System

Abstract

An object of a system according to an embodiment is to reduce food loss by effectively utilizing ingredients possessed by a user.SOLUTION: A system includes an ingredient input part, an analysis part, a recipe generation part, a sharing part, an evaluation part, a battle part, and a ranking part. The ingredient input unit inputs an ingredient possessed by a user. The analysis unit analyzes the foodstuff input by the foodstuff input unit. The recipe generation unit generates a recipe based on the food material analyzed by the analysis unit. The sharing unit shares the recipe generated by the recipe generation unit. The evaluation unit evaluates the recipe shared by the sharing unit. The battle unit performs a battle based on the recipe evaluated by the evaluation unit. The ranking unit calculates a food loss contribution degree on the basis of a result of the battle performed by the battle unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have had the problem of not providing sufficient means for users to effectively utilize ingredients they have on hand and reduce food waste.

[0005] The system according to the embodiment aims to effectively utilize ingredients that users have on hand and reduce food waste. [Means for solving the problem]

[0006] The system according to the embodiment includes an ingredient input unit, an analysis unit, a recipe generation unit, a sharing unit, an evaluation unit, a battle unit, and a ranking unit. The ingredient input unit inputs ingredients owned by the user. The analysis unit analyzes the ingredients input by the ingredient input unit. The recipe generation unit generates a recipe based on the ingredients analyzed by the analysis unit. The sharing unit shares the recipe generated by the recipe generation unit. The evaluation unit evaluates the recipe shared by the sharing unit. The battle unit holds a battle based on the recipe evaluated by the evaluation unit. The ranking unit calculates the contribution to food waste based on the results of the battle held by the battle unit. [Effects of the Invention]

[0007] The system according to the embodiment can effectively utilize ingredients that the user has on hand and reduce food waste. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

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

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

[0028] (Example 1) The food waste countermeasure system according to an embodiment of the present invention uses a generation AI to generate unique recipes based on ingredients owned by the user, and allows users to share, rate, battle, and rank recipes within the community. This allows users to easily tackle the food waste problem.

[0029] The food waste reduction system according to the embodiment includes an ingredient input unit, an analysis unit, a recipe generation unit, a sharing unit, an evaluation unit, a battle unit, and a ranking unit. The ingredient input unit inputs ingredients owned by the user. For example, the user may take a photo of the ingredient with a camera and upload the image to an application. The ingredient input unit can also manually input the name of an ingredient. For example, if the user inputs "tomato," the system recognizes the ingredient. The analysis unit analyzes the ingredients input by the ingredient input unit. For example, the generation AI may use image analysis technology to identify the type and quantity of the ingredient. The analysis unit can also evaluate the freshness and quality of the ingredient. For example, it may determine the freshness of a tomato based on its color and shape. The recipe generation unit generates a recipe based on the ingredients analyzed by the analysis unit. For example, the generation AI may suggest a new pasta recipe using tomatoes, cheese, and basil. The recipe generation unit can also generate recipes by referencing the user's past cooking history and taking into account frequently used ingredients and preferred seasonings. The sharing unit shares the recipes generated by the recipe generation unit. For example, a user can share a recipe they created within a community, allowing other users to view the recipe. The evaluation unit evaluates the recipe shared by the sharing unit. For example, other users can leave star ratings and comments on the recipe. The battle unit conducts a battle based on the recipe evaluated by the evaluation unit. For example, users make different dishes using the same ingredients and receive ratings within the community. The ranking unit calculates the food waste contribution based on the results of the battle conducted by the battle unit. For example, the food waste contribution is calculated based on the amount and type of ingredients used by the user and reflected in the ranking. In this way, the food waste reduction system according to the embodiment allows users to easily tackle the food waste problem. For example, by trying a new dish using ingredients left over in the refrigerator, they can make effective use of ingredients without wasting them. Furthermore, by sharing and rating within the community, users can contribute to reducing food waste while having fun interacting with other users.

[0030] The analysis unit can evaluate the freshness and quality of ingredients and suggest the best time to use them. For example, when analyzing images of ingredients, the analysis unit uses an algorithm in which the generative AI evaluates the freshness of ingredients. For example, it can determine the freshness of a tomato from its color and shape and suggest the best time to use it. The analysis unit can also take into account the origin and cultivation method of ingredients to evaluate their quality. For example, it can prioritize organic and locally produced ingredients. This makes it possible to reduce food waste by evaluating the freshness and quality of ingredients and suggesting the best time to use them.

[0031] The analysis unit can refer to the user's past cooking history and perform analysis taking into account frequently used ingredients and preferred seasonings. For example, the analysis unit stores the user's past cooking history in a database, and the generation AI refers to that data to identify frequently used ingredients. For example, it prioritizes the analysis of tomatoes and cheese, which the user often uses. The analysis unit can also refer to past ratings and feedback to take into account the user's preferred seasonings. For example, it can learn the characteristics of recipes that the user has given high ratings and suggest similar recipes. This makes it possible to provide the user with the optimal recipe by referring to the user's past cooking history and performing analysis taking into account frequently used ingredients and preferred seasonings.

[0032] The ingredient input unit can add voice input and barcode scanning to improve user convenience. For example, the ingredient input unit can add a voice input function as an ingredient input method, allowing the user to input ingredients simply by speaking the name of the ingredient. For example, if the user speaks "tomato," the generation AI will recognize and analyze it. The ingredient input unit can also add a barcode scanning function, allowing the user to input ingredients simply by scanning the barcode of the ingredient. For example, the barcode can be scanned using a smartphone camera to automatically obtain ingredient information. This can improve user convenience by adding voice input and barcode scanning.

[0033] The analysis unit can provide nutritional value and calorie information based on the analysis results of ingredients, which can be useful for health management for users. For example, the analysis unit uses the generation AI to calculate the nutritional value of each ingredient based on the analysis results of the ingredients and provide this to the user. For example, it displays the vitamin C content and calories of a tomato. In addition, the analysis unit can take into account changes in calories depending on the cooking method in order to provide calorie information for ingredients. For example, it provides information taking into account changes in calories depending on the cooking method, such as fried or baked. In this way, providing nutritional value and calorie information based on the analysis results of ingredients can be useful for health management for users.

[0034] The analysis unit can analyze the uploaded image, identify the origin and producer information of the ingredients, and provide it to the user. The analysis unit can, for example, use image analysis technology to identify the origin information of the ingredients and provide it to the user. For example, it can display that tomatoes are from Italy. The analysis unit can also refer to traceability data to identify the producer information of the ingredients. For example, it can analyze the label information of the ingredients and display the name and location of the producer. In this way, the reliability and quality of the ingredients can be improved by identifying the origin and producer information of the ingredients and providing it to the user.

[0035] The analysis unit can take into account the user's past consumption patterns and suggest appropriate amounts. For example, the analysis unit stores the user's past consumption patterns in a database, and the generation AI refers to that data to suggest appropriate amounts of ingredients. For example, it suggests appropriate amounts based on the amount of tomatoes the user has used in the past. The analysis unit can also refer to past purchase history and frequency of use to take into account the user's consumption patterns. For example, it makes suggestions based on the amount of ingredients the user frequently purchases. In this way, by taking into account the user's past consumption patterns and suggesting appropriate amounts, it is possible to reduce food waste.

