system

Abstract

The system according to this embodiment aims to reduce food waste by providing personalized meal plans to individual users. [Solution] The system according to the embodiment comprises a planning unit, a list creation unit, a nutrition management unit, and a waste reduction unit. The planning unit learns the nutritional balance, preferences, and allergies of individual users and proposes a personalized meal plan. The list creation unit creates a shopping list based on the meal plan proposed by the planning unit. The nutrition management unit manages inventory and provides recipes based on the shopping list created by the list creation unit. The waste reduction unit manages surplus food information from food suppliers and distributes it to appropriate recipients.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, a meal plan tailored to the nutritional balance and preferences of individual users and the reduction of food loss have not been sufficiently carried out, and there is room for improvement.

[0005] The system according to the embodiment aims to provide a personalized meal plan for individual users and reduce food loss.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a planning unit, a list creation unit, a nutrition management unit, and a waste reduction unit. The planning unit learns the nutritional balance, preferences, and allergies of individual users and proposes a personalized meal plan. The list creation unit creates a shopping list based on the meal plan proposed by the planning unit. The nutrition management unit manages inventory and provides recipes based on the shopping list created by the list creation unit. The waste reduction unit manages information on surplus food from food suppliers and distributes it to appropriate recipients. [Effects of the Invention]

[0007] The system according to this embodiment can provide personalized meal plans to individual users and reduce food waste. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

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

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

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

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The AI ​​agent system according to an embodiment of the present invention is a system that comprehensively supports all issues related to "food," such as meal planning, shopping list creation, nutrition management, food waste reduction, community-based cooking classes, and agricultural management. This AI agent system learns the nutritional balance, preferences, allergies, etc. of individual users in real time and proposes personalized meal plans. For example, the AI ​​agent system can provide a meal plan that takes into account the optimal nutritional balance based on the user's eating history and health condition. Next, the AI ​​agent system creates a shopping list based on that meal plan and also manages inventory and provides recipes. For example, the AI ​​agent system can manage the inventory in the user's refrigerator and list the necessary ingredients. In addition, the AI ​​agent system centrally manages surplus food information from food suppliers such as food companies, supermarkets, and retailers and distributes it to the most suitable recipients in real time. For example, the AI ​​agent system can reduce food waste by distributing surplus food to welfare projects and food banks. Furthermore, the AI ​​agent system forms online communities and holds cooking classes tailored to each household. For example, the AI ​​agent system can provide customized cooking classes according to the skill level and preferences of the participants. For farmers, the AI ​​agent system develops optimal cultivation plans based on weather data, cultivation data, and sales data, and provides real-time advice. For example, the AI ​​agent system can suggest the optimal planting and harvesting times to farmers. This enables efficient production and supply of food ingredients. In addition, the AI ​​agent system provides healthy eating in a gamified way, allowing users to enjoy a healthy diet. For example, the AI ​​agent system visualizes daily score management and results, and allows users to compete with family and friends. In this way, the AI ​​agent system can comprehensively support users' eating habits and provide a healthy diet.

[0029] The AI ​​agent system according to this embodiment comprises a planning unit, a list creation unit, a nutrition management unit, and a waste reduction unit. The planning unit learns the nutritional balance, preferences, and allergies of individual users and proposes a personalized meal plan. For example, the planning unit provides a meal plan that considers the optimal nutritional balance based on the user's eating history and health condition. The planning unit can use AI to learn user data in real time and optimize the meal plan. The list creation unit creates a shopping list based on the meal plan proposed by the planning unit. For example, the list creation unit manages the inventory in the user's refrigerator and lists the necessary ingredients. The list creation unit can use AI to optimize the user's shopping list. The nutrition management unit performs inventory management and provides recipes based on the shopping list created by the list creation unit. For example, the nutrition management unit manages the inventory in the user's refrigerator and lists the necessary ingredients. The nutrition management unit can use AI to optimize the user's inventory management and recipe provision. The waste reduction unit manages surplus food information from food suppliers and distributes it to the most suitable recipients. The waste reduction unit distributes surplus food to welfare programs and food banks, for example. The waste reduction unit can optimize the distribution of surplus food using AI. As a result, the AI ​​agent system according to the embodiment can efficiently provide personalized meal plans based on the user's nutritional balance, preferences, and allergies, and can create shopping lists, manage inventory, provide recipes, and distribute surplus food.

[0030] The planning department learns each user's nutritional balance, preferences, and allergies to propose personalized meal plans. Specifically, users input their daily meal details into the application, and the planning department collects this data and analyzes the user's eating history in detail. Furthermore, it also collects information on the user's health status, taking into account health indicators such as blood pressure, blood sugar levels, and weight. This allows the planning department to provide an optimal nutritional balance tailored to the user's health condition. The AI ​​learns this data in real time and generates meal plans that reflect the user's preferences and allergies. For example, if a user is allergic to a particular ingredient, it will suggest recipes that do not include that ingredient. It can also offer a variety of menus, such as Japanese, Western, and Chinese cuisine, according to the user's preferences. In addition, the planning department can propose meal plans that take into account seasonal and regional specialties, supporting a varied and enjoyable eating lifestyle for the user. In this way, the planning department can balance maintaining the user's health with enjoying food.

[0031] The list creation unit generates shopping lists based on meal plans proposed by the planning unit. Specifically, it utilizes sensors and cameras inside the refrigerator to manage the inventory in the user's refrigerator and list the necessary ingredients. This allows the list creation unit to understand the quantity and expiration dates of ingredients in the refrigerator, enabling it to generate efficient shopping lists. The AI ​​learns the user's past shopping history and consumption patterns to predict necessary ingredients. For example, it prioritizes listing ingredients that the user frequently uses or ingredients needed for specific dishes. The list creation unit can also adjust the contents of the shopping list according to the user's lifestyle and meal frequency. For example, it lists small amounts of ingredients for users living alone and large amounts for large families. Furthermore, the list creation unit can utilize sale information and coupons to reduce the user's shopping costs. In this way, the list creation unit can provide efficient and economical shopping lists, improving the user's shopping experience.

[0032] The Nutrition Management Department manages inventory and provides recipes based on shopping lists created by the List Creation Department. Specifically, it utilizes sensors and cameras inside the refrigerator to manage the inventory in the user's refrigerator and list the necessary ingredients. This allows the Nutrition Management Department to understand the quantity and expiration dates of ingredients in the refrigerator, enabling efficient inventory management. AI learns the user's past consumption patterns and meal history to suggest optimal recipes. For example, it provides easy-to-make recipes and nutritionally balanced recipes based on the ingredients in the refrigerator. The Nutrition Management Department can also suggest recipes that take into account the user's health condition and nutritional balance. For example, it provides low-calorie recipes to users on a diet and high-protein recipes to users aiming to build muscle. Furthermore, the Nutrition Management Department can collect user satisfaction with meals and feedback to continuously improve the recipes. This allows the Nutrition Management Department to balance maintaining the user's health with enjoying food.

[0033] The Loss Reduction Department manages information on surplus food from food suppliers and distributes it to the most suitable recipients. Specifically, it uses AI to collect information on surplus food provided by food suppliers and distribute it to appropriate recipients such as welfare organizations and food banks. The AI ​​analyzes information such as the type and quantity of surplus food and its expiration date to identify the optimal recipient. For example, it prioritizes the distribution of food nearing its expiration date, minimizing food waste. The Loss Reduction Department can also distribute efficiently by considering the demand and receiving capacity of recipients. For example, it can grasp the inventory status and demand of welfare organizations and food banks in real time and distribute the necessary food at the appropriate time. Furthermore, the Loss Reduction Department can monitor the distribution status of surplus food and evaluate the effectiveness of the distribution. In this way, the Loss Reduction Department can reduce food waste and make a social contribution. In addition, the Loss Reduction Department can strengthen cooperation with food suppliers and recipients and build a sustainable food supply chain. In this way, the Loss Reduction Department can distribute surplus food efficiently and effectively, achieving both food waste reduction and social contribution.

