System

Abstract

An object of a system according to an embodiment is to effectively eliminate food loss in a home.SOLUTION: A system includes a food ingredients management part, a data input part, a use method proposal part, and a recipe generation part. The food ingredient management unit manages information on food ingredients. The data input unit receives information on a foodstuff input by a user. The use method proposal unit proposes a method for using the ingredient on the basis of the ingredient information received by the data input unit. The recipe generation unit generates a specific recipe based on the use method proposed by the use method proposal 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 technology has had the problem of making it difficult to effectively eliminate food waste within the home.

[0005] The system according to the embodiment aims to effectively eliminate food waste within the home. [Means for solving the problem]

[0006] The system according to the embodiment includes an ingredient management unit, a data input unit, a usage suggestion unit, and a recipe creation unit. The ingredient management unit manages ingredient information. The data input unit accepts ingredient information entered by a user. The usage suggestion unit proposes ingredient usage methods based on the ingredient information accepted by the data input unit. The recipe creation unit creates specific recipes based on the usage methods proposed by the usage suggestion unit. [Effects of the Invention]

[0007] The system according to the embodiment can effectively eliminate food waste within the home. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The food waste reduction system according to an embodiment of the present invention is a system in which a generation AI devise and create methods and recipes for using up opened ingredients in order to reduce food waste within the home. As a result, the food waste reduction system can effectively reduce food waste within the home and realize a sustainable diet.

[0029] The food waste reduction system according to the embodiment includes an ingredient management unit, a data input unit, a usage suggestion unit, and a recipe generation unit. The ingredient management unit manages ingredient information, such as the name, quantity, expiration date, and nutritional value of the ingredient. The ingredient management unit also periodically updates the status of the ingredients to maintain the latest information. The data input unit accepts ingredient information entered by a user. For example, a user can manually enter ingredient information. The data input unit can also automatically enter ingredient information using barcode scanning or voice recognition technology. The usage suggestion unit proposes ingredient usage methods based on the ingredient information accepted by the data input unit. For example, the generation AI considers the characteristics and expiration date of the ingredient to propose optimal usage methods. The usage suggestion unit can also analyze the user's past cooking history and preferences to propose personalized usage methods. The recipe generation unit generates specific recipes based on the usage methods proposed by the usage suggestion unit. For example, the generation AI customizes recipes based on the user's preferences and the amount of ingredients. The recipe generation unit can also generate recipes that consider the nutritional value and health benefits of the ingredients. As a result, the food loss reduction system according to the embodiment can effectively reduce food loss within the home. For example, by utilizing recipes and storage methods suggested by the generation AI, it is possible to improve the efficiency of food use and reduce food waste.

[0030] The data input unit can automatically input information about ingredients using barcode scanning. For example, when a user scans an ingredient's barcode using a smartphone camera, information such as the ingredient's type, quantity, and expiration date is automatically entered into the system. The generation AI uses this information to manage ingredients and suggest ways to use them. The data input unit also uses a barcode scanner built into the refrigerator to automatically input data each time ingredients are placed in the refrigerator. The generation AI updates this data in real time and manages the status of the ingredients. The data input unit also scans barcodes on supermarket receipts, which inputs information about purchased ingredients into the system all at once. The generation AI uses this information to manage the household's ingredient inventory and suggest ways to use them. This allows for quick and accurate input of ingredient information.

[0031] The data input unit can input information about ingredients using voice recognition technology. For example, when a user says to a smart speaker, "Add three tomatoes," the voice recognition technology analyzes the information and automatically inputs it into the system. The generation AI manages ingredients based on this information. Alternatively, when a user uses a smartphone's voice assistant to say, "Tell me what ingredients are in the refrigerator," the data input unit uses voice recognition technology to obtain the ingredient information and input it into the system. The generation AI uses this information to suggest ways to use the information. Alternatively, when a user uses a voice recognition device installed in the kitchen to say, "There's not much milk left," the voice recognition technology analyzes the information and inputs it into the system. The generation AI manages ingredients based on this information. This allows users to input ingredient information verbally.

[0032] The data input unit can automatically collect ingredient information in cooperation with other devices in the home. For example, the data input unit uses a smart refrigerator's built-in camera to take pictures of ingredients and automatically inputs the ingredient information into the system using image recognition technology. The generation AI manages ingredients based on this information. The data input unit also uses a smart speaker to analyze user conversations and automatically collect information about ingredients and input it into the system. The generation AI suggests usage methods based on this information. The data input unit also works with smart kitchen devices to automatically input information about ingredients used during cooking into the system. The generation AI manages ingredient inventory based on this information and suggests the next use of ingredients. This makes it possible to automatically collect ingredient information in cooperation with other devices in the home.