[0036] The analysis unit can perform analysis by combining multiple images to improve the accuracy of image analysis. For example, the analysis unit allows a user to upload multiple images to improve the accuracy of image analysis. For example, images of tomatoes taken from different angles are combined and analyzed. The analysis unit can also perform analysis using images taken continuously. For example, the shape and size of ingredients are accurately identified based on images taken continuously. This allows the accuracy of image analysis to be improved by combining multiple images and performing analysis.

[0037] The analysis unit can identify the types and amounts of ingredients by taking into account the user's allergy information and dietary restrictions. For example, the analysis unit stores the user's allergy information in a database, and the generation AI references that information to identify the types and amounts of ingredients. For example, it can exclude ingredients to which the user is allergic. The analysis unit can also refer to a doctor's instructions or the user's self-reporting to take into account the user's dietary restrictions. For example, it can prioritize identifying low-carbohydrate and low-salt ingredients. This makes it possible to identify the types and amounts of ingredients that are appropriate for the user by taking into account the user's allergy information and dietary restrictions.

[0038] The recipe generation unit can take into account the user's past ratings and feedback to suggest recipes that better suit their preferences. For example, the recipe generation unit makes suggestions that match preferences when the generation AI generates recipes based on the user's past rating data. For example, it learns the characteristics of recipes that the user has given high ratings to suggest similar recipes. The recipe generation unit can also refer to surveys and comments to take user feedback into consideration. For example, if a user comments that they "like spicier dishes," it will suggest spicy recipes. In this way, by taking into account the user's past ratings and feedback, it can suggest recipes that better suit their preferences.

[0039] The recipe generation unit can consider the season and weather when generating a recipe and suggest the most suitable dish. For example, the recipe generation unit suggests the most suitable dish when the generation AI generates a recipe based on seasonal and weather data. For example, it suggests cold dishes in summer and hot dishes in winter. The recipe generation unit can also consider seasonal ingredients. For example, it suggests recipes using fresh vegetables in spring. This makes it possible to suggest the most suitable dish to the user by considering the season and weather.

[0040] The recipe generation unit can incorporate cuisine from different cultures and regions to provide users with new food experiences. For example, the recipe generation unit trains the generation AI with food data from different cultures and regions and incorporates that data when generating recipes. For example, it can suggest recipes that combine Italian and Japanese cuisine. The recipe generation unit can also suggest traditional dishes or unusual ingredients to provide users with new food experiences. For example, it can suggest recipes that use ingredients that the user does not normally use. This makes it possible to provide users with new food experiences by incorporating cuisine from different cultures and regions.

[0041] The recipe generation unit can suggest healthy recipes by taking into account the user's health condition and nutritional balance. For example, the recipe generation unit makes healthy suggestions when the generation AI generates recipes based on the user's health condition data. For example, it can suggest low-calorie, high-nutrition recipes. The recipe generation unit can also evaluate the vitamin and mineral content to consider the user's nutritional balance. For example, it can suggest recipes using ingredients rich in vitamin C. This makes it possible to suggest healthy recipes by taking into account the user's health condition and nutritional balance.

[0042] The recipe generation unit can propose optimal recipes by taking into account the compatibility of flavors between ingredient combinations. For example, to consider the compatibility of flavors between ingredient combinations, the recipe generation unit uses a generation AI to learn from past recipe data and propose optimal combinations. For example, a recipe is generated based on the compatibility of tomatoes and basil. The recipe generation unit can also consider how seasonings are used. For example, it can propose recipes using specific seasonings. This allows the unit to propose optimal recipes to users by taking into account the compatibility of flavors between ingredient combinations.

[0043] The recipe generation unit can take into account the user's cooking skill level and suggest recipes of an appropriate level of difficulty. For example, the recipe generation unit stores the user's cooking skill level in a database, and the generation AI refers to that data to suggest recipes of an appropriate level of difficulty. For example, it suggests easy recipes for beginners. The recipe generation unit can also take into account an evaluation of the user's cooking skills. For example, it makes suggestions based on past cooking experience. In this way, it is possible to suggest recipes of an appropriate level of difficulty by taking into account the user's cooking skill level.

[0044] The recipe generation unit can propose customized recipes by taking into account the user's ingredient preferences and allergy information. For example, the recipe generation unit stores the user's ingredient preferences and allergy information in a database, and the generation AI refers to that data to propose customized recipes. For example, ingredients that the user prefers are used preferentially. The recipe generation unit can also refer to medical data to consider the user's allergy information. For example, recipes can be generated by excluding ingredients that the user is allergic to. This makes it possible to propose customized recipes by taking into account the user's ingredient preferences and allergy information.

[0045] The recipe generation unit can consider the user's dietary goals and suggest the most suitable recipe. For example, the recipe generation unit stores the user's dietary goals in a database, and the generation AI refers to that data to suggest the most suitable recipe. For example, low-calorie recipes are suggested to a user who is on a diet. The recipe generation unit can also set goals such as maintaining health or building muscle in order to consider the user's dietary goals. For example, high-protein recipes are suggested to a user who is aiming to build muscle. In this way, the most suitable recipes can be suggested by considering the user's dietary goals.

[0046] The sharing unit allows the generation AI to automatically evaluate shared photos of food and provide feedback to the user. For example, the sharing unit introduces an algorithm that analyzes shared photos of food and allows the generation AI to automatically evaluate. For example, it evaluates the appearance and presentation of the food and provides feedback to the user. The sharing unit can also allow the generation AI to make improvement suggestions based on user feedback. For example, it makes suggestions based on comments such as "It would be better if the colors were more vibrant." In this way, the generation AI can automatically evaluate shared photos of food and provide feedback to the user, thereby improving the quality of the food.

[0047] The sharing unit allows the generation AI to make improvement suggestions for shared food photos based on comments and ratings from other users. The sharing unit, for example, analyzes other users' comments and ratings on shared food photos and builds a system in which the generation AI makes improvement suggestions. For example, the sharing unit makes suggestions based on comments such as "It would be better if the colors were more vibrant." The sharing unit can also allow the generation AI to make improvement suggestions based on user feedback. For example, the sharing unit makes suggestions based on comments such as "It would be good to think about how the food is presented." In this way, the generation AI can make improvement suggestions based on comments and ratings from other users, thereby improving the quality of the food.

[0048] The sharing unit allows the generation AI to automatically generate recipes based on shared photos of dishes and suggest them to other users. The sharing unit, for example, analyzes shared photos of dishes and builds a system in which the generation AI automatically generates recipes. For example, it identifies ingredients from the photo and suggests recipes using those ingredients. The sharing unit can also allow the generation AI to suggest new recipes based on user feedback. For example, it makes suggestions based on comments such as "I suggest a side dish that goes well with this dish." This allows the generation AI to automatically generate recipes based on shared photos of dishes and suggest them to other users, thereby increasing the diversity of recipes.