[0034] The planning department can learn the user's nutritional balance, preferences, and allergies in real time. For example, the planning department provides a meal plan that considers the optimal nutritional balance based on the user's eating history and health status. The planning department can use AI to learn user data in real time and optimize meal plans. This allows it to provide more accurate and personalized meal plans by learning the user's nutritional balance, preferences, and allergies in real time. Specific methods and criteria for real-time learning include the data update frequency and learning algorithms.

[0035] The list creation unit can create a shopping list based on the meal plan proposed by the planning unit. For example, the list creation unit manages the user's refrigerator inventory and lists the necessary ingredients. The list creation unit can use AI to optimize the user's shopping list. This enables efficient shopping by creating a shopping list based on the meal plan. Specific methods and criteria for creating the shopping list include selection criteria for necessary ingredients and the list format.

[0036] The Nutrition Management Department can manage inventory and provide recipes based on shopping lists created by the List Creation Department. For example, the Nutrition Management Department can manage the inventory in a user's refrigerator and list the necessary ingredients. The Nutrition Management Department can use AI to optimize the user's inventory management and recipe provision. This enables efficient nutrition management by managing inventory and providing recipes based on shopping lists. Specific methods and criteria for inventory management include methods for tracking inventory and timing of replenishment. Specific methods and criteria for recipe provision include criteria for selecting recipes and methods of provision.

[0037] The waste reduction department manages information on surplus food from food suppliers and can distribute it to the most suitable recipients. For example, the waste reduction department can distribute surplus food to welfare programs or food banks. The waste reduction department can use AI to optimize the distribution of surplus food. This reduces food waste by managing surplus food information and distributing it to the most suitable recipients. The specific content and management methods of surplus food information include the type of food, quantity, and expiration date. The specific criteria and selection methods for appropriate recipients include locations with demand and facilities that can accept the food.

[0038] The waste reduction unit can immediately distribute food to welfare programs, food banks, and general consumers. For example, the waste reduction unit distributes surplus food to welfare programs and food banks in real time. The waste reduction unit can use AI to optimize the distribution of surplus food. This further reduces food waste by distributing surplus food to welfare programs, food banks, and general consumers in real time. The specific content and target of welfare programs includes which welfare programs are eligible. The specific content and target of food banks includes which food banks are eligible. The specific definition and target of general consumers includes which consumers are eligible. The specific methods and criteria for immediate distribution include the timing and method of distribution.

[0039] The planning department can learn from user feedback and provide personalized recipe suggestions. For example, the planning department optimizes meal plans based on user feedback. The planning department can use AI to learn from user data in real time and optimize recipe suggestions. This allows for more accurate personalized recipe suggestions by learning from user feedback. The specific content and collection methods of user feedback include survey results and usage history. The specific methods and criteria for personalized recipe suggestions include customization methods based on individual user data.

[0040] The nutrition management department can provide healthy meals in a gamified way, allowing users to enjoy a healthy diet. For example, the nutrition management department could introduce a point system or ranking when providing healthy meals. The nutrition management department can use AI to learn from user data in real time and optimize the provision of healthy meals. This allows users to enjoy a healthy diet by providing healthy meals in a gamified way. Specific methods and criteria for providing meals in a gamified way include point systems and rankings.

[0041] The planning department can analyze a user's past eating history and propose an optimal meal plan. For example, the planning department can suggest similar dishes based on the dishes the user has enjoyed eating in the past. The planning department can also consider ingredients the user has avoided in the past and propose a meal plan that excludes them. The planning department can also propose a meal plan that takes nutritional balance into consideration based on the user's past eating history. In this way, by analyzing the user's past eating history, a more appropriate meal plan can be provided. The specific content and collection methods of past eating history include meal records and purchase history. The specific criteria and proposal methods for the optimal meal plan include nutritional balance and the user's preferences.

[0042] The planning department can adjust the nutritional balance of meal plans based on the user's health condition and exercise level. For example, if the user has just exercised, the planning department will suggest a meal plan high in protein. The planning department can also suggest a meal plan that supplements nutrients identified in the user's health checkup. The planning department can also suggest a meal plan that adjusts calorie intake according to the user's daily exercise level. This allows for the provision of more appropriate meal plans by adjusting the nutritional balance based on the user's health condition and exercise level. Specific details and evaluation methods for health condition include medical records and self-reports. Specific details and evaluation methods for exercise level include exercise records and fitness tracker data. Specific methods and criteria for adjusting nutritional balance include increasing or decreasing specific nutrients or changing ingredients.

[0043] The planning department can use regionally specific ingredients based on the user's geographical location when proposing meal plans. For example, the planning department can propose meal plans using seasonal ingredients from the area where the user lives. If the user is traveling, the planning department can also propose meal plans using local specialties from that area. Based on the user's geographical location, the planning department can also propose recipes using local ingredients. This allows for the provision of more appropriate meal plans by considering the user's geographical location and using regionally specific ingredients. The specific content and collection methods of geographical location information include GPS data and address information. The specific content and selection methods of regionally specific ingredients include locally produced vegetables and specialty products.

[0044] The planning department can suggest relevant meal plans based on the user's social media activity. For example, the planning department can suggest similar dishes based on dishes the user has shared on social media. The planning department can also suggest meal plans based on recipes from cooking accounts the user follows. The planning department can also suggest meal plans based on dishes the user has "liked" on social media. This allows the department to provide more appropriate meal plans by analyzing the user's social media activity. Specific details and methods of analyzing social media activity include the content of posts and the number of likes. Specific details and methods of suggesting relevant meal plans include trending recipes and popular ingredients.

[0045] The list creation unit can analyze the user's past purchase history to create an optimal shopping list. For example, the list creation unit can automatically add ingredients that the user has frequently purchased in the past to the list. The list creation unit can also exclude ingredients that the user has avoided in the past. The list creation unit can also create a list that takes nutritional balance into consideration based on the user's past purchase history. In this way, by analyzing the user's past purchase history, a more appropriate shopping list can be provided. The specific content and collection method of past purchase history includes purchase records and receipt information. The specific criteria and creation method of the optimal list include the selection criteria for necessary ingredients and the list format.

[0046] The list creation function can optimize shopping lists based on the user's budget and purchase frequency. For example, it can add ingredients that are within the user's budget to the list. It can also prioritize adding ingredients that the user frequently purchases. The list creation function can create an optimal list considering the user's budget and purchase frequency. This allows for the provision of more appropriate shopping lists by optimizing the list based on the user's budget and purchase frequency. Specific details and methods for setting the budget include monthly food expenses and the amount that can be spent. Specific details and methods for evaluating purchase frequency include how many times a week the user shops and the frequency of purchasing specific ingredients. Specific methods and criteria for optimizing the list include criteria for selecting necessary ingredients and the list format.

[0047] The list creation unit can include groceries available at the nearest store when creating a shopping list, taking into account the user's geographical location. For example, the list creation unit can add groceries available at the nearest supermarket in the user's area. If the user is traveling, the list creation unit can also add groceries available at supermarkets in that area. The list creation unit can also add groceries available at the nearest store based on the user's geographical location. This allows for the provision of a more appropriate shopping list by including groceries available at the nearest store, taking the user's geographical location into consideration. The specific details and selection methods for the nearest store include distance and the products they carry. The specific details and selection methods for available groceries include inventory status and the products they carry.