[0033] The usage suggestion unit uses image recognition technology to automatically evaluate the condition of ingredients and can suggest usage methods based on that information. For example, the usage suggestion unit uses a camera inside the refrigerator to periodically take images of ingredients and uses image recognition technology to evaluate the freshness of the ingredients and the number of days since opening. The generation AI uses this information to suggest how to use the ingredients. The usage suggestion unit also uses a smartphone camera to take images of ingredients and uses image recognition technology to analyze their condition. The generation AI uses this information to suggest the optimal way to use the ingredients. The usage suggestion unit also uses a camera installed in the kitchen to take images of ingredients and uses image recognition technology to evaluate the condition of the ingredients. The generation AI uses this information to suggest how to use the ingredients. In this way, the condition of ingredients can be automatically evaluated and the optimal way to use them can be suggested.

[0034] The usage suggestion unit can analyze the user's past cooking history and suggest ways to use ingredients that suit their preferences. For example, the generation AI in the usage suggestion unit analyzes the user's past cooking history and suggests ways to use ingredients that suit their preferences. For example, it suggests new recipes based on dishes that the user has liked to make in the past. The usage suggestion unit also suggests ways to use ingredients that suit their preferences based on the user's cooking history data. For example, it suggests recipes that prioritize ingredients that the user uses frequently. The usage suggestion unit also suggests ways to use ingredients that suit their preferences based on the user's past cooking history. For example, it suggests variations of dishes that the user has made in the past. This makes it possible to suggest ways to use ingredients that suit the user's preferences.

[0035] The usage suggestion unit can suggest usage methods that take into account the nutritional value and health benefits of ingredients. For example, the generation AI in the usage suggestion unit analyzes the nutritional value and health benefits of ingredients and suggests usage methods to support a healthy diet. For example, it suggests recipes that use ingredients rich in vitamins and minerals. Furthermore, the generation AI in the usage suggestion unit suggests usage methods that take into account health benefits based on the nutritional value data of ingredients. For example, it suggests recipes that are low in calories and have good nutritional balance. Furthermore, the generation AI in the usage suggestion unit analyzes the nutritional value and health benefits of ingredients and suggests usage methods to support a healthy diet. For example, it suggests recipes to supplement specific nutrients. This makes it possible to suggest usage methods to support a healthy diet.

[0036] The usage suggestion unit can suggest ways to use ingredients according to the season and weather. For example, the generation AI analyzes seasonal and weather data and suggests ways to use ingredients that incorporate a sense of the season based on that information. For example, it suggests cold dishes in summer and hot dishes in winter. The usage suggestion unit also takes into account the characteristics of ingredients in each season and suggests ways to use ingredients that incorporate a sense of the season. For example, it suggests recipes using fresh vegetables in spring. The usage suggestion unit also suggests ways to use ingredients that incorporate a sense of the season based on weather data. For example, it suggests recipes using hot soup on rainy days. In this way, it is possible to suggest ways to use ingredients that incorporate a sense of the season.

[0037] The usage suggestion unit can suggest ways to use ingredients that incorporate recipes from different cultures and regions. For example, the generation AI analyzes recipe data from different cultures and regions and suggests international cuisine variations based on that information. For example, it suggests recipes such as Italian, Mexican, and Asian. The usage suggestion unit also takes into account the characteristics of ingredients from different regions and suggests international cuisine variations. For example, it suggests recipes for Mediterranean cuisine and Chinese cuisine. The usage suggestion unit also analyzes recipe data from different cultures and regions and suggests international cuisine variations based on that information. For example, it suggests recipes for French cuisine and Indian cuisine. This makes it possible to provide international cuisine variations.

[0038] The recipe generation unit can provide customized recipes ranging from beginner to advanced recipes, taking into account the user's cooking skill level. For example, the generation AI analyzes the user's cooking skill level and provides customized recipes ranging from simple recipes for beginners to complex recipes for advanced cooks. For example, the generation AI provides a step-by-step guide for beginners and suggests creative arrangements for advanced cooks. The recipe generation unit also evaluates the cooking skill level of the user based on the user's past cooking history and feedback, and provides recipes accordingly. For example, the generation AI suggests recipes that teach basic cooking techniques to beginners. The recipe generation unit also adjusts the difficulty of the recipe, taking into account the user's cooking skill level. For example, the generation AI suggests challenging dishes for advanced cooks and provides easy, foolproof recipes for beginners. This allows the unit to provide recipes according to the user's cooking skill level.