[0049] The sharing unit allows the generation AI to automatically provide nutritional value and calorie information based on shared photos of dishes. For example, the sharing unit analyzes shared photos of dishes and builds a system in which the generation AI automatically provides nutritional value and calorie information. For example, it identifies ingredients from the photo and displays their nutritional value. The sharing unit can also allow the generation AI to provide nutritional value and calorie information based on user feedback. For example, it provides information based on comments such as "I want to know the calories in this dish." This allows the generation AI to automatically provide nutritional value and calorie information based on shared photos of dishes, which can be useful for users' health management.

[0050] The battle section can add a function to the battle evaluation criteria in which the generation AI automatically evaluates the creativity and appearance of dishes. For example, the battle section can introduce an algorithm in which the generation AI evaluates the creativity of dishes and reflect this in the battle evaluation criteria. For example, it can evaluate unique ingredient combinations and cooking methods. The battle section can also take color and plating into account to evaluate the appearance of dishes. For example, it can evaluate the beauty of presentation. This allows the generation AI to automatically evaluate the creativity and appearance of dishes, thereby improving the battle evaluation criteria.

[0051] In the battle section, the generation AI can make suggestions to the user for improvements to be made to the user for the next battle based on the results of the battle. For example, the battle section will analyze the results of the battle and build a system in which the generation AI makes suggestions to the user for improvements to be made to the user for the next battle. For example, the system may make a suggestion such as, "Next time, it would be better to present the food more colorfully." The battle section can also make suggestions to the generation AI for improvements based on user feedback. For example, the system may make suggestions based on comments such as, "Suggest a way to shorten cooking time." In this way, the generation AI can make suggestions to improve the user's cooking skills by making suggestions to improve based on the results of the battle.

[0052] The battle section can add an evaluation that takes into account the user's health condition and nutritional balance to the battle evaluation criteria. The battle section can add a health factor to the battle evaluation criteria, for example, based on the user's health condition data. For example, a high score can be given to a low-calorie, highly nutritious dish. The battle section can also evaluate the vitamin and mineral content to take into account the user's nutritional balance. For example, a high score can be given to a dish that uses ingredients rich in vitamin C. In this way, the battle evaluation criteria can be improved by adding an evaluation that takes into account the user's health condition and nutritional balance.

[0053] The battle section allows the generation AI to suggest new recipes to users based on the results of the battle. The battle section, for example, analyzes the results of the battle and builds a system in which the generation AI suggests new recipes to users. For example, it may suggest new recipes based on ingredients and cooking methods that received high marks in the battle. The battle section can also allow the generation AI to suggest new recipes based on user feedback. For example, it may make suggestions based on comments such as "I'd like to suggest another recipe using these ingredients." This allows the generation AI to suggest new recipes based on the results of the battle, broadening the range of cooking available to users.

[0054] When calculating the food waste contribution, the ranking unit allows the generation AI to consider the freshness and quality of ingredients and suggest the optimal time to use them. For example, the ranking unit may have the generation AI evaluate the freshness and quality of ingredients and reflect this in the calculation of food waste contribution. For example, using ingredients before they lose their freshness will result in a higher contribution. The ranking unit may also consider the origin and cultivation method of ingredients to evaluate their quality. For example, it may prioritize organic ingredients and locally produced ingredients. This allows the generation AI to consider the freshness and quality of ingredients and suggest the optimal time to use them, thereby improving the food waste contribution.

[0055] The ranking unit can take into account the user's past consumption patterns when calculating the food waste contribution and suggest appropriate amounts. For example, the ranking unit stores the user's past consumption patterns in a database, and the generation AI refers to that data to suggest appropriate amounts. For example, it suggests appropriate amounts based on the amounts of ingredients the user has used in the past. The ranking unit can also refer to the user's past purchase history and frequency of use to take the user's consumption patterns into account. For example, it makes suggestions based on the amounts of ingredients the user frequently purchases. In this way, it is possible to improve the food waste contribution by taking the user's past consumption patterns into account and suggesting appropriate amounts.

[0056] The ranking unit can take into account the user's allergy information and dietary restrictions when calculating the food waste contribution. For example, the ranking unit stores the user's allergy information in a database, and the generation AI references that information to calculate the food waste contribution. For example, it may calculate the food waste contribution by excluding ingredients to which the user is allergic. The ranking unit can also refer to a doctor's instructions or the user's self-reporting to take into account the user's dietary restrictions. For example, it may prioritize low-sugar and low-salt ingredients. In this way, the food waste contribution can be improved by taking into account the user's allergy information and dietary restrictions.

[0057] The ranking unit can take into account the user's health condition and nutritional balance when calculating the food waste contribution. For example, the ranking unit uses a generation AI to calculate the food waste contribution based on the user's health condition data. For example, using low-calorie, highly nutritious ingredients can result in a high contribution. The ranking unit can also evaluate the vitamin and mineral content to take into account the user's nutritional balance. For example, dishes that use ingredients rich in vitamin C can be given a high score. This makes it possible to improve the food waste contribution by taking into account the user's health condition and nutritional balance.

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

[0059] The analysis unit can not only evaluate the freshness and quality of ingredients, but also suggest ways to store them. For example, it can suggest refrigerating tomatoes before they lose their freshness. The analysis unit can also predict the shelf life of ingredients and notify the user. For example, it can recommend refrigerating tomatoes and consuming them within three days. This can further reduce food waste by suggesting ways to store ingredients.

[0060] The recipe generation unit can propose customized recipes by taking into account the user's ingredient preferences and allergy information. For example, ingredients to which the user is allergic can be excluded and preferred ingredients can be used preferentially. The recipe generation unit can also refer to medical data to take into account the user's dietary restrictions. This allows the recipe generation unit to propose customized recipes by taking into account the user's ingredient preferences and allergy information.

[0061] The analysis unit can provide nutritional value and calorie information based on the analysis results of ingredients, which can be useful for the user's health management. For example, it can display the vitamin C content and calories of tomatoes. In addition, the analysis unit can take into account changes in calories due to cooking methods in order to provide calorie information for ingredients. In this way, providing nutritional value and calorie information based on the analysis results of ingredients can be useful for the user's health management.

[0062] The analysis unit can analyze the uploaded image to identify the origin and producer information of the ingredients and provide it to the user. For example, it can display that the tomatoes are from Italy. The analysis unit can also refer to traceability data to identify the producer information of the ingredients. This allows the reliability and quality of ingredients to be improved by identifying the origin and producer information of the ingredients and providing it to the user.

[0063] The analysis unit can take into account the user's past consumption patterns and suggest appropriate amounts. For example, it can suggest appropriate amounts based on the amount of tomatoes the user has used in the past. The analysis unit can also refer to the user's past purchase history and frequency of use to consider the user's consumption patterns. This allows the analysis unit to reduce food waste by taking into account the user's past consumption patterns and suggesting appropriate amounts.

[0064] The analysis unit can combine multiple images to perform analysis in order to improve the accuracy of image analysis. For example, images of tomatoes taken from different angles can be combined and analyzed. The analysis unit can also perform analysis using images taken continuously. This allows the accuracy of image analysis to be improved by combining multiple images and performing analysis.

[0065] The processing flow of the first embodiment will be briefly explained below.