[0048] The list creation function can analyze a user's social media activity when creating a shopping list and add relevant ingredients to the list. For example, it can add ingredients used in dishes the user has shared on social media. It can also add ingredients used in recipes from cooking accounts the user follows. It can also add ingredients used in dishes the user has "liked" on social media. This allows the system to provide a more appropriate shopping list by analyzing the user's social media activity. The specific content and analysis methods of social media activity include the content of posts and the number of likes. The specific content and selection methods of relevant ingredients include trending ingredients and popular ingredients.

[0049] The Nutrition Management Department can analyze a user's past eating history to propose the most suitable nutritional management method during nutritional management. For example, the Nutrition Management Department can suggest similar dishes based on the dishes the user has enjoyed eating in the past. The Nutrition Management Department can also consider ingredients the user has avoided in the past and propose a nutritional management method that excludes them. The Nutrition Management Department can also propose a nutritional management method that takes nutritional balance into consideration based on the user's past eating history. In this way, by analyzing the user's past eating history, a more appropriate nutritional management method can be provided. The specific content and collection methods of past eating history include meal records and purchase history. The specific criteria and proposal methods for the optimal nutritional management method include nutritional balance and the user's preferences.

[0050] The Nutrition Management Department can adjust nutritional balance based on the user's health condition and exercise level during nutritional management. For example, if a user has just exercised, the Nutrition Management Department may suggest a nutritional management method that includes a high amount of protein. The Nutrition Management Department can also suggest a nutritional management method that supplements nutrients identified by the user in a health checkup. The Nutrition Management Department can also suggest a nutritional management method that adjusts calorie intake according to the user's daily exercise level. This allows for more appropriate nutritional management by adjusting nutritional balance based on the user's health condition and exercise level. Specific details and evaluation methods for health condition include medical records and self-reports. Specific details and evaluation methods for exercise level include exercise records and fitness tracker data. Specific methods and criteria for adjusting nutritional balance include increasing or decreasing specific nutrients or changing ingredients.

[0051] The Nutrition Management Department can propose region-specific nutrition management methods when providing nutritional management, taking into account the user's geographical location information. For example, the Nutrition Management Department can propose nutrition management methods using seasonal ingredients from the area where the user lives. If the user is traveling, the Nutrition Management Department can also propose nutrition management methods using local specialties from that area. Based on the user's geographical location information, the Nutrition Management Department can also propose nutrition management methods using local ingredients. This allows for more appropriate nutrition management by proposing region-specific nutrition management methods that take the user's geographical location information into account. The specific content and collection methods of geographical location information include GPS data and address information. The specific content and selection methods of region-specific nutrition management methods include locally produced vegetables and specialty products.

[0052] The Nutrition Management Department can analyze users' social media activity and suggest relevant nutritional management methods during nutritional management. For example, the Nutrition Management Department can suggest similar dishes based on dishes shared by users on social media. The Nutrition Management Department can also suggest nutritional management methods based on recipes from cooking accounts that users follow. The Nutrition Management Department can also suggest nutritional management methods based on dishes that users "like" on social media. This allows for the provision of more appropriate nutritional management methods by analyzing users' social media activity. Specific details and methods of analyzing social media activity include the content of posts and the number of likes. Specific details and methods of suggesting relevant nutritional management methods include trend-based recipes and popular ingredients.

[0053] The waste reduction unit can analyze the past supply history of food suppliers to propose the optimal distribution method when allocating surplus food. For example, the waste reduction unit proposes the optimal distribution method based on the types and quantities of food that food suppliers have supplied in the past. The waste reduction unit can also prioritize the allocation of high-demand foods based on the food supplier's past supply history. The waste reduction unit can also analyze the food supplier's past supply history to propose an efficient distribution method. In this way, by analyzing the food supplier's past supply history, a more appropriate distribution method can be provided.

[0054] The waste reduction unit can optimize the allocation of surplus food based on the demand and inventory status of the recipients. For example, the waste reduction unit can grasp the demand of recipients in real time and prioritize the allocation of food with high demand. The waste reduction unit can also consider the inventory status of recipients and prioritize the allocation of food with low inventory. The waste reduction unit can also propose efficient allocation methods based on the demand and inventory status of recipients. As a result, more appropriate allocation becomes possible by optimizing the allocation based on the demand and inventory status of recipients.

[0055] The waste reduction unit can select the optimal distribution destination for surplus food by considering the geographical location information of the recipient. For example, the waste reduction unit can select the nearest distribution destination based on the geographical location information of the recipient. The waste reduction unit can also select a distribution destination with good transportation access by considering the geographical location information of the recipient. The waste reduction unit can also propose an efficient distribution route based on the geographical location information of the recipient. As a result, by selecting the optimal distribution destination by considering the geographical location information of the recipient, more appropriate distribution becomes possible.

[0056] The waste reduction department can analyze the social media activity of recipients when distributing surplus food and propose appropriate distribution methods. For example, the waste reduction department can distribute relevant food based on the food that recipients have shared on social media. The waste reduction department can also propose distribution methods based on information about food accounts that recipients follow. The waste reduction department can also distribute relevant food based on the food that recipients have "liked" on social media. In this way, by analyzing the social media activity of recipients, it is possible to provide more appropriate distribution methods.

[0057] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0058] The planning department can use regionally specific ingredients based on the user's geographical location. For example, it can suggest meal plans using seasonal ingredients from the user's area of ​​residence. If the user is traveling, it can also suggest meal plans using local specialties. Based on the user's geographical location, it can also suggest recipes using local ingredients. This allows for the provision of more appropriate meal plans by considering the user's geographical location and using regionally specific ingredients.

[0059] The list creation section can analyze a user's past purchase history to create an optimal list. For example, it can automatically add ingredients that the user has frequently purchased in the past to the list. It can also exclude ingredients that the user has avoided in the past. Based on the user's past purchase history, it can also create a list that takes nutritional balance into consideration. In this way, by analyzing the user's past purchase history, it can provide a more appropriate shopping list.

[0060] The nutrition management department can adjust nutritional balance based on the user's health condition and exercise level. For example, if a user has just exercised, it can suggest a nutritional management plan that includes a high amount of protein. It can also suggest a nutritional management plan that supplements nutrients identified in the user's health checkup. It can also suggest a nutritional management plan that adjusts calorie intake according to the user's daily exercise level. In this way, more appropriate nutritional management becomes possible by adjusting the nutritional balance based on the user's health condition and exercise level.

[0061] The loss reduction unit can optimize allocation based on the demand and inventory status of the supply destination. For example, it can grasp the supply destination's demand in real time and prioritize the allocation of high-demand food items. It can also consider the supply destination's inventory status and prioritize the allocation of food items with low inventory levels. It can also propose efficient allocation methods based on the supply destination's demand and inventory status. As a result, more appropriate allocation becomes possible by optimizing allocation based on the supply destination's demand and inventory status.

[0062] The loss reduction unit can select the optimal distribution destination by considering the geographical location information of the supply destination. For example, it can select the nearest distribution destination based on the geographical location information of the supply destination. It can also select a distribution destination with good transportation access by considering the geographical location information of the supply destination. It can also propose an efficient distribution route based on the geographical location information of the supply destination. As a result, by selecting the optimal distribution destination by considering the geographical location information of the supply destination, more appropriate distribution becomes possible.

[0063] The following briefly describes the processing flow for example form 1.