[0039] The recipe generation unit can propose safe recipes by taking into account allergy information about ingredients. For example, the recipe generation unit uses a generation AI to analyze a user's allergy information and propose safe recipes based on that information. For example, it provides recipes that use ingredients that do not contain allergens. The recipe generation unit also uses a generation AI to generate recipes that avoid allergens based on allergy information entered by the user. For example, it proposes recipes that do not contain nuts to a user with a nut allergy. The recipe generation unit also uses a generation AI to propose safe recipes by taking into account allergy information about ingredients. For example, it provides recipes that do not contain dairy products to a user with a dairy allergy. This makes it possible to propose safe recipes that take allergy information into account.

[0040] The recipe generation unit can provide personalized recipes based on the user's ingredient preferences and past feedback. For example, the recipe generation unit uses a generation AI to analyze the user's past feedback and provide personalized recipes based on that information. For example, it may suggest new recipes based on the user's favorite dishes. The recipe generation unit also uses a generation AI to suggest personalized recipes based on the user's ingredient preferences. For example, it may provide recipes using the user's favorite ingredients. The recipe generation unit also uses a generation AI to analyze the user's past feedback and provide personalized recipes based on that information. For example, it may make new suggestions based on recipes that the user has given a high rating. This makes it possible to provide personalized recipes based on the user's preferences and feedback.

[0041] The recipe generation unit can propose economical recipes by taking into account the cost of ingredients. For example, the recipe generation unit uses a generation AI to analyze ingredient cost data and propose economical recipes. For example, it may provide recipes that use inexpensive ingredients. The recipe generation unit also uses a generation AI to propose recipes that take cost into account based on the user's budget. For example, it may provide recipes in which ingredients are selected to fit within the budget. The recipe generation unit also uses a generation AI to analyze ingredient cost data and propose economical recipes. For example, it may provide recipes that use special sale or seasonal ingredients. This makes it possible to propose economical recipes that take ingredient costs into account.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The food waste reduction system can further include a storage method suggestion unit that suggests ways to store ingredients. In the storage method suggestion unit, for example, the generation AI analyzes the characteristics and expiration date of ingredients and suggests the optimal storage method. For example, it suggests methods such as refrigerated storage, frozen storage, and dry storage. The storage method suggestion unit also takes into account the user's storage environment and customizes the storage method. For example, it suggests storage methods based on the temperature and humidity of the refrigerator. The storage method suggestion unit also suggests storage methods that preserve the nutritional value of ingredients. For example, it suggests storage methods that prevent the loss of vitamins and minerals. This allows for the optimization of food storage methods and further reduces food waste.

[0044] The data input unit can also input ingredient information in cooperation with a smartwatch. For example, when a user scans the barcode of an ingredient using a smartwatch, the information is automatically entered into the system. The user can also input ingredient information using the voice recognition function of the smartwatch. For example, when the user says "add tomatoes," the information is entered into the system. The GPS function of the smartwatch can also be used to automatically input information about ingredients purchased by the user at the supermarket into the system. This allows ingredient information to be entered quickly and accurately using a smartwatch.

[0045] The data input unit can also automatically obtain the user's food purchase history. For example, it can automatically input information about food ingredients purchased by the user using a supermarket membership card into the system. It can also link the purchase history from an online shopping site and automatically input information about purchased ingredients into the system. Furthermore, the generation AI can manage food inventory and suggest ways to use ingredients based on information about food ingredients purchased by the user in the past. This allows for efficient management of food ingredient information by utilizing the user's food purchase history.

[0046] The usage suggestion unit can further suggest ways to use ingredients that take into account the user's health condition. For example, the generation AI analyzes the user's health data and suggests healthy ways to use ingredients based on that information. For example, it could suggest low-salt recipes to a user with high blood pressure. The usage suggestion unit also takes into account the user's health goals, and the generation AI customizes how to use ingredients. For example, it could suggest low-calorie recipes to a user on a diet. The usage suggestion unit also takes into account the user's allergy information, and the generation AI suggests safe ways to use ingredients. For example, it could suggest nut-free recipes to a user with a nut allergy. This makes it possible to suggest ways to use ingredients that take into account the user's health condition.

[0047] The usage suggestion unit can further suggest ways to use ingredients that suit the user's lifestyle. For example, the generation AI analyzes the user's lifestyle data and suggests ways to use ingredients based on that information. For example, it can suggest recipes that can be cooked in a short amount of time to a busy user. The usage suggestion unit also customizes ways to use ingredients based on the user's lifestyle data. For example, it can suggest recipes that are well-balanced in nutrition to a health-conscious user. The usage suggestion unit can also suggest ways to use ingredients that suit the user's lifestyle. For example, it can suggest recipes that can be cooked while camping to a user who loves the outdoors. In this way, it can suggest ways to use ingredients that suit the user's lifestyle.