[0066] Step 1: The ingredient input unit allows the user to input ingredients they have on hand. For example, the user can take a photo of the ingredient with a camera and upload the image to the application. The user can also manually enter the name of the ingredient. For example, if the user enters "tomato," the system will recognize the ingredient. Step 2: The analysis unit analyzes the ingredients input by the ingredient input unit. For example, the generation AI can use image analysis technology to identify the type and quantity of ingredients. It can also evaluate the freshness and quality of ingredients. For example, it can determine the freshness of a tomato based on its color and shape. Step 3: The recipe generation unit generates a recipe based on the ingredients analyzed by the analysis unit. For example, the generation AI suggests a new pasta recipe using tomatoes, cheese, and basil. It can also refer to the user's past cooking history and generate recipes taking into account frequently used ingredients and preferred seasonings. Step 4: The sharing unit shares the recipe generated by the recipe generation unit. For example, a user may share the generated recipe within a community so that other users can view the recipe. Step 5: The rating unit rates the recipe shared by the sharing unit. For example, other users can leave star ratings or comments on the recipe. Step 6: The battle section engages in a battle based on the recipes evaluated by the evaluation section. For example, users make different dishes using the same ingredients and receive evaluations from the community. Step 7: The ranking unit calculates the food waste contribution based on the results of the battle conducted by the battle unit. For example, the ranking unit calculates the food waste contribution based on the amount and type of ingredients used by the user and reflects this in the ranking.

[0067] (Example 2) The food waste countermeasure system according to an embodiment of the present invention uses a generation AI to generate unique recipes based on ingredients owned by the user, and allows users to share, rate, battle, and rank recipes within the community. This allows users to easily tackle the food waste problem.

[0068] The food waste reduction system according to the embodiment includes an ingredient input unit, an analysis unit, a recipe generation unit, a sharing unit, an evaluation unit, a battle unit, and a ranking unit. The ingredient input unit inputs ingredients owned by the user. For example, the user may take a photo of the ingredient with a camera and upload the image to an application. The ingredient input unit can also manually input the name of an ingredient. For example, if the user inputs "tomato," the system recognizes the ingredient. The analysis unit analyzes the ingredients input by the ingredient input unit. For example, the generation AI may use image analysis technology to identify the type and quantity of the ingredient. The analysis unit can also evaluate the freshness and quality of the ingredient. For example, it may determine the freshness of a tomato based on its color and shape. The recipe generation unit generates a recipe based on the ingredients analyzed by the analysis unit. For example, the generation AI may suggest a new pasta recipe using tomatoes, cheese, and basil. The recipe generation unit can also generate recipes by referencing the user's past cooking history and taking into account frequently used ingredients and preferred seasonings. The sharing unit shares the recipes generated by the recipe generation unit. For example, a user can share a recipe they created within a community, allowing other users to view the recipe. The evaluation unit evaluates the recipe shared by the sharing unit. For example, other users can leave star ratings and comments on the recipe. The battle unit conducts a battle based on the recipe evaluated by the evaluation unit. For example, users make different dishes using the same ingredients and receive ratings within the community. The ranking unit calculates the food waste contribution based on the results of the battle conducted by the battle unit. For example, the food waste contribution is calculated based on the amount and type of ingredients used by the user and reflected in the ranking. In this way, the food waste reduction system according to the embodiment allows users to easily tackle the food waste problem. For example, by trying a new dish using ingredients left over in the refrigerator, they can make effective use of ingredients without wasting them. Furthermore, by sharing and rating within the community, users can contribute to reducing food waste while having fun interacting with other users.

[0069] The analysis unit can evaluate the freshness and quality of ingredients and suggest the best time to use them. For example, when analyzing images of ingredients, the analysis unit uses an algorithm in which the generative AI evaluates the freshness of ingredients. For example, it can determine the freshness of a tomato from its color and shape and suggest the best time to use it. The analysis unit can also take into account the origin and cultivation method of ingredients to evaluate their quality. For example, it can prioritize organic and locally produced ingredients. This makes it possible to reduce food waste by evaluating the freshness and quality of ingredients and suggesting the best time to use them.

[0070] The analysis unit can refer to the user's past cooking history and perform analysis taking into account frequently used ingredients and preferred seasonings. For example, the analysis unit stores the user's past cooking history in a database, and the generation AI refers to that data to identify frequently used ingredients. For example, it prioritizes the analysis of tomatoes and cheese, which the user often uses. The analysis unit can also refer to past ratings and feedback to take into account the user's preferred seasonings. For example, it can learn the characteristics of recipes that the user has given high ratings and suggest similar recipes. This makes it possible to provide the user with the optimal recipe by referring to the user's past cooking history and performing analysis taking into account frequently used ingredients and preferred seasonings.

[0071] The analysis unit can use the emotion estimation function to analyze the emotion of the user when entering ingredients and suggest ingredients that will elicit positive emotions. For example, the analysis unit can use the emotion estimation function to analyze the user's facial expression and voice when entering ingredients and calculate an emotion score. For example, if the user is smiling while entering ingredients, the analysis unit can suggest ingredients that will elicit positive emotions. The analysis unit can also use facial expression recognition technology and voice analysis technology to analyze the user's emotions. For example, the analysis unit can analyze changes in the user's facial expression and tone of voice and calculate an emotion score. This allows the analysis of the user's emotions and suggest ingredients that will elicit positive emotions, thereby improving user satisfaction.

[0072] The ingredient input unit can add voice input and barcode scanning to improve user convenience. For example, the ingredient input unit can add a voice input function as an ingredient input method, allowing the user to input ingredients simply by speaking the name of the ingredient. For example, if the user speaks "tomato," the generation AI will recognize and analyze it. The ingredient input unit can also add a barcode scanning function, allowing the user to input ingredients simply by scanning the barcode of the ingredient. For example, the barcode can be scanned using a smartphone camera to automatically obtain ingredient information. This can improve user convenience by adding voice input and barcode scanning.

[0073] The analysis unit can provide nutritional value and calorie information based on the analysis results of ingredients, which can be useful for health management for users. For example, the analysis unit uses the generation AI to calculate the nutritional value of each ingredient based on the analysis results of the ingredients and provide this to the user. For example, it displays the vitamin C content and calories of a tomato. In addition, the analysis unit can take into account changes in calories depending on the cooking method in order to provide calorie information for ingredients. For example, it provides information taking into account changes in calories depending on the cooking method, such as fried or baked. In this way, providing nutritional value and calorie information based on the analysis results of ingredients can be useful for health management for users.

[0074] The analysis unit can use the emotion estimation function to analyze the user's emotions in real time when entering ingredients and provide interactive feedback to make the input process more enjoyable. For example, the analysis unit can use the emotion estimation function to analyze the user's emotions in real time when entering ingredients and provide positive feedback. For example, if the user is smiling while entering ingredients, the analysis unit can display "Great choice!". The analysis unit can also use facial expression recognition technology and voice analysis technology to analyze the user's emotions in real time. For example, the analysis unit can analyze changes in the user's facial expression and tone of voice to calculate an emotion score. This allows the analysis of the user's emotions in real time and provides interactive feedback to make the input process more enjoyable, thereby improving user satisfaction.