[0064] Step 1: The planning department learns each user's nutritional balance, preferences, and allergies, and proposes a personalized meal plan. Based on the user's eating history and health status, the planning department provides a meal plan that takes into account the optimal nutritional balance. Furthermore, it can use AI to learn from the user's data in real time and optimize the meal plan. Step 2: The list creation unit creates a shopping list based on the meal plan proposed by the planning unit. The list creation unit manages the inventory in the user's refrigerator and lists the necessary ingredients. Furthermore, it can optimize the user's shopping list using AI. Step 3: The nutrition management department manages inventory and provides recipes based on the shopping list created by the list creation department. The nutrition management department manages the inventory in the user's refrigerator and lists the necessary ingredients. Furthermore, it can use AI to optimize the user's inventory management and recipe provision. Step 4: The waste reduction unit manages information on surplus food from food suppliers and distributes it to the most suitable recipients. The waste reduction unit distributes surplus food to welfare programs and food banks. Furthermore, AI can be used to optimize the distribution of surplus food.

[0065] (Example of form 2) The AI ​​agent system according to an embodiment of the present invention is a system that comprehensively supports all issues related to "food," such as meal planning, shopping list creation, nutrition management, food waste reduction, community-based cooking classes, and agricultural management. This AI agent system learns the nutritional balance, preferences, allergies, etc. of individual users in real time and proposes personalized meal plans. For example, the AI ​​agent system can provide a meal plan that takes into account the optimal nutritional balance based on the user's eating history and health condition. Next, the AI ​​agent system creates a shopping list based on that meal plan and also manages inventory and provides recipes. For example, the AI ​​agent system can manage the inventory in the user's refrigerator and list the necessary ingredients. In addition, the AI ​​agent system centrally manages surplus food information from food suppliers such as food companies, supermarkets, and retailers and distributes it to the most suitable recipients in real time. For example, the AI ​​agent system can reduce food waste by distributing surplus food to welfare projects and food banks. Furthermore, the AI ​​agent system forms online communities and holds cooking classes tailored to each household. For example, the AI ​​agent system can provide customized cooking classes according to the skill level and preferences of the participants. For farmers, the AI ​​agent system develops optimal cultivation plans based on weather data, cultivation data, and sales data, and provides real-time advice. For example, the AI ​​agent system can suggest the optimal planting and harvesting times to farmers. This enables efficient production and supply of food ingredients. In addition, the AI ​​agent system provides healthy eating in a gamified way, allowing users to enjoy a healthy diet. For example, the AI ​​agent system visualizes daily score management and results, and allows users to compete with family and friends. In this way, the AI ​​agent system can comprehensively support users' eating habits and provide a healthy diet.

[0066] The AI ​​agent system according to this embodiment comprises a planning unit, a list creation unit, a nutrition management unit, and a waste reduction unit. The planning unit learns the nutritional balance, preferences, and allergies of individual users and proposes a personalized meal plan. For example, the planning unit provides a meal plan that considers the optimal nutritional balance based on the user's eating history and health condition. The planning unit can use AI to learn user data in real time and optimize the meal plan. The list creation unit creates a shopping list based on the meal plan proposed by the planning unit. For example, the list creation unit manages the inventory in the user's refrigerator and lists the necessary ingredients. The list creation unit can use AI to optimize the user's shopping list. The nutrition management unit performs inventory management and provides recipes based on the shopping list created by the list creation unit. For example, the nutrition management unit manages the inventory in the user's refrigerator and lists the necessary ingredients. The nutrition management unit can use AI to optimize the user's inventory management and recipe provision. The waste reduction unit manages surplus food information from food suppliers and distributes it to the most suitable recipients. The waste reduction unit distributes surplus food to welfare programs and food banks, for example. The waste reduction unit can optimize the distribution of surplus food using AI. As a result, the AI ​​agent system according to the embodiment can efficiently provide personalized meal plans based on the user's nutritional balance, preferences, and allergies, and can create shopping lists, manage inventory, provide recipes, and distribute surplus food.

[0067] The planning department learns each user's nutritional balance, preferences, and allergies to propose personalized meal plans. Specifically, users input their daily meal details into the application, and the planning department collects this data and analyzes the user's eating history in detail. Furthermore, it also collects information on the user's health status, taking into account health indicators such as blood pressure, blood sugar levels, and weight. This allows the planning department to provide an optimal nutritional balance tailored to the user's health condition. The AI ​​learns this data in real time and generates meal plans that reflect the user's preferences and allergies. For example, if a user is allergic to a particular ingredient, it will suggest recipes that do not include that ingredient. It can also offer a variety of menus, such as Japanese, Western, and Chinese cuisine, according to the user's preferences. In addition, the planning department can propose meal plans that take into account seasonal and regional specialties, supporting a varied and enjoyable eating lifestyle for the user. In this way, the planning department can balance maintaining the user's health with enjoying food.

[0068] The list creation unit generates shopping lists based on meal plans proposed by the planning unit. Specifically, it utilizes sensors and cameras inside the refrigerator to manage the inventory in the user's refrigerator and list the necessary ingredients. This allows the list creation unit to understand the quantity and expiration dates of ingredients in the refrigerator, enabling it to generate efficient shopping lists. The AI ​​learns the user's past shopping history and consumption patterns to predict necessary ingredients. For example, it prioritizes listing ingredients that the user frequently uses or ingredients needed for specific dishes. The list creation unit can also adjust the contents of the shopping list according to the user's lifestyle and meal frequency. For example, it lists small amounts of ingredients for users living alone and large amounts for large families. Furthermore, the list creation unit can utilize sale information and coupons to reduce the user's shopping costs. In this way, the list creation unit can provide efficient and economical shopping lists, improving the user's shopping experience.

[0069] The Nutrition Management Department manages inventory and provides recipes based on shopping lists created by the List Creation Department. Specifically, it utilizes sensors and cameras inside the refrigerator to manage the inventory in the user's refrigerator and list the necessary ingredients. This allows the Nutrition Management Department to understand the quantity and expiration dates of ingredients in the refrigerator, enabling efficient inventory management. AI learns the user's past consumption patterns and meal history to suggest optimal recipes. For example, it provides easy-to-make recipes and nutritionally balanced recipes based on the ingredients in the refrigerator. The Nutrition Management Department can also suggest recipes that take into account the user's health condition and nutritional balance. For example, it provides low-calorie recipes to users on a diet and high-protein recipes to users aiming to build muscle. Furthermore, the Nutrition Management Department can collect user satisfaction with meals and feedback to continuously improve the recipes. This allows the Nutrition Management Department to balance maintaining the user's health with enjoying food.

[0070] The Loss Reduction Department manages information on surplus food from food suppliers and distributes it to the most suitable recipients. Specifically, it uses AI to collect information on surplus food provided by food suppliers and distribute it to appropriate recipients such as welfare organizations and food banks. The AI ​​analyzes information such as the type and quantity of surplus food and its expiration date to identify the optimal recipient. For example, it prioritizes the distribution of food nearing its expiration date, minimizing food waste. The Loss Reduction Department can also distribute efficiently by considering the demand and receiving capacity of recipients. For example, it can grasp the inventory status and demand of welfare organizations and food banks in real time and distribute the necessary food at the appropriate time. Furthermore, the Loss Reduction Department can monitor the distribution status of surplus food and evaluate the effectiveness of the distribution. In this way, the Loss Reduction Department can reduce food waste and make a social contribution. In addition, the Loss Reduction Department can strengthen cooperation with food suppliers and recipients and build a sustainable food supply chain. In this way, the Loss Reduction Department can distribute surplus food efficiently and effectively, achieving both food waste reduction and social contribution.