[0048] The usage suggestion unit can further suggest personalized ways to use ingredients based on the user's ingredient preferences and past feedback. For example, the generation AI analyzes the user's past feedback and suggests personalized ways to use ingredients based on that information. For example, it suggests new recipes based on the user's favorite dishes. The usage suggestion unit also suggests personalized ways to use ingredients based on the user's ingredient preferences. For example, it provides recipes using the user's favorite ingredients. The usage suggestion unit also suggests personalized ways to use ingredients based on the user's past feedback. For example, it makes new suggestions based on recipes that the user has given a high rating. This makes it possible to suggest personalized ways to use ingredients based on the user's preferences and feedback.

[0049] The processing flow of the first embodiment will be briefly explained below.

[0050] Step 1: The food ingredient management unit manages information about food ingredients, such as the name, quantity, expiration date, and nutritional value of the ingredients, and periodically updates the status of the ingredients to keep the information up to date. Step 2: The data input unit accepts the ingredient information entered by the user. For example, the ingredient information can be entered manually by the user, or the ingredient information can be automatically entered using barcode scanning or voice recognition technology. Step 3: The usage suggestion unit suggests ways to use ingredients based on the information about the ingredients received by the data input unit. For example, the generation AI considers the characteristics and expiration date of the ingredients to suggest the optimal way to use them. It can also analyze the user's past cooking history and preferences to suggest personalized ways to use ingredients. Step 4: The recipe generation unit generates specific recipes based on the usage methods suggested by the usage suggestion unit. For example, the generation AI can customize recipes according to the user's preferences and the amounts of ingredients, and generate recipes that take into account the nutritional value and health benefits of the ingredients.

[0051] (Example 2) The food waste reduction system according to an embodiment of the present invention is a system in which a generation AI devise and create methods and recipes for using up opened ingredients in order to reduce food waste within the home. As a result, the food waste reduction system can effectively reduce food waste within the home and realize a sustainable diet.

[0052] The food waste reduction system according to the embodiment includes an ingredient management unit, a data input unit, a usage suggestion unit, and a recipe generation unit. The ingredient management unit manages ingredient information, such as the name, quantity, expiration date, and nutritional value of the ingredient. The ingredient management unit also periodically updates the status of the ingredients to maintain the latest information. The data input unit accepts ingredient information entered by a user. For example, a user can manually enter ingredient information. The data input unit can also automatically enter ingredient information using barcode scanning or voice recognition technology. The usage suggestion unit proposes ingredient usage methods based on the ingredient information accepted by the data input unit. For example, the generation AI considers the characteristics and expiration date of the ingredient to propose optimal usage methods. The usage suggestion unit can also analyze the user's past cooking history and preferences to propose personalized usage methods. The recipe generation unit generates specific recipes based on the usage methods proposed by the usage suggestion unit. For example, the generation AI customizes recipes based on the user's preferences and the amount of ingredients. The recipe generation unit can also generate recipes that consider the nutritional value and health benefits of the ingredients. As a result, the food loss reduction system according to the embodiment can effectively reduce food loss within the home. For example, by utilizing recipes and storage methods suggested by the generation AI, it is possible to improve the efficiency of food use and reduce food waste.

[0053] The data input unit can automatically input information about ingredients using barcode scanning. For example, when a user scans an ingredient's barcode using a smartphone camera, information such as the ingredient's type, quantity, and expiration date is automatically entered into the system. The generation AI uses this information to manage ingredients and suggest ways to use them. The data input unit also uses a barcode scanner built into the refrigerator to automatically input data each time ingredients are placed in the refrigerator. The generation AI updates this data in real time and manages the status of the ingredients. The data input unit also scans barcodes on supermarket receipts, which inputs information about purchased ingredients into the system all at once. The generation AI uses this information to manage the household's ingredient inventory and suggest ways to use them. This allows for quick and accurate input of ingredient information.

[0054] The data input unit can input information about ingredients using voice recognition technology. For example, when a user says to a smart speaker, "Add three tomatoes," the voice recognition technology analyzes the information and automatically inputs it into the system. The generation AI manages ingredients based on this information. Alternatively, when a user uses a smartphone's voice assistant to say, "Tell me what ingredients are in the refrigerator," the data input unit uses voice recognition technology to obtain the ingredient information and input it into the system. The generation AI uses this information to suggest ways to use the information. Alternatively, when a user uses a voice recognition device installed in the kitchen to say, "There's not much milk left," the voice recognition technology analyzes the information and inputs it into the system. The generation AI manages ingredients based on this information. This allows users to input ingredient information verbally.