[0075] The analysis unit can analyze the uploaded image, identify the origin and producer information of the ingredients, and provide it to the user. The analysis unit can, for example, use image analysis technology to identify the origin information of the ingredients and provide it to the user. For example, it can display that tomatoes are from Italy. The analysis unit can also refer to traceability data to identify the producer information of the ingredients. For example, it can analyze the label information of the ingredients and display the name and location of the producer. In this way, the reliability and quality of the ingredients can be improved by identifying the origin and producer information of the ingredients and providing it to the user.

[0076] The analysis unit can take into account the user's past consumption patterns and suggest appropriate amounts. For example, the analysis unit stores the user's past consumption patterns in a database, and the generation AI refers to that data to suggest appropriate amounts of ingredients. For example, it suggests appropriate amounts based on the amount of tomatoes the user has used in the past. The analysis unit can also refer to past purchase history and frequency of use to take into account the user's consumption patterns. For example, it makes suggestions based on the amount of ingredients the user frequently purchases. In this way, by taking into account the user's past consumption patterns and suggesting appropriate amounts, it is possible to reduce food waste.

[0077] The analysis unit can use the emotion estimation function to analyze the emotion of the user when entering the amount of ingredients and suggest amounts that will elicit positive emotions. For example, the analysis unit can use the emotion estimation function to analyze the emotion of the user when entering the amount of ingredients and suggest amounts that will elicit positive emotions. For example, if the user is smiling when entering the amount, the analysis unit can suggest an appropriate amount. The analysis unit can also use facial expression recognition technology or voice analysis technology to analyze the user's emotion. For example, the analysis unit can analyze changes in the user's facial expression and tone of voice to calculate an emotion score. This allows the analysis of the user's emotion and suggest amounts that will elicit positive emotions, thereby improving user satisfaction.

[0078] The analysis unit can perform analysis by combining multiple images to improve the accuracy of image analysis. For example, the analysis unit allows a user to upload multiple images to improve the accuracy of image analysis. For example, images of tomatoes taken from different angles are combined and analyzed. The analysis unit can also perform analysis using images taken continuously. For example, the shape and size of ingredients are accurately identified based on images taken continuously. This allows the accuracy of image analysis to be improved by combining multiple images and performing analysis.

[0079] The analysis unit can identify the types and amounts of ingredients by taking into account the user's allergy information and dietary restrictions. For example, the analysis unit stores the user's allergy information in a database, and the generation AI references that information to identify the types and amounts of ingredients. For example, it can exclude ingredients to which the user is allergic. The analysis unit can also refer to a doctor's instructions or the user's self-reporting to take into account the user's dietary restrictions. For example, it can prioritize identifying low-carbohydrate and low-salt ingredients. This makes it possible to identify the types and amounts of ingredients that are appropriate for the user by taking into account the user's allergy information and dietary restrictions.

[0080] The analysis unit can use the emotion estimation function to analyze the user's emotions in real time when entering the amounts of ingredients and provide interactive feedback to make the input process more enjoyable. For example, the analysis unit can use the emotion estimation function to analyze the user's emotions in real time when entering the amounts of ingredients and provide positive feedback. For example, if the user is entering the amounts of ingredients with a smile, the analysis unit can display "Great choice!". The analysis unit can also use facial expression recognition technology and voice analysis technology to analyze the user's emotions in real time. For example, the analysis unit can analyze changes in the user's facial expression and tone of voice to calculate an emotion score. This makes it possible to analyze the user's emotions in real time and provide interactive feedback to make the input process more enjoyable, thereby improving user satisfaction.

[0081] The recipe generation unit can take into account the user's past ratings and feedback to suggest recipes that better suit their preferences. For example, the recipe generation unit makes suggestions that match preferences when the generation AI generates recipes based on the user's past rating data. For example, it learns the characteristics of recipes that the user has given high ratings to suggest similar recipes. The recipe generation unit can also refer to surveys and comments to take user feedback into consideration. For example, if a user comments that they "like spicier dishes," it will suggest spicy recipes. In this way, by taking into account the user's past ratings and feedback, it can suggest recipes that better suit their preferences.

[0082] The recipe generation unit can consider the season and weather when generating a recipe and suggest the most suitable dish. For example, the recipe generation unit suggests the most suitable dish when the generation AI generates a recipe based on seasonal and weather data. For example, it suggests cold dishes in summer and hot dishes in winter. The recipe generation unit can also consider seasonal ingredients. For example, it suggests recipes using fresh vegetables in spring. This makes it possible to suggest the most suitable dish to the user by considering the season and weather.

[0083] The recipe generation unit can use the emotion estimation function to analyze the emotion a user feels when viewing a recipe and suggest recipes that elicit positive emotions. For example, the recipe generation unit can use the emotion estimation function to analyze the emotion a user feels when viewing a recipe and suggest recipes that elicit positive emotions. For example, if a user is smiling while viewing a recipe, the recipe generation unit can suggest similar recipes. The recipe generation unit can also use facial expression recognition technology and voice analysis technology to analyze the user's emotions. For example, the recipe generation unit can analyze changes in the user's facial expression and tone of voice to calculate an emotion score. This allows the recipe generation unit to analyze the user's emotions and suggest recipes that elicit positive emotions, thereby improving user satisfaction.

[0084] The recipe generation unit can incorporate cuisine from different cultures and regions to provide users with new food experiences. For example, the recipe generation unit trains the generation AI with food data from different cultures and regions and incorporates that data when generating recipes. For example, it can suggest recipes that combine Italian and Japanese cuisine. The recipe generation unit can also suggest traditional dishes or unusual ingredients to provide users with new food experiences. For example, it can suggest recipes that use ingredients that the user does not normally use. This makes it possible to provide users with new food experiences by incorporating cuisine from different cultures and regions.

[0085] The recipe generation unit can suggest healthy recipes by taking into account the user's health condition and nutritional balance. For example, the recipe generation unit makes healthy suggestions when the generation AI generates recipes based on the user's health condition data. For example, it can suggest low-calorie, high-nutrition recipes. The recipe generation unit can also evaluate the vitamin and mineral content to consider the user's nutritional balance. For example, it can suggest recipes using ingredients rich in vitamin C. This makes it possible to suggest healthy recipes by taking into account the user's health condition and nutritional balance.

[0086] The recipe generation unit can use the emotion estimation function to analyze the user's emotions in real time when browsing a recipe and provide interactive feedback to make the browsing process more enjoyable. For example, the recipe generation unit can use the emotion estimation function to analyze the user's emotions in real time when browsing a recipe and provide positive feedback. For example, if the user is smiling while browsing a recipe, the recipe generation unit can display "Great choice!". The recipe generation unit can also use facial expression recognition technology and voice analysis technology to analyze the user's emotions in real time. For example, the recipe generation unit can analyze changes in the user's facial expression and tone of voice to calculate an emotion score. This allows the recipe generation unit to analyze the user's emotions in real time and provide interactive feedback to make the browsing process more enjoyable, thereby improving user satisfaction.