[0071] The planning department can learn the user's nutritional balance, preferences, and allergies in real time. For example, the planning department provides a meal plan that considers the optimal nutritional balance based on the user's eating history and health status. The planning department can use AI to learn user data in real time and optimize meal plans. This allows it to provide more accurate and personalized meal plans by learning the user's nutritional balance, preferences, and allergies in real time. Specific methods and criteria for real-time learning include the data update frequency and learning algorithms.

[0072] The list creation unit can create a shopping list based on the meal plan proposed by the planning unit. For example, the list creation unit manages the user's refrigerator inventory and lists the necessary ingredients. The list creation unit can use AI to optimize the user's shopping list. This enables efficient shopping by creating a shopping list based on the meal plan. Specific methods and criteria for creating the shopping list include selection criteria for necessary ingredients and the list format.

[0073] The Nutrition Management Department can manage inventory and provide recipes based on shopping lists created by the List Creation Department. For example, the Nutrition Management Department can manage the inventory in a user's refrigerator and list the necessary ingredients. The Nutrition Management Department can use AI to optimize the user's inventory management and recipe provision. This enables efficient nutrition management by managing inventory and providing recipes based on shopping lists. Specific methods and criteria for inventory management include methods for tracking inventory and timing of replenishment. Specific methods and criteria for recipe provision include criteria for selecting recipes and methods of provision.

[0074] The waste reduction department manages information on surplus food from food suppliers and can distribute it to the most suitable recipients. For example, the waste reduction department can distribute surplus food to welfare programs or food banks. The waste reduction department can use AI to optimize the distribution of surplus food. This reduces food waste by managing surplus food information and distributing it to the most suitable recipients. The specific content and management methods of surplus food information include the type of food, quantity, and expiration date. The specific criteria and selection methods for appropriate recipients include locations with demand and facilities that can accept the food.

[0075] The waste reduction unit can immediately distribute food to welfare programs, food banks, and general consumers. For example, the waste reduction unit distributes surplus food to welfare programs and food banks in real time. The waste reduction unit can use AI to optimize the distribution of surplus food. This further reduces food waste by distributing surplus food to welfare programs, food banks, and general consumers in real time. The specific content and target of welfare programs includes which welfare programs are eligible. The specific content and target of food banks includes which food banks are eligible. The specific definition and target of general consumers includes which consumers are eligible. The specific methods and criteria for immediate distribution include the timing and method of distribution.

[0076] The planning department can learn from user feedback and provide personalized recipe suggestions. For example, the planning department optimizes meal plans based on user feedback. The planning department can use AI to learn from user data in real time and optimize recipe suggestions. This allows for more accurate personalized recipe suggestions by learning from user feedback. The specific content and collection methods of user feedback include survey results and usage history. The specific methods and criteria for personalized recipe suggestions include customization methods based on individual user data.

[0077] The nutrition management department can provide healthy meals in a gamified way, allowing users to enjoy a healthy diet. For example, the nutrition management department could introduce a point system or ranking when providing healthy meals. The nutrition management department can use AI to learn from user data in real time and optimize the provision of healthy meals. This allows users to enjoy a healthy diet by providing healthy meals in a gamified way. Specific methods and criteria for providing meals in a gamified way include point systems and rankings.

[0078] The planning unit can estimate the user's emotions and adjust the meal plan based on those emotions. For example, if the user is stressed, the planning unit can suggest a meal plan using ingredients that have a relaxing effect. If the user is tired, the planning unit can also suggest a meal plan using ingredients suitable for energy replenishment. If the user is enjoying themselves, the planning unit can also suggest a visually appealing and enjoyable meal plan. In this way, by adjusting the meal plan based on the user's emotions, a more appropriate meal plan can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Specific methods and criteria for estimating the user's emotions include facial recognition and voice analysis. Specific methods and criteria for adjusting the meal plan include changes in nutritional balance and changes in ingredients.

[0079] The planning department can analyze a user's past eating history and propose an optimal meal plan. For example, the planning department can suggest similar dishes based on the dishes the user has enjoyed eating in the past. The planning department can also consider ingredients the user has avoided in the past and propose a meal plan that excludes them. The planning department can also propose a meal plan that takes nutritional balance into consideration based on the user's past eating history. In this way, by analyzing the user's past eating history, a more appropriate meal plan can be provided. The specific content and collection methods of past eating history include meal records and purchase history. The specific criteria and proposal methods for the optimal meal plan include nutritional balance and the user's preferences.

[0080] The planning department can adjust the nutritional balance of meal plans based on the user's health condition and exercise level. For example, if the user has just exercised, the planning department will suggest a meal plan high in protein. The planning department can also suggest a meal plan that supplements nutrients identified in the user's health checkup. The planning department can also suggest a meal plan that adjusts calorie intake according to the user's daily exercise level. This allows for the provision of more appropriate meal plans by adjusting the nutritional balance based on the user's health condition and exercise level. Specific details and evaluation methods for health condition include medical records and self-reports. Specific details and evaluation methods for exercise level include exercise records and fitness tracker data. Specific methods and criteria for adjusting nutritional balance include increasing or decreasing specific nutrients or changing ingredients.

[0081] The planning unit can estimate the user's emotions and prioritize meal plans based on those emotions. For example, if the user is stressed, the planning unit will prioritize meal plans that promote relaxation. If the user is tired, the planning unit may also prioritize meal plans that provide energy. If the user is enjoying themselves, the planning unit may also prioritize visually appealing and enjoyable meal plans. By prioritizing meal plans based on the user's emotions, a more appropriate meal plan can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Specific methods and criteria for determining meal plan priorities include nutritional balance and user preferences.

[0082] The planning department can use regionally specific ingredients based on the user's geographical location when proposing meal plans. For example, the planning department can propose meal plans using seasonal ingredients from the area where the user lives. If the user is traveling, the planning department can also propose meal plans using local specialties from that area. Based on the user's geographical location, the planning department can also propose recipes using local ingredients. This allows for the provision of more appropriate meal plans by considering the user's geographical location and using regionally specific ingredients. The specific content and collection methods of geographical location information include GPS data and address information. The specific content and selection methods of regionally specific ingredients include locally produced vegetables and specialty products.

[0083] The planning department can suggest relevant meal plans based on the user's social media activity. For example, the planning department can suggest similar dishes based on dishes the user has shared on social media. The planning department can also suggest meal plans based on recipes from cooking accounts the user follows. The planning department can also suggest meal plans based on dishes the user has "liked" on social media. This allows the department to provide more appropriate meal plans by analyzing the user's social media activity. Specific details and methods of analyzing social media activity include the content of posts and the number of likes. Specific details and methods of suggesting relevant meal plans include trending recipes and popular ingredients.

[0084] The list creation unit can estimate the user's emotions and adjust the contents of the shopping list based on those emotions. For example, if the user is feeling stressed, the list creation unit can add relaxing foods to the list. If the user is tired, the list creation unit can also add foods suitable for energy replenishment. If the user is having fun, the list creation unit can also add visually appealing and enjoyable foods to the list. In this way, by adjusting the contents of the shopping list based on the user's emotions, a more appropriate shopping list can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Specific methods and criteria for adjusting the contents of the shopping list include changing the required foods and adjusting quantities.

[0085] The list creation unit can analyze the user's past purchase history to create an optimal shopping list. For example, the list creation unit can automatically add ingredients that the user has frequently purchased in the past to the list. The list creation unit can also exclude ingredients that the user has avoided in the past. The list creation unit can also create a list that takes nutritional balance into consideration based on the user's past purchase history. In this way, by analyzing the user's past purchase history, a more appropriate shopping list can be provided. The specific content and collection method of past purchase history includes purchase records and receipt information. The specific criteria and creation method of the optimal list include the selection criteria for necessary ingredients and the list format.