[0055] The data input unit can automatically collect ingredient information in cooperation with other devices in the home. For example, the data input unit uses a smart refrigerator's built-in camera to take pictures of ingredients and automatically inputs the ingredient information into the system using image recognition technology. The generation AI manages ingredients based on this information. The data input unit also uses a smart speaker to analyze user conversations and automatically collect information about ingredients and input it into the system. The generation AI suggests usage methods based on this information. The data input unit also works with smart kitchen devices to automatically input information about ingredients used during cooking into the system. The generation AI manages ingredient inventory based on this information and suggests the next use of ingredients. This makes it possible to automatically collect ingredient information in cooperation with other devices in the home.

[0056] The usage suggestion unit uses image recognition technology to automatically evaluate the condition of ingredients and can suggest usage methods based on that information. For example, the usage suggestion unit uses a camera inside the refrigerator to periodically take images of ingredients and uses image recognition technology to evaluate the freshness of the ingredients and the number of days since opening. The generation AI uses this information to suggest how to use the ingredients. The usage suggestion unit also uses a smartphone camera to take images of ingredients and uses image recognition technology to analyze their condition. The generation AI uses this information to suggest the optimal way to use the ingredients. The usage suggestion unit also uses a camera installed in the kitchen to take images of ingredients and uses image recognition technology to evaluate the condition of the ingredients. The generation AI uses this information to suggest how to use the ingredients. In this way, the condition of ingredients can be automatically evaluated and the optimal way to use them can be suggested.

[0057] The data input unit can use the emotion estimation function to analyze the emotion a user feels when entering ingredients and provide an interface for eliciting positive emotions. For example, the data input unit can analyze the emotion using a camera or microphone when the user enters ingredients and provide an interface for eliciting positive emotions. For example, the data input unit can display encouraging messages or positive feedback. The data input unit can also use the emotion estimation function to analyze the emotion a user feels when entering ingredients in real time and provide animations or audio guidance to elicit positive emotions. The data input unit can also customize the interface based on the emotion estimation data when the user enters ingredients and provide colors and designs to elicit positive emotions. This makes it possible to provide an interface for analyzing the user's emotions and eliciting positive emotions.

[0058] The data input unit can use the emotion estimation function to monitor the user's emotions in real time when entering ingredients and make suggestions to improve the input process. For example, the data input unit can monitor the user's emotions in real time using a camera or microphone when entering ingredients and make suggestions to improve the input process. For example, it can display a guide to ensure smooth input. The data input unit can also use the emotion estimation function to analyze the user's emotions when entering ingredients and provide feedback to improve the input process. For example, it can display an encouraging message if the input is slow. The data input unit can also provide real-time feedback based on the emotion estimation data when the user enters ingredients and offer advice to improve the input process. For example, it can adjust the interface to ensure smooth input. This makes it possible to monitor the user's emotions in real time and make suggestions to improve the input process.

[0059] The usage suggestion unit can analyze the user's past cooking history and suggest ways to use ingredients that suit their preferences. For example, the generation AI in the usage suggestion unit analyzes the user's past cooking history and suggests ways to use ingredients that suit their preferences. For example, it suggests new recipes based on dishes that the user has liked to make in the past. The usage suggestion unit also suggests ways to use ingredients that suit their preferences based on the user's cooking history data. For example, it suggests recipes that prioritize ingredients that the user uses frequently. The usage suggestion unit also suggests ways to use ingredients that suit their preferences based on the user's past cooking history. For example, it suggests variations of dishes that the user has made in the past. This makes it possible to suggest ways to use ingredients that suit the user's preferences.

[0060] The usage suggestion unit can suggest usage methods that take into account the nutritional value and health benefits of ingredients. For example, the generation AI in the usage suggestion unit analyzes the nutritional value and health benefits of ingredients and suggests usage methods to support a healthy diet. For example, it suggests recipes that use ingredients rich in vitamins and minerals. Furthermore, the generation AI in the usage suggestion unit suggests usage methods that take into account health benefits based on the nutritional value data of ingredients. For example, it suggests recipes that are low in calories and have good nutritional balance. Furthermore, the generation AI in the usage suggestion unit analyzes the nutritional value and health benefits of ingredients and suggests usage methods to support a healthy diet. For example, it suggests recipes to supplement specific nutrients. This makes it possible to suggest usage methods to support a healthy diet.

[0061] The usage suggestion unit can suggest ways to use ingredients according to the season and weather. For example, the generation AI analyzes seasonal and weather data and suggests ways to use ingredients that incorporate a sense of the season based on that information. For example, it suggests cold dishes in summer and hot dishes in winter. The usage suggestion unit also takes into account the characteristics of ingredients in each season and suggests ways to use ingredients that incorporate a sense of the season. For example, it suggests recipes using fresh vegetables in spring. The usage suggestion unit also suggests ways to use ingredients that incorporate a sense of the season based on weather data. For example, it suggests recipes using hot soup on rainy days. In this way, it is possible to suggest ways to use ingredients that incorporate a sense of the season.