[0087] The recipe generation unit can propose optimal recipes by taking into account the compatibility of flavors between ingredient combinations. For example, to consider the compatibility of flavors between ingredient combinations, the recipe generation unit uses a generation AI to learn from past recipe data and propose optimal combinations. For example, a recipe is generated based on the compatibility of tomatoes and basil. The recipe generation unit can also consider how seasonings are used. For example, it can propose recipes using specific seasonings. This allows the unit to propose optimal recipes to users by taking into account the compatibility of flavors between ingredient combinations.

[0088] The recipe generation unit can take into account the user's cooking skill level and suggest recipes of an appropriate level of difficulty. For example, the recipe generation unit stores the user's cooking skill level in a database, and the generation AI refers to that data to suggest recipes of an appropriate level of difficulty. For example, it suggests easy recipes for beginners. The recipe generation unit can also take into account an evaluation of the user's cooking skills. For example, it makes suggestions based on past cooking experience. In this way, it is possible to suggest recipes of an appropriate level of difficulty by taking into account the user's cooking skill level.

[0089] The recipe generation unit can use the emotion estimation function to analyze the emotion a user feels when viewing a recipe and suggest recipes that elicit positive emotions. For example, the recipe generation unit can use the emotion estimation function to analyze the emotion a user feels when viewing a recipe and suggest recipes that elicit positive emotions. For example, if a user is smiling while viewing a recipe, the recipe generation unit can suggest similar recipes. The recipe generation unit can also use facial expression recognition technology and voice analysis technology to analyze the user's emotions. For example, the recipe generation unit can analyze changes in the user's facial expression and tone of voice to calculate an emotion score. This allows the recipe generation unit to analyze the user's emotions and suggest recipes that elicit positive emotions, thereby improving user satisfaction.

[0090] The recipe generation unit can propose customized recipes by taking into account the user's ingredient preferences and allergy information. For example, the recipe generation unit stores the user's ingredient preferences and allergy information in a database, and the generation AI refers to that data to propose customized recipes. For example, ingredients that the user prefers are used preferentially. The recipe generation unit can also refer to medical data to consider the user's allergy information. For example, recipes can be generated by excluding ingredients that the user is allergic to. This makes it possible to propose customized recipes by taking into account the user's ingredient preferences and allergy information.

[0091] The recipe generation unit can consider the user's dietary goals and suggest the most suitable recipe. For example, the recipe generation unit stores the user's dietary goals in a database, and the generation AI refers to that data to suggest the most suitable recipe. For example, low-calorie recipes are suggested to a user who is on a diet. The recipe generation unit can also set goals such as maintaining health or building muscle in order to consider the user's dietary goals. For example, high-protein recipes are suggested to a user who is aiming to build muscle. In this way, the most suitable recipes can be suggested by considering the user's dietary goals.

[0092] The recipe generation unit can use the emotion estimation function to analyze the user's emotions in real time when browsing a recipe and provide interactive feedback to make the browsing process more enjoyable. For example, the recipe generation unit can use the emotion estimation function to analyze the user's emotions in real time when browsing a recipe and provide positive feedback. For example, if the user is smiling while browsing a recipe, the recipe generation unit can display "Great choice!". The recipe generation unit can also use facial expression recognition technology and voice analysis technology to analyze the user's emotions in real time. For example, the recipe generation unit can analyze changes in the user's facial expression and tone of voice to calculate an emotion score. This allows the recipe generation unit to analyze the user's emotions in real time and provide interactive feedback to make the browsing process more enjoyable, thereby improving user satisfaction.

[0093] The sharing unit allows the generation AI to automatically evaluate shared photos of food and provide feedback to the user. For example, the sharing unit introduces an algorithm that analyzes shared photos of food and allows the generation AI to automatically evaluate. For example, it evaluates the appearance and presentation of the food and provides feedback to the user. The sharing unit can also allow the generation AI to make improvement suggestions based on user feedback. For example, it makes suggestions based on comments such as "It would be better if the colors were more vibrant." In this way, the generation AI can automatically evaluate shared photos of food and provide feedback to the user, thereby improving the quality of the food.

[0094] The sharing unit allows the generation AI to make improvement suggestions for shared food photos based on comments and ratings from other users. The sharing unit, for example, analyzes other users' comments and ratings on shared food photos and builds a system in which the generation AI makes improvement suggestions. For example, the sharing unit makes suggestions based on comments such as "It would be better if the colors were more vibrant." The sharing unit can also allow the generation AI to make improvement suggestions based on user feedback. For example, the sharing unit makes suggestions based on comments such as "It would be good to think about how the food is presented." In this way, the generation AI can make improvement suggestions based on comments and ratings from other users, thereby improving the quality of the food.

[0095] The sharing unit can use the emotion estimation function to analyze the emotion a user feels when sharing and provide a sharing process that elicits positive emotions. For example, the sharing unit can use the emotion estimation function to analyze the emotion a user feels when sharing a dish and provide positive feedback. For example, if the user is sharing with a smile, the sharing unit can display "Great post!". The sharing unit can also use facial expression recognition technology and voice analysis technology to analyze the user's emotion. For example, it can analyze changes in the user's facial expression and tone of voice to calculate an emotion score. This can improve user satisfaction by analyzing the user's emotion and providing a sharing process that elicits positive emotions.

[0096] The sharing unit allows the generation AI to automatically generate recipes based on shared photos of dishes and suggest them to other users. The sharing unit, for example, analyzes shared photos of dishes and builds a system in which the generation AI automatically generates recipes. For example, it identifies ingredients from the photo and suggests recipes using those ingredients. The sharing unit can also allow the generation AI to suggest new recipes based on user feedback. For example, it makes suggestions based on comments such as "I suggest a side dish that goes well with this dish." This allows the generation AI to automatically generate recipes based on shared photos of dishes and suggest them to other users, thereby increasing the diversity of recipes.

[0097] The sharing unit allows the generation AI to automatically provide nutritional value and calorie information based on shared photos of dishes. For example, the sharing unit analyzes shared photos of dishes and builds a system in which the generation AI automatically provides nutritional value and calorie information. For example, it identifies ingredients from the photo and displays their nutritional value. The sharing unit can also allow the generation AI to provide nutritional value and calorie information based on user feedback. For example, it provides information based on comments such as "I want to know the calories in this dish." This allows the generation AI to automatically provide nutritional value and calorie information based on shared photos of dishes, which can be useful for users' health management.

[0098] The sharing unit can use the emotion estimation function to analyze the user's emotions when sharing in real time and provide interactive feedback to make the sharing process more enjoyable. For example, the sharing unit can use the emotion estimation function to analyze the user's emotions when sharing a dish in real time and provide positive feedback. For example, if the user is sharing with a smile, the sharing unit can display "Great post!". The sharing unit can also use facial expression recognition technology and voice analysis technology to analyze the user's emotions in real time. For example, the sharing unit can analyze changes in the user's facial expression and tone of voice to calculate an emotion score. This makes it possible to analyze the user's emotions in real time and provide interactive feedback to make the sharing process more enjoyable, thereby improving user satisfaction.

[0099] The battle section can add a function to the battle evaluation criteria in which the generation AI automatically evaluates the creativity and appearance of dishes. For example, the battle section can introduce an algorithm in which the generation AI evaluates the creativity of dishes and reflect this in the battle evaluation criteria. For example, it can evaluate unique ingredient combinations and cooking methods. The battle section can also take color and plating into account to evaluate the appearance of dishes. For example, it can evaluate the beauty of presentation. This allows the generation AI to automatically evaluate the creativity and appearance of dishes, thereby improving the battle evaluation criteria.

