. The consumption plan proposal unit can also analyze temperature fluctuations inside the refrigerator and send alerts to the user to prevent food deterioration. For example, the generation AI can issue a warning if the refrigerator door has been left open for a long period of time. The consumption plan proposal unit can also provide specific advice to extend food storage periods based on temperature and humidity data inside the refrigerator. For example, the generation AI can suggest freezing certain foods. This can help maintain food quality and reduce food waste by suggesting optimal storage conditions.

[0129] The consumption plan proposal unit can predict changes in the nutritional value and quality of food and reevaluate the expiration date. For example, the generation AI calculates the rate of vitamin C loss and reevaluates the expiration date. The consumption plan proposal unit can also predict changes in quality during storage based on food quality data and reevaluate the expiration date. For example, the generation AI measures the freshness of meat with a sensor and updates the expiration date. The consumption plan proposal unit can also analyze data on food storage environments and predict changes in nutritional value and quality due to storage conditions. For example, the generation AI shortens the expiration date of food stored in a high-temperature environment. In this way, food waste can be reduced by predicting changes in nutritional value and quality and reevaluating the expiration date.

[0130] The consumption plan proposal unit can propose economically advantageous consumption plans by taking into account food price fluctuations and market trends. For example, the generation AI can propose a plan to consume before prices rise. The consumption plan proposal unit can also identify foods with excess inventory based on market trend data and propose economically advantageous consumption plans. For example, the generation AI can propose a plan to consume during a sale period. The consumption plan proposal unit can also integrate food price fluctuations and market trend data to propose specific economically advantageous consumption plans. For example, the generation AI can propose a plan to consume when prices are stable. In this way, by proposing economically advantageous consumption plans, food waste can be reduced while easing the user's financial burden.

[0131] The consumption plan suggestion unit can propose a consumption plan that minimizes time and effort, taking into account the user's lifestyle and schedule. For example, the generation AI can propose recipes that are easy to make on busy days. The consumption plan suggestion unit can also propose a consumption plan that shortens cooking time based on the user's schedule data. For example, the generation AI can propose dishes that can be prepared the day before. The consumption plan suggestion unit can also integrate the user's lifestyle and schedule data to propose a specific consumption plan that minimizes time and effort. For example, the generation AI can propose recipes that can be prepared in advance on the weekend. This allows for the proposal of a consumption plan that minimizes time and effort, reducing food waste while easing the burden on the user.

[0132] The processing flow of the second embodiment will be briefly explained below.

[0133] Step 1: The expiration date management unit manages the expiration dates of food products. For example, the expiration date management unit scans the barcode of the food product and registers the expiration date in a database. The expiration date management unit can also identify food products that are close to their expiration date. For example, it calculates the expiration date based on the number of days that have passed since the date of production. Step 2: The inventory management unit manages the food inventory status. For example, the inventory management unit registers the food inventory quantity in a database. The inventory management unit can also identify foods with excess inventory. For example, it can set a threshold for the inventory quantity and issue an alert if the threshold is exceeded. Step 3: The consumption plan proposal unit proposes an optimal consumption plan based on the data managed by the expiration date management unit and the inventory management unit. For example, the consumption plan proposal unit proposes recipes that prioritize the use of foods with an approaching expiration date. It can also propose dishes that use foods with excess inventory. Furthermore, it proposes recipes taking into account the user's dietary preferences and allergy information.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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).

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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).

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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).

[0187] 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.

[0188] 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."

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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]

[0201] 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 expiration date management department that manages the expiration dates of food products; Inventory control department that manages food inventory status; a consumption plan proposal unit that proposes an optimal consumption plan based on the data managed by the expiration date management unit and the inventory management unit. A system characterized by:

2. The expiration date management unit Scan food barcodes to register expiration dates and inventory information in a database 2. The system of claim 1.

3. The consumption plan proposal unit Suggest recipes that prioritize the use of foods with a short expiration date 2. The system of claim 1.

4. The consumption plan proposal unit Suggest recipes that use overstocked foods 2. The system of claim 1.

5. The consumption plan proposal unit Offer ways to donate to local food banks 2. The system of claim 1.

6. The consumption plan proposal unit Propose new recipes using recycled food 2. The system of claim 1.

Images