[0062] The usage suggestion unit can suggest ways to use ingredients that incorporate recipes from different cultures and regions. For example, the generation AI analyzes recipe data from different cultures and regions and suggests international cuisine variations based on that information. For example, it suggests recipes such as Italian, Mexican, and Asian. The usage suggestion unit also takes into account the characteristics of ingredients from different regions and suggests international cuisine variations. For example, it suggests recipes for Mediterranean cuisine and Chinese cuisine. The usage suggestion unit also analyzes recipe data from different cultures and regions and suggests international cuisine variations based on that information. For example, it suggests recipes for French cuisine and Indian cuisine. This makes it possible to provide international cuisine variations.

[0063] The usage suggestion unit can use the emotion estimation function to identify the usage of an ingredient that the user is most interested in and prioritize suggest that usage. For example, the usage suggestion unit can use the emotion estimation function to identify the usage of an ingredient that the user is most interested in and prioritize suggest that usage. For example, it can suggest recipes using ingredients that the user has shown interest in. The usage suggestion unit can also identify the usage of an ingredient that the generation AI is most interested in based on the user's emotion data and prioritize suggest that usage. For example, it can suggest variations of dishes that the user likes. The usage suggestion unit can also use the emotion estimation function to identify the usage of an ingredient that the user is most interested in and prioritize suggest that usage. For example, it can make new suggestions based on recipes that the user has previously rated highly. This allows the usage of an ingredient that the user is most interested in to be prioritized.

[0064] The recipe generation unit can provide customized recipes ranging from beginner to advanced recipes, taking into account the user's cooking skill level. For example, the generation AI analyzes the user's cooking skill level and provides customized recipes ranging from simple recipes for beginners to complex recipes for advanced cooks. For example, the generation AI provides a step-by-step guide for beginners and suggests creative arrangements for advanced cooks. The recipe generation unit also evaluates the cooking skill level of the user based on the user's past cooking history and feedback, and provides recipes accordingly. For example, the generation AI suggests recipes that teach basic cooking techniques to beginners. The recipe generation unit also adjusts the difficulty of the recipe, taking into account the user's cooking skill level. For example, the generation AI suggests challenging dishes for advanced cooks and provides easy, foolproof recipes for beginners. This allows the unit to provide recipes according to the user's cooking skill level.

[0065] The recipe generation unit can propose safe recipes by taking into account allergy information about ingredients. For example, the recipe generation unit uses a generation AI to analyze a user's allergy information and propose safe recipes based on that information. For example, it provides recipes that use ingredients that do not contain allergens. The recipe generation unit also uses a generation AI to generate recipes that avoid allergens based on allergy information entered by the user. For example, it proposes recipes that do not contain nuts to a user with a nut allergy. The recipe generation unit also uses a generation AI to propose safe recipes by taking into account allergy information about ingredients. For example, it provides recipes that do not contain dairy products to a user with a dairy allergy. This makes it possible to propose safe recipes that take allergy information into account.

[0066] The recipe generation unit uses the emotion estimation function to generate recipes that match the user's emotions, thereby bringing out the joy of cooking. For example, the recipe generation unit uses the emotion estimation function to analyze the user's current emotions and generate recipes that match those emotions. For example, when you want to relax, it will suggest an easy and fun recipe. Furthermore, the recipe generation unit uses the generation AI to suggest recipes that match the user's emotions based on the user's emotion data. For example, when you want to cheer up, it will provide a recipe that is suitable for replenishing energy. Furthermore, the recipe generation unit uses the emotion estimation function to generate recipes that match the user's emotions, thereby bringing out the joy of cooking. For example, it will suggest a recipe that has a relaxing effect to reduce stress. In this way, it is possible to generate recipes that match the user's emotions and bring out the joy of cooking.

[0067] The recipe generation unit can provide personalized recipes based on the user's ingredient preferences and past feedback. For example, the recipe generation unit uses a generation AI to analyze the user's past feedback and provide personalized recipes based on that information. For example, it may suggest new recipes based on the user's favorite dishes. The recipe generation unit also uses a generation AI to suggest personalized recipes based on the user's ingredient preferences. For example, it may provide recipes using the user's favorite ingredients. The recipe generation unit also uses a generation AI to analyze the user's past feedback and provide personalized recipes based on that information. For example, it may make new suggestions based on recipes that the user has given a high rating. This makes it possible to provide personalized recipes based on the user's preferences and feedback.