[0100] In the battle section, the generation AI can make suggestions to the user for improvements to be made to the user for the next battle based on the results of the battle. For example, the battle section will analyze the results of the battle and build a system in which the generation AI makes suggestions to the user for improvements to be made to the user for the next battle. For example, the system may make a suggestion such as, "Next time, it would be better to present the food more colorfully." The battle section can also make suggestions to the generation AI for improvements based on user feedback. For example, the system may make suggestions based on comments such as, "Suggest a way to shorten cooking time." In this way, the generation AI can make suggestions to improve the user's cooking skills by making suggestions to improve based on the results of the battle.

[0101] The battle unit can use the emotion estimation function to analyze the emotions of a user when participating in a battle and provide a battle process that draws out positive emotions. The battle unit, for example, uses the emotion estimation function to analyze the emotions of a user when participating in a battle and provide positive feedback. For example, if a user participates in a battle with a smile, the battle unit can display "Great challenge!". The battle unit can also use facial expression recognition technology and voice analysis technology to analyze the user's emotions. For example, it can analyze changes in the user's facial expression and tone of voice to calculate an emotion score. This allows the battle unit to analyze the user's emotions and provide a battle process that draws out positive emotions, thereby improving user satisfaction.

[0102] The battle section can add an evaluation that takes into account the user's health condition and nutritional balance to the battle evaluation criteria. The battle section can add a health factor to the battle evaluation criteria, for example, based on the user's health condition data. For example, a high score can be given to a low-calorie, highly nutritious dish. The battle section can also evaluate the vitamin and mineral content to take into account the user's nutritional balance. For example, a high score can be given to a dish that uses ingredients rich in vitamin C. In this way, the battle evaluation criteria can be improved by adding an evaluation that takes into account the user's health condition and nutritional balance.

[0103] The battle section allows the generation AI to suggest new recipes to users based on the results of the battle. The battle section, for example, analyzes the results of the battle and builds a system in which the generation AI suggests new recipes to users. For example, it may suggest new recipes based on ingredients and cooking methods that received high marks in the battle. The battle section can also allow the generation AI to suggest new recipes based on user feedback. For example, it may make suggestions based on comments such as "I'd like to suggest another recipe using these ingredients." This allows the generation AI to suggest new recipes based on the results of the battle, broadening the range of cooking available to users.

[0104] The battle unit can use the emotion estimation function to analyze the emotions of the user when participating in a battle in real time and provide interactive feedback to make the battle process more enjoyable. The battle unit, for example, uses the emotion estimation function to analyze the emotions of the user when participating in a battle in real time and provide positive feedback. For example, if the user is participating in a battle with a smile, it displays "Great challenge!". The battle unit can also use facial expression recognition technology and voice analysis technology to analyze the user's emotions in real time. For example, it can analyze changes in the user's facial expression and tone of voice to calculate an emotion score. This allows the user's emotions to be analyzed in real time and interactive feedback to make the battle process more enjoyable, thereby improving user satisfaction.

[0105] When calculating the food waste contribution, the ranking unit allows the generation AI to consider the freshness and quality of ingredients and suggest the optimal time to use them. For example, the ranking unit may have the generation AI evaluate the freshness and quality of ingredients and reflect this in the calculation of food waste contribution. For example, using ingredients before they lose their freshness will result in a higher contribution. The ranking unit may also consider the origin and cultivation method of ingredients to evaluate their quality. For example, it may prioritize organic ingredients and locally produced ingredients. This allows the generation AI to consider the freshness and quality of ingredients and suggest the optimal time to use them, thereby improving the food waste contribution.

[0106] The ranking unit can take into account the user's past consumption patterns when calculating the food waste contribution and suggest appropriate amounts. For example, the ranking unit stores the user's past consumption patterns in a database, and the generation AI refers to that data to suggest appropriate amounts. For example, it suggests appropriate amounts based on the amounts of ingredients the user has used in the past. The ranking unit can also refer to the user's past purchase history and frequency of use to take the user's consumption patterns into account. For example, it makes suggestions based on the amounts of ingredients the user frequently purchases. In this way, it is possible to improve the food waste contribution by taking the user's past consumption patterns into account and suggesting appropriate amounts.

[0107] The ranking unit can use the emotion estimation function to analyze the emotions of the user when checking the food waste contribution level and provide feedback to elicit positive emotions. The ranking unit, for example, uses the emotion estimation function to analyze the emotions of the user when checking the food waste contribution level and provide positive feedback. For example, if the user is checking with a smile, the ranking unit can display "What a great contribution!" The ranking unit can also use facial expression recognition technology and voice analysis technology to analyze the user's emotions. For example, the ranking unit can analyze changes in the user's facial expression and tone of voice to calculate an emotion score. This makes it possible to analyze the user's emotions and provide feedback to elicit positive emotions, thereby improving user satisfaction.

[0108] The ranking unit can take into account the user's allergy information and dietary restrictions when calculating the food waste contribution. For example, the ranking unit stores the user's allergy information in a database, and the generation AI references that information to calculate the food waste contribution. For example, it may calculate the food waste contribution by excluding ingredients to which the user is allergic. The ranking unit can also refer to a doctor's instructions or the user's self-reporting to take into account the user's dietary restrictions. For example, it may prioritize low-sugar and low-salt ingredients. In this way, the food waste contribution can be improved by taking into account the user's allergy information and dietary restrictions.

[0109] The ranking unit can take into account the user's health condition and nutritional balance when calculating the food waste contribution. For example, the ranking unit uses a generation AI to calculate the food waste contribution based on the user's health condition data. For example, using low-calorie, highly nutritious ingredients can result in a high contribution. The ranking unit can also evaluate the vitamin and mineral content to take into account the user's nutritional balance. For example, dishes that use ingredients rich in vitamin C can be given a high score. This makes it possible to improve the food waste contribution by taking into account the user's health condition and nutritional balance.

[0110] The ranking unit can use the emotion estimation function to analyze the emotions of the user when checking their food waste contribution in real time and provide interactive feedback to make the checking process more enjoyable. The ranking unit, for example, uses the emotion estimation function to analyze the emotions of the user when checking their food waste contribution in real time and provide positive feedback. For example, if the user is checking with a smile, the ranking unit can display "What a great contribution!". The ranking unit can also use facial expression recognition technology and voice analysis technology to analyze the user's emotions in real time. For example, the ranking unit can analyze changes in the user's facial expression and tone of voice to calculate an emotion score. This makes it possible to analyze the user's emotions in real time and provide interactive feedback to make the checking process more enjoyable, thereby improving user satisfaction.

[0111] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0112] The analysis unit can not only evaluate the freshness and quality of ingredients, but also suggest ways to store them. For example, it can suggest refrigerating tomatoes before they lose their freshness. The analysis unit can also predict the shelf life of ingredients and notify the user. For example, it can recommend refrigerating tomatoes and consuming them within three days. This can further reduce food waste by suggesting ways to store ingredients.