[0086] The list creation function can optimize shopping lists based on the user's budget and purchase frequency. For example, it can add ingredients that are within the user's budget to the list. It can also prioritize adding ingredients that the user frequently purchases. The list creation function can create an optimal list considering the user's budget and purchase frequency. This allows for the provision of more appropriate shopping lists by optimizing the list based on the user's budget and purchase frequency. Specific details and methods for setting the budget include monthly food expenses and the amount that can be spent. Specific details and methods for evaluating purchase frequency include how many times a week the user shops and the frequency of purchasing specific ingredients. Specific methods and criteria for optimizing the list include criteria for selecting necessary ingredients and the list format.

[0087] The list creation unit can estimate the user's emotions and determine the priority of the shopping list based on those emotions. For example, if the user is feeling stressed, the list creation unit might prioritize foods with relaxing effects. If the user is tired, it might prioritize foods suitable for energy replenishment. If the user is having fun, it might prioritize foods that are visually appealing and enjoyable. This allows for the provision of a more appropriate shopping list by determining the priority of the shopping list based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Specific methods and criteria for determining the priority of the shopping list include the importance of the necessary foods and the urgency of purchase.

[0088] The list creation unit can include groceries available at the nearest store when creating a shopping list, taking into account the user's geographical location. For example, the list creation unit can add groceries available at the nearest supermarket in the user's area. If the user is traveling, the list creation unit can also add groceries available at supermarkets in that area. The list creation unit can also add groceries available at the nearest store based on the user's geographical location. This allows for the provision of a more appropriate shopping list by including groceries available at the nearest store, taking the user's geographical location into consideration. The specific details and selection methods for the nearest store include distance and the products they carry. The specific details and selection methods for available groceries include inventory status and the products they carry.

[0089] The list creation function can analyze a user's social media activity when creating a shopping list and add relevant ingredients to the list. For example, it can add ingredients used in dishes the user has shared on social media. It can also add ingredients used in recipes from cooking accounts the user follows. It can also add ingredients used in dishes the user has "liked" on social media. This allows the system to provide a more appropriate shopping list by analyzing the user's social media activity. The specific content and analysis methods of social media activity include the content of posts and the number of likes. The specific content and selection methods of relevant ingredients include trending ingredients and popular ingredients.

[0090] The nutrition management department can estimate the user's emotions and adjust the nutrition management method based on those emotions. For example, if the user is stressed, the nutrition management department can suggest a nutrition management method using ingredients with relaxing effects. If the user is tired, the nutrition management department can also suggest a nutrition management method using ingredients suitable for energy replenishment. If the user is having fun, the nutrition management department can also suggest a nutrition management method using visually appealing and enjoyable ingredients. By adjusting the nutrition management method based on the user's emotions, more appropriate nutrition management becomes possible. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Specific methods and criteria for adjusting the nutrition management method include changes in nutritional balance and changes in ingredients.

[0091] The Nutrition Management Department can analyze a user's past eating history to propose the most suitable nutritional management method during nutritional management. For example, the Nutrition Management Department can suggest similar dishes based on the dishes the user has enjoyed eating in the past. The Nutrition Management Department can also consider ingredients the user has avoided in the past and propose a nutritional management method that excludes them. The Nutrition Management Department can also propose a nutritional management method that takes nutritional balance into consideration based on the user's past eating history. In this way, by analyzing the user's past eating history, a more appropriate nutritional management method can be provided. The specific content and collection methods of past eating history include meal records and purchase history. The specific criteria and proposal methods for the optimal nutritional management method include nutritional balance and the user's preferences.

[0092] The Nutrition Management Department can adjust nutritional balance based on the user's health condition and exercise level during nutritional management. For example, if a user has just exercised, the Nutrition Management Department may suggest a nutritional management method that includes a high amount of protein. The Nutrition Management Department can also suggest a nutritional management method that supplements nutrients identified by the user in a health checkup. The Nutrition Management Department can also suggest a nutritional management method that adjusts calorie intake according to the user's daily exercise level. This allows for more appropriate nutritional management by adjusting nutritional balance based on the user's health condition and exercise level. Specific details and evaluation methods for health condition include medical records and self-reports. Specific details and evaluation methods for exercise level include exercise records and fitness tracker data. Specific methods and criteria for adjusting nutritional balance include increasing or decreasing specific nutrients or changing ingredients.

[0093] The nutrition management department can estimate the user's emotions and determine nutrition management priorities based on those estimated emotions. For example, if the user is stressed, the nutrition management department will prioritize nutrition management methods using ingredients with relaxing effects. If the user is tired, the nutrition management department may also prioritize nutrition management methods using ingredients suitable for energy replenishment. If the user is enjoying themselves, the nutrition management department may also prioritize nutrition management methods using visually appealing and enjoyable ingredients. This allows for more appropriate nutrition management by determining nutrition management priorities based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Specific methods and criteria for determining nutrition management priorities include nutritional balance and user preferences.

[0094] The Nutrition Management Department can propose region-specific nutrition management methods when providing nutritional management, taking into account the user's geographical location information. For example, the Nutrition Management Department can propose nutrition management methods using seasonal ingredients from the area where the user lives. If the user is traveling, the Nutrition Management Department can also propose nutrition management methods using local specialties from that area. Based on the user's geographical location information, the Nutrition Management Department can also propose nutrition management methods using local ingredients. This allows for more appropriate nutrition management by proposing region-specific nutrition management methods that take the user's geographical location information into account. The specific content and collection methods of geographical location information include GPS data and address information. The specific content and selection methods of region-specific nutrition management methods include locally produced vegetables and specialty products.

[0095] The Nutrition Management Department can analyze users' social media activity and suggest relevant nutritional management methods during nutritional management. For example, the Nutrition Management Department can suggest similar dishes based on dishes shared by users on social media. The Nutrition Management Department can also suggest nutritional management methods based on recipes from cooking accounts that users follow. The Nutrition Management Department can also suggest nutritional management methods based on dishes that users "like" on social media. This allows for the provision of more appropriate nutritional management methods by analyzing users' social media activity. Specific details and methods of analyzing social media activity include the content of posts and the number of likes. Specific details and methods of suggesting relevant nutritional management methods include trend-based recipes and popular ingredients.

[0096] The waste reduction unit can estimate the user's emotions and adjust the distribution method of surplus food based on the estimated emotions. For example, if the user is stressed, the waste reduction unit will prioritize distributing ingredients with relaxing effects. If the user is tired, the waste reduction unit can also prioritize distributing ingredients suitable for energy replenishment. If the user is having fun, the waste reduction unit can also prioritize distributing visually appealing and enjoyable ingredients. By adjusting the distribution method of surplus food based on the user's emotions, a more appropriate distribution becomes possible. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0097] The waste reduction unit can analyze the past supply history of food suppliers to propose the optimal distribution method when allocating surplus food. For example, the waste reduction unit proposes the optimal distribution method based on the types and quantities of food that food suppliers have supplied in the past. The waste reduction unit can also prioritize the allocation of high-demand foods based on the food supplier's past supply history. The waste reduction unit can also analyze the food supplier's past supply history to propose an efficient distribution method. In this way, by analyzing the food supplier's past supply history, a more appropriate distribution method can be provided.

[0098] The waste reduction unit can optimize the allocation of surplus food based on the demand and inventory status of the recipients. For example, the waste reduction unit can grasp the demand of recipients in real time and prioritize the allocation of food with high demand. The waste reduction unit can also consider the inventory status of recipients and prioritize the allocation of food with low inventory. The waste reduction unit can also propose efficient allocation methods based on the demand and inventory status of recipients. As a result, more appropriate allocation becomes possible by optimizing the allocation based on the demand and inventory status of recipients.