[0068] The recipe generation unit can propose economical recipes by taking into account the cost of ingredients. For example, the recipe generation unit uses a generation AI to analyze ingredient cost data and propose economical recipes. For example, it may provide recipes that use inexpensive ingredients. The recipe generation unit also uses a generation AI to propose recipes that take cost into account based on the user's budget. For example, it may provide recipes in which ingredients are selected to fit within the budget. The recipe generation unit also uses a generation AI to analyze ingredient cost data and propose economical recipes. For example, it may provide recipes that use special sale or seasonal ingredients. This makes it possible to propose economical recipes that take ingredient costs into account.

[0069] The recipe generation unit can use the emotion estimation function to identify the recipe that the user will enjoy most and provide that recipe preferentially. The recipe generation unit, for example, uses the emotion estimation function to identify the recipe that the user will enjoy most and provide that recipe preferentially. For example, it proposes a new recipe based on dishes that the user has enjoyed in the past. The recipe generation unit also identifies the recipe that the generation AI will enjoy most based on the user's emotion data and provides that recipe preferentially. For example, it makes new suggestions based on recipes that the user has given high ratings. The recipe generation unit also uses the emotion estimation function to identify the recipe that the user will enjoy most and provide that recipe preferentially. For example, it proposes recipes using ingredients that the user has shown interest in. This allows the recipe that the user will enjoy most to be provided preferentially.

[0070] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0071] The food waste reduction system can further include a storage method suggestion unit that suggests ways to store ingredients. In the storage method suggestion unit, for example, the generation AI analyzes the characteristics and expiration date of ingredients and suggests the optimal storage method. For example, it suggests methods such as refrigerated storage, frozen storage, and dry storage. The storage method suggestion unit also takes into account the user's storage environment and customizes the storage method. For example, it suggests storage methods based on the temperature and humidity of the refrigerator. The storage method suggestion unit also suggests storage methods that preserve the nutritional value of ingredients. For example, it suggests storage methods that prevent the loss of vitamins and minerals. This allows for the optimization of food storage methods and further reduces food waste.

[0072] The data input unit can also input ingredient information in cooperation with a smartwatch. For example, when a user scans the barcode of an ingredient using a smartwatch, the information is automatically entered into the system. The user can also input ingredient information using the voice recognition function of the smartwatch. For example, when the user says "add tomatoes," the information is entered into the system. The GPS function of the smartwatch can also be used to automatically input information about ingredients purchased by the user at the supermarket into the system. This allows ingredient information to be entered quickly and accurately using a smartwatch.

[0073] The data input unit can also automatically obtain the user's food purchase history. For example, it can automatically input information about food ingredients purchased by the user using a supermarket membership card into the system. It can also link the purchase history from an online shopping site and automatically input information about purchased ingredients into the system. Furthermore, the generation AI can manage food inventory and suggest ways to use ingredients based on information about food ingredients purchased by the user in the past. This allows for efficient management of food ingredient information by utilizing the user's food purchase history.

[0074] The data input unit can also input ingredient information in cooperation with the smart mirror. For example, if a user speaks to the smart mirror and says, "Tell me what ingredients are in the refrigerator," the smart mirror will use voice recognition technology to input that information into the system. The smart mirror's camera can also be used to take pictures of ingredients, and ingredient information can be automatically input into the system using image recognition technology. Furthermore, the smart mirror can analyze the user's facial expressions and suggest ways to use ingredients based on their emotions. This allows ingredient information to be input efficiently using the smart mirror.

[0075] The usage suggestion unit can further suggest ways to use ingredients that take into account the user's health condition. For example, the generation AI analyzes the user's health data and suggests healthy ways to use ingredients based on that information. For example, it could suggest low-salt recipes to a user with high blood pressure. The usage suggestion unit also takes into account the user's health goals, and the generation AI customizes how to use ingredients. For example, it could suggest low-calorie recipes to a user on a diet. The usage suggestion unit also takes into account the user's allergy information, and the generation AI suggests safe ways to use ingredients. For example, it could suggest nut-free recipes to a user with a nut allergy. This makes it possible to suggest ways to use ingredients that take into account the user's health condition.

[0076] The data input unit can further analyze the user's emotions and provide an interface for reducing stress. For example, when the user inputs ingredients, the camera and microphone can be used to analyze the user's emotions and provide relaxing music and videos to reduce stress. The data input unit can also use an emotion estimation function to analyze the user's emotions in real time when inputting ingredients and provide advice for reducing stress. For example, a message encouraging deep breathing can be displayed. The data input unit can also customize the interface based on the emotion estimation data when the user inputs ingredients and provide colors and designs to reduce stress. This makes it possible to analyze the user's emotions and provide an interface for reducing stress.