[0113] The analysis unit can use the emotion estimation function to analyze the user's emotions when inputting ingredients and make suggestions to reduce stress. For example, if the user is tired, it can suggest easy recipes. The analysis unit can also use facial expression recognition technology and voice analysis technology to analyze the user's emotions. This allows the analysis of the user's emotions and makes suggestions to reduce stress, thereby improving user satisfaction.

[0114] The recipe generation unit can propose customized recipes by taking into account the user's ingredient preferences and allergy information. For example, ingredients to which the user is allergic can be excluded and preferred ingredients can be used preferentially. The recipe generation unit can also refer to medical data to take into account the user's dietary restrictions. This allows the recipe generation unit to propose customized recipes by taking into account the user's ingredient preferences and allergy information.

[0115] The analysis unit can use the emotion estimation function to analyze the emotion a user has when entering ingredients and suggest ingredients that will elicit positive emotions. For example, if a user is smiling while entering ingredients, the analysis unit can suggest ingredients that will elicit positive emotions. The analysis unit can also use facial expression recognition technology and voice analysis technology to analyze the user's emotions. This allows the analysis of the user's emotions and suggests ingredients that will elicit positive emotions, thereby improving user satisfaction.

[0116] The analysis unit can provide nutritional value and calorie information based on the analysis results of ingredients, which can be useful for the user's health management. For example, it can display the vitamin C content and calories of tomatoes. In addition, the analysis unit can take into account changes in calories due to cooking methods in order to provide calorie information for ingredients. In this way, providing nutritional value and calorie information based on the analysis results of ingredients can be useful for the user's health management.

[0117] The analysis unit can use the emotion estimation function to analyze the user's emotions in real time when entering ingredients and provide interactive feedback to make the input process more enjoyable. For example, if the user is smiling when entering ingredients, the analysis unit can display "Great choice!". The analysis unit can also use facial expression recognition technology and voice analysis technology to analyze the user's emotions in real time. This allows the analysis of the user's emotions in real time and provides interactive feedback to make the input process more enjoyable, thereby improving user satisfaction.

[0118] The analysis unit can analyze the uploaded image to identify the origin and producer information of the ingredients and provide it to the user. For example, it can display that the tomatoes are from Italy. The analysis unit can also refer to traceability data to identify the producer information of the ingredients. This allows the reliability and quality of ingredients to be improved by identifying the origin and producer information of the ingredients and providing it to the user.

[0119] The analysis unit can take into account the user's past consumption patterns and suggest appropriate amounts. For example, it can suggest appropriate amounts based on the amount of tomatoes the user has used in the past. The analysis unit can also refer to the user's past purchase history and frequency of use to consider the user's consumption patterns. This allows the analysis unit to reduce food waste by taking into account the user's past consumption patterns and suggesting appropriate amounts.

[0120] The analysis unit can use the emotion estimation function to analyze the user's emotions when inputting the amount of ingredients and suggest portions that will elicit positive emotions. For example, if the user is smiling when inputting, the analysis unit can suggest appropriate portions. The analysis unit can also use facial expression recognition technology and voice analysis technology to analyze the user's emotions. This allows the analysis of the user's emotions and suggest portions that will elicit positive emotions, thereby improving user satisfaction.

[0121] The analysis unit can combine multiple images to perform analysis in order to improve the accuracy of image analysis. For example, images of tomatoes taken from different angles can be combined and analyzed. The analysis unit can also perform analysis using images taken continuously. This allows the accuracy of image analysis to be improved by combining multiple images and performing analysis.

[0122] The processing flow of the second embodiment will be briefly explained below.

[0123] Step 1: The ingredient input unit allows the user to input ingredients they have on hand. For example, the user can take a photo of the ingredient with a camera and upload the image to the application. The user can also manually enter the name of the ingredient. For example, if the user enters "tomato," the system will recognize the ingredient. Step 2: The analysis unit analyzes the ingredients input by the ingredient input unit. For example, the generation AI can use image analysis technology to identify the type and quantity of ingredients. It can also evaluate the freshness and quality of ingredients. For example, it can determine the freshness of a tomato based on its color and shape. Step 3: The recipe generation unit generates a recipe based on the ingredients analyzed by the analysis unit. For example, the generation AI suggests a new pasta recipe using tomatoes, cheese, and basil. It can also refer to the user's past cooking history and generate recipes taking into account frequently used ingredients and preferred seasonings. Step 4: The sharing unit shares the recipe generated by the recipe generation unit. For example, a user may share the generated recipe within a community so that other users can view the recipe. Step 5: The rating unit rates the recipe shared by the sharing unit. For example, other users can leave star ratings or comments on the recipe. Step 6: The battle section engages in a battle based on the recipes evaluated by the evaluation section. For example, users make different dishes using the same ingredients and receive evaluations from the community. Step 7: The ranking unit calculates the food waste contribution based on the results of the battle conducted by the battle unit. For example, the ranking unit calculates the food waste contribution based on the amount and type of ingredients used by the user and reflects this in the ranking.

[0124] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0125] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0126] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

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

[0128] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

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

[0130] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

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

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

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

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

[0135] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0136] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

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

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

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

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

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

[0142] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0143] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0144] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0145] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0146] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0147] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0148] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0149] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0150] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0151] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0152] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0153] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0154] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0155] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0156] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0157] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0158] 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.

[0159] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0160] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0161] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0162] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0163] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0164] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0165] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0166] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0167] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0168] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0169] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0170] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0171] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0172] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0173] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0174] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0175] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0176] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0177] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0178] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0179] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0180] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0181] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0182] 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.

[0183] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0184] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0185] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0186] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0187] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0188] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0189] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0190] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0191] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an ingredient input unit for inputting ingredients possessed by the user; an analysis unit that analyzes the ingredients input by the ingredient input unit; a recipe creation unit that creates a recipe based on the ingredients analyzed by the analysis unit; a sharing unit that shares the recipe generated by the recipe generating unit; an evaluation unit that evaluates the recipe shared by the sharing unit; a battle section that conducts a battle based on the recipes evaluated by the evaluation section; a ranking unit that calculates the food loss contribution level based on the results of the battle conducted by the battle unit; A system characterized by:

2. The ingredient input unit Add voice input and barcode scanning to improve user convenience 2. The system of claim 1.

3. The analysis unit Analyzes uploaded images, identifies the origin and producer information of ingredients, and provides it to the user 2. The system of claim 1.

4. The recipe generation unit Taking into account the user's past ratings and feedback, the app suggests recipes that better suit their preferences.

2. The system of claim 1.

5. The common part is The generation AI automatically evaluates the shared food photos and provides feedback to the user.

2. The system of claim 1.

6. The battle section includes: The battle evaluation criteria will include a feature that allows the AI ​​to automatically evaluate the creativity and appearance of dishes.

2. The system of claim 1.

7. The ranking unit When calculating the food waste contribution, the AI ​​takes into account the freshness and quality of ingredients and suggests the optimal time to use them.

2. The system of claim 1.

8. The analysis unit Analyze the emotions of the user when inputting ingredients and suggest ingredients that will elicit positive emotions 2. The system of claim 1.

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