[0099] The waste reduction unit can estimate the user's emotions and determine the distribution priority of surplus food based on the estimated emotions. For example, if the user is stressed, the waste reduction unit will prioritize distributing foods with relaxing effects. If the user is tired, the waste reduction unit can also prioritize distributing foods suitable for energy replenishment. If the user is having fun, the waste reduction unit can also prioritize distributing foods that are visually appealing and enjoyable. This allows for more appropriate distribution by determining the distribution priority of surplus food based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0100] The waste reduction unit can select the optimal distribution destination for surplus food by considering the geographical location information of the recipient. For example, the waste reduction unit can select the nearest distribution destination based on the geographical location information of the recipient. The waste reduction unit can also select a distribution destination with good transportation access by considering the geographical location information of the recipient. The waste reduction unit can also propose an efficient distribution route based on the geographical location information of the recipient. As a result, by selecting the optimal distribution destination by considering the geographical location information of the recipient, more appropriate distribution becomes possible.

[0101] The waste reduction department can analyze the social media activity of recipients when distributing surplus food and propose appropriate distribution methods. For example, the waste reduction department can distribute relevant food based on the food that recipients have shared on social media. The waste reduction department can also propose distribution methods based on information about food accounts that recipients follow. The waste reduction department can also distribute relevant food based on the food that recipients have "liked" on social media. In this way, by analyzing the social media activity of recipients, it is possible to provide more appropriate distribution methods.

[0102] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0103] The planning department can estimate the user's emotions and adjust the meal plan based on those emotions. For example, if the user is stressed, it can suggest a meal plan using ingredients that have a relaxing effect. If the user is tired, it can suggest a meal plan using ingredients suitable for energy replenishment. If the user is having fun, it can suggest a visually appealing and enjoyable meal plan. In this way, by adjusting the meal plan based on the user's emotions, a more appropriate meal plan can be provided.

[0104] The list creation unit can estimate the user's emotions and adjust the contents of the shopping list based on those emotions. For example, if the user is feeling stressed, it can add relaxing foods to the list. If the user is tired, it can add foods suitable for replenishing energy. If the user is having fun, it can add visually appealing and enjoyable foods to the list. In this way, by adjusting the contents of the shopping list based on the user's emotions, a more appropriate shopping list can be provided.

[0105] The nutrition management department can estimate the user's emotions and adjust the nutrition management method based on those emotions. For example, if the user is stressed, it can suggest a nutrition management method using ingredients that have a relaxing effect. If the user is tired, it can suggest a nutrition management method using ingredients suitable for energy replenishment. If the user is having fun, it can suggest a nutrition management method using visually appealing and enjoyable ingredients. By adjusting the nutrition management method based on the user's emotions, more appropriate nutrition management becomes possible.

[0106] The waste reduction unit can estimate the user's emotions and adjust the distribution method of surplus food based on those emotions. For example, if the user is stressed, it can prioritize distributing ingredients that have a relaxing effect. If the user is tired, it can prioritize distributing ingredients suitable for energy replenishment. If the user is having fun, it can prioritize distributing visually appealing and enjoyable ingredients. By adjusting the distribution method of surplus food based on the user's emotions, a more appropriate distribution becomes possible.

[0107] The planning department can estimate the user's emotions and prioritize meal plans based on those emotions. For example, if the user is stressed, it can prioritize meal plans that promote relaxation. If the user is tired, it can prioritize meal plans that provide energy. If the user is having fun, it can prioritize meal plans that are visually appealing and enjoyable. By prioritizing meal plans based on the user's emotions, the system can provide more appropriate meal plans.

[0108] The planning department can use regionally specific ingredients based on the user's geographical location. For example, it can suggest meal plans using seasonal ingredients from the user's area of ​​residence. If the user is traveling, it can also suggest meal plans using local specialties. Based on the user's geographical location, it can also suggest recipes using local ingredients. This allows for the provision of more appropriate meal plans by considering the user's geographical location and using regionally specific ingredients.

[0109] The list creation section can analyze a user's past purchase history to create an optimal list. For example, it can automatically add ingredients that the user has frequently purchased in the past to the list. It can also exclude ingredients that the user has avoided in the past. Based on the user's past purchase history, it can also create a list that takes nutritional balance into consideration. In this way, by analyzing the user's past purchase history, it can provide a more appropriate shopping list.

[0110] The nutrition management department can adjust nutritional balance based on the user's health condition and exercise level. For example, if a user has just exercised, it can suggest a nutritional management plan that includes a high amount of protein. It can also suggest a nutritional management plan that supplements nutrients identified in the user's health checkup. It can also suggest a nutritional management plan that adjusts calorie intake according to the user's daily exercise level. In this way, more appropriate nutritional management becomes possible by adjusting the nutritional balance based on the user's health condition and exercise level.

[0111] The loss reduction unit can optimize allocation based on the demand and inventory status of the supply destination. For example, it can grasp the supply destination's demand in real time and prioritize the allocation of high-demand food items. It can also consider the supply destination's inventory status and prioritize the allocation of food items with low inventory levels. It can also propose efficient allocation methods based on the supply destination's demand and inventory status. As a result, more appropriate allocation becomes possible by optimizing allocation based on the supply destination's demand and inventory status.

[0112] The loss reduction unit can select the optimal distribution destination by considering the geographical location information of the supply destination. For example, it can select the nearest distribution destination based on the geographical location information of the supply destination. It can also select a distribution destination with good transportation access by considering the geographical location information of the supply destination. It can also propose an efficient distribution route based on the geographical location information of the supply destination. As a result, by selecting the optimal distribution destination by considering the geographical location information of the supply destination, more appropriate distribution becomes possible.

[0113] The following briefly describes the processing flow for example form 2.

[0114] Step 1: The planning department learns each user's nutritional balance, preferences, and allergies, and proposes a personalized meal plan. Based on the user's eating history and health status, the planning department provides a meal plan that takes into account the optimal nutritional balance. Furthermore, it can use AI to learn from the user's data in real time and optimize the meal plan. Step 2: The list creation unit creates a shopping list based on the meal plan proposed by the planning unit. The list creation unit manages the inventory in the user's refrigerator and lists the necessary ingredients. Furthermore, it can optimize the user's shopping list using AI. Step 3: The nutrition management department manages inventory and provides recipes based on the shopping list created by the list creation department. The nutrition management department manages the inventory in the user's refrigerator and lists the necessary ingredients. Furthermore, it can use AI to optimize the user's inventory management and recipe provision. Step 4: The waste reduction unit manages information on surplus food from food suppliers and distributes it to the most suitable recipients. The waste reduction unit distributes surplus food to welfare programs and food banks. Furthermore, AI can be used to optimize the distribution of surplus food.

[0115] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0116] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0117] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0118] Each of the multiple elements described above, including the planning unit, list creation unit, nutrition management unit, and loss reduction unit, is implemented by, for example, at least one of the smart device 14 and the data processing unit 12. For example, the planning unit is implemented by the control unit 46A of the smart device 14, which learns the user's nutritional balance, preferences, and allergies and proposes a personalized meal plan. The list creation unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12, which creates a shopping list based on the meal plan. The nutrition management unit is implemented by, for example, the control unit 46A of the smart device 14, which manages inventory and provides recipes. The loss reduction unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12, which manages surplus food information and distributes it to the optimal recipient. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0119] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0120] As shown in Figure 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.