[0077] The data input unit can further use an emotion estimation function to provide an interface that increases the user's motivation. For example, when the user inputs ingredients, the camera and microphone can be used to analyze the user's emotions and display encouraging messages or positive feedback to increase motivation. The data input unit can also use the emotion estimation function to analyze the user's emotions in real time when inputting ingredients and provide animations and audio guidance to increase motivation. The data input unit can also customize the interface based on the emotion estimation data when the user inputs ingredients and provide colors and designs to increase motivation. This makes it possible to provide an interface that analyzes the user's emotions and increases motivation.

[0078] The usage suggestion unit can further analyze the user's emotions and suggest ways to use ingredients that match their mood. For example, the generation AI analyzes the user's emotional data and, based on that information, suggests ways to use ingredients that match their mood. For example, when they want to relax, it suggests easy and fun recipes. The usage suggestion unit also suggests ways to use ingredients that match their mood based on the user's emotional data. For example, when they want to feel energized, it provides recipes that are suitable for replenishing energy. The usage suggestion unit also uses the emotion estimation function to suggest ways to use ingredients that match the user's emotions. For example, it suggests recipes that have a relaxing effect to reduce stress. This makes it possible to suggest ways to use ingredients that match the user's emotions.

[0079] The usage suggestion unit can further suggest ways to use ingredients that suit the user's lifestyle. For example, the generation AI analyzes the user's lifestyle data and suggests ways to use ingredients based on that information. For example, it can suggest recipes that can be cooked in a short amount of time to a busy user. The usage suggestion unit also customizes ways to use ingredients based on the user's lifestyle data. For example, it can suggest recipes that are well-balanced in nutrition to a health-conscious user. The usage suggestion unit can also suggest ways to use ingredients that suit the user's lifestyle. For example, it can suggest recipes that can be cooked while camping to a user who loves the outdoors. In this way, it can suggest ways to use ingredients that suit the user's lifestyle.

[0080] The usage suggestion unit can further suggest personalized ways to use ingredients based on the user's ingredient preferences and past feedback. For example, the generation AI analyzes the user's past feedback and suggests personalized ways to use ingredients based on that information. For example, it suggests new recipes based on the user's favorite dishes. The usage suggestion unit also suggests personalized ways to use ingredients based on the user's ingredient preferences. For example, it provides recipes using the user's favorite ingredients. The usage suggestion unit also suggests personalized ways to use ingredients based on the user's past feedback. For example, it makes new suggestions based on recipes that the user has given a high rating. This makes it possible to suggest personalized ways to use ingredients based on the user's preferences and feedback.

[0081] The processing flow of the second embodiment will be briefly explained below.

[0082] Step 1: The food ingredient management unit manages information about food ingredients, such as the name, quantity, expiration date, and nutritional value of the ingredients, and periodically updates the status of the ingredients to keep the information up to date. Step 2: The data input unit accepts the ingredient information entered by the user. For example, the ingredient information can be entered manually by the user, or the ingredient information can be automatically entered using barcode scanning or voice recognition technology. Step 3: The usage suggestion unit suggests ways to use ingredients based on the information about the ingredients received by the data input unit. For example, the generation AI considers the characteristics and expiration date of the ingredients to suggest the optimal way to use them. It can also analyze the user's past cooking history and preferences to suggest personalized ways to use ingredients. Step 4: The recipe generation unit generates specific recipes based on the usage methods suggested by the usage suggestion unit. For example, the generation AI can customize recipes according to the user's preferences and the amounts of ingredients, and generate recipes that take into account the nutritional value and health benefits of the ingredients.

[0083] 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.

[0084] 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.

[0085] 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.

[0086] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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).

[0092] 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.

[0093] 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.

[0094] 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.

[0095] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0096] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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).

[0107] 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.

[0108] 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.

[0109] 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.

[0110] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0111] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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).

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0127] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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).

[0136] 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.

[0137] 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."

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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]

[0150] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A food ingredient management department that manages information on ingredients; a data input unit that accepts information about ingredients input by a user; a usage suggestion unit that suggests ways to use ingredients based on the ingredient information received by the data input unit; a recipe generation unit that generates a specific recipe based on the usage method proposed by the usage method proposal unit. A system characterized by:

2. The data input unit Automatically inputting the ingredient information using barcode scanning 2. The system of claim 1.

3. The data input unit Input the information about the ingredients using voice recognition technology 2. The system of claim 1.

4. The data input unit Linking with other devices in the home to automatically collect food information 2. The system of claim 1.

5. The usage method suggestion unit Using image recognition technology, the condition of the ingredients is automatically evaluated and usage methods are suggested based on that information.

2. The system of claim 1.

Images