[0121] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0122] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0123] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0124] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0125] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0126] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0127] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0128] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0129] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0130] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0131] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0132] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0133] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0134] Each of the multiple elements described above, including the planning unit, list creation unit, nutrition management unit, and loss reduction unit, is implemented by at least one of the smart glasses 214 and the data processing unit 12. For example, the planning unit is implemented by the control unit 46A of the smart glasses 214, which learns the user's nutritional balance, preferences, and allergies and proposes a personalized meal plan. The list creation unit is implemented by the identification processing unit 290 of the data processing unit 12, which creates a shopping list based on the meal plan. The nutrition management unit is implemented by the control unit 46A of the smart glasses 214, which manages inventory and provides recipes. The loss reduction unit is implemented by the identification processing unit 290 of the data processing unit 12, which manages surplus food information and distributes it to the optimal recipient. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0135] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0136] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0137] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0138] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0139] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0140] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0141] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0142] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0143] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0144] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0145] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0146] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0147] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0148] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0149] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0150] Each of the multiple elements described above, including the planning unit, list creation unit, nutrition management unit, and loss reduction unit, is implemented by at least one of the headset terminal 314 and the data processing unit 12. For example, the planning unit is implemented by the control unit 46A of the headset terminal 314, which learns the user's nutritional balance, preferences, and allergies and proposes a personalized meal plan. The list creation unit is implemented by the specific processing unit 290 of the data processing unit 12, which creates a shopping list based on the meal plan. The nutrition management unit is implemented by the control unit 46A of the headset terminal 314, which manages inventory and provides recipes. The loss reduction unit is implemented by the specific processing unit 290 of the data processing unit 12, which manages surplus food information and distributes it to the optimal recipient. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0151] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0152] As shown in Figure 7, the 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.

[0153] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0154] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0155] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0156] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0157] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0158] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0159] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0160] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0161] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0162] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0163] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0164] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 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 a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0166] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0167] Each of the multiple elements described above, including the planning unit, list creation unit, nutrition management unit, and loss reduction unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the planning unit is implemented by the control unit 46A of the robot 414, which learns the user's nutritional balance, preferences, and allergies and proposes a personalized meal plan. The list creation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which creates a shopping list based on the meal plan. The nutrition management unit is implemented by, for example, the control unit 46A of the robot 414, which manages inventory and provides recipes. The loss reduction unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which manages surplus food information and distributes it to the optimal recipient. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0168] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0169] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0170] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0171] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0172] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0173] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0174] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0175] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0176] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0177] 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.

[0178] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0179] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0180] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0181] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0182] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0183] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0184] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0185] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0186] (Note 1) The planning department learns each user's nutritional balance, preferences, and allergies, and proposes personalized meal plans. A list creation unit creates a shopping list based on the meal plan proposed by the planning unit, The nutrition management department manages inventory and provides recipes based on the shopping list created by the aforementioned list creation department, It includes a loss reduction unit that manages information on surplus food from food suppliers and distributes it to appropriate recipients. A system characterized by the following features. (Note 2) The aforementioned planning unit, Learns the user's nutritional balance, preferences, and allergies in real time. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned list creation unit, A shopping list is created based on the meal plan proposed by the aforementioned planning department. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned nutrition management department, Based on the shopping list created by the aforementioned list creation unit, inventory management and recipe provision are performed. The system described in Appendix 1, characterized by the features described herein. (Note 5) The loss reduction unit is, Manage surplus food information from food suppliers and distribute it to the most suitable recipients. The system described in Appendix 1, characterized by the features described herein. (Note 6) The loss reduction unit is, Distribute immediately to welfare programs, food banks, and general consumers. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned planning unit, Based on user feedback, the system learns from the data and provides personalized recipe suggestions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned nutrition management department, We aim to provide healthy eating in a game-like format, allowing users to enjoy a healthy diet while having fun. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned planning unit, The system estimates the user's emotions and adjusts the meal plan based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned planning unit, It analyzes the user's past eating history and suggests the optimal meal plan. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned planning unit, When proposing a meal plan, the nutritional balance is adjusted based on the user's health condition and activity level. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned planning unit, It estimates the user's emotions and determines the priority of meal plans based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned planning unit, When suggesting meal plans, use local ingredients based on the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned planning unit, When suggesting meal plans, the system will suggest relevant meal plans based on the user's social media activity. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned list creation unit, The system estimates the user's emotions and adjusts the contents of the shopping list based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned list creation unit, When creating a shopping list, the system analyzes the user's past purchase history to create the most optimal list. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned list creation unit, When creating a shopping list, optimize the list based on the user's budget and purchase frequency. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned list creation unit, It estimates the user's emotions and prioritizes items on the shopping list based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned list creation unit, When creating a shopping list, the system takes the user's geographical location into consideration and includes ingredients that can be purchased at the nearest store. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned list creation unit, When creating a shopping list, the system analyzes the user's social media activity and adds relevant ingredients to the list. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned nutrition management department, It estimates the user's emotions and adjusts the nutritional management method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned nutrition management department, During nutritional management, the system analyzes the user's past eating history to suggest the optimal nutritional management method. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned nutrition management department, When managing nutrition, the balance of nutrients is adjusted based on the user's health condition and activity level. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned nutrition management department, It estimates the user's emotions and determines the priority of nutritional management based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned nutrition management department, When providing nutritional management, we propose region-specific nutritional management methods that take into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned nutrition management department, When providing nutritional management, we analyze users' social media activity and suggest relevant nutritional management methods. The system described in Appendix 1, characterized by the features described herein. (Note 27) The loss reduction unit is, The system estimates the user's emotions and adjusts the distribution method of surplus food based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The loss reduction unit is, When distributing surplus food, we analyze the past supply history of food suppliers to propose the optimal distribution method. The system described in Appendix 1, characterized by the features described herein. (Note 29) The loss reduction unit is, When allocating surplus food, optimize the allocation based on the demand and inventory status of the recipient. The system described in Appendix 1, characterized by the features described herein. (Note 30) The loss reduction unit is, The system estimates the user's emotions and determines the priority for distributing surplus food based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 31) The loss reduction unit is, When distributing surplus food, the optimal distribution destination is selected by considering the geographical location of the recipient. The system described in Appendix 1, characterized by the features described herein. (Note 32) The loss reduction unit is, When distributing surplus food, we analyze the social media activity of recipients and propose relevant distribution methods. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0187] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The planning department learns each user's nutritional balance, preferences, and allergies, and proposes personalized meal plans. A list creation unit creates a shopping list based on the meal plan proposed by the planning unit, The nutrition management department manages inventory and provides recipes based on the shopping lists created by the aforementioned list creation department, It includes a loss reduction unit that manages information on surplus food from food suppliers and distributes it to appropriate recipients. A system characterized by the following features.

2. The aforementioned planning unit, Learns the user's nutritional balance, preferences, and allergies in real time. The system according to feature 1.

3. The aforementioned list creation unit, A shopping list is created based on the meal plan proposed by the aforementioned planning department. The system according to feature 1.

4. The aforementioned nutrition management department, Based on the shopping list created by the aforementioned list creation unit, inventory management and recipe provision are performed. The system according to feature 1.

5. The loss reduction unit is, Manage surplus food information from food suppliers and distribute it to the most suitable recipients. The system according to feature 1.

6. The loss reduction unit is, Distribute immediately to welfare programs, food banks, and general consumers. The system according to feature 1.

7. The aforementioned planning unit, Based on user feedback, the system learns from the data and provides personalized recipe suggestions. The system according to feature 1.

8. The aforementioned nutrition management department, We aim to provide healthy eating in a game-like format, allowing users to enjoy a healthy diet while having fun. The system according to feature 1.

Images