System

Abstract

An object of a system according to an embodiment is to reduce food loss at home and automatically propose a daily menu.SOLUTION: A system according to an embodiment includes a camera, an image recognizing part, and a generation AI part. The camera is installed in a refrigerator or a pantry. The image recognition unit recognizes a type, an amount, and a best-before date of an ingredient or a seasoning acquired by the camera. The generation AI unit proposes a menu on the basis of the information recognized by the image recognizing 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 leads to food waste at home and requires the effort of planning daily meals, so there is room for improvement.

[0005] The system according to the embodiment aims to reduce food waste in the home and automatically suggest daily menus. [Means for solving the problem]

[0006] The system according to the embodiment includes a camera, an image recognition unit, and a generation AI unit. The camera is installed in a refrigerator or pantry. The image recognition unit recognizes the types, quantities, and expiration dates of ingredients and seasonings captured by the camera. The generation AI unit suggests menu items based on the data recognized by the image recognition unit. [Effects of the Invention]

[0007] The system according to the embodiment can reduce food waste at home and automatically suggest daily menus. [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 and menu suggestion system according to an embodiment of the present invention uses a camera installed in a refrigerator or pantry to recognize the type, quantity, and expiration date of ingredients and seasonings, and the generation AI automatically suggests menus. As a result, the food waste reduction and menu suggestion system makes it possible to use ingredients efficiently and easily decide daily menus.

[0029] A food waste reduction and menu suggestion system according to an embodiment includes a camera, an image recognition unit, and a generation AI unit. The camera is installed in a refrigerator or pantry and acquires the types, quantities, and expiration dates of ingredients and seasonings. For example, the camera scans the labels of vegetables, meat, and seasonings in the refrigerator and records their respective expiration dates and quantities. The image recognition unit recognizes the types, quantities, and expiration dates of the ingredients and seasonings acquired by the camera. For example, the image recognition unit uses deep learning technology to identify ingredients and register them in a database. The image recognition unit can also identify the type of ingredient using an object detection algorithm. The generation AI unit proposes menus based on the data recognized by the image recognition unit. For example, the generation AI unit uses text generation AI (e.g., LLM) to automatically suggest menus that can be made with ingredients available at home. The generation AI unit can also use multimodal generation AI to suggest menus that take into account the types, quantities, and expiration dates of ingredients. This allows the food waste reduction and menu suggestion system according to an embodiment to use ingredients efficiently and easily determine daily menus. For example, the generation AI unit will suggest menus that prioritize the use of ingredients with a short expiration date. This will reduce food waste and prevent food loss. The generation AI unit also considers past history, the season, and the user's preferences when suggesting menus. For example, if a user has previously enjoyed cooking Japanese food, it will prioritize Japanese recipes. The generation AI unit will also suggest appropriate portions of food based on the number of people specified by the user. For example, if making dinner for four people, it will suggest ingredients and recipes for four people. This makes it possible to provide appropriate amounts of food without making excessive amounts of food.

[0030] The image recognition unit can evaluate the freshness and quality of ingredients in real time and identify ingredients that are deteriorating. For example, the image recognition unit uses image recognition technology to analyze changes in the color and shape of the surface of ingredients to evaluate freshness and quality. For example, deterioration can be identified when the color of vegetables changes or when there are wrinkles on the surface of fruit. The image recognition unit also combines image recognition technology with a temperature sensor to evaluate the freshness of ingredients. For example, it can simultaneously monitor temperature changes inside a refrigerator and the appearance of ingredients to detect a decrease in freshness. The image recognition unit also combines image recognition technology with a chemical sensor to evaluate the quality of ingredients. For example, it can detect pesticide residues on the surface of fruit and evaluate its quality. This allows for the identification of ingredients that are deteriorating and reduces waste.

[0031] The image recognition unit simultaneously acquires information on the nutritional value and allergens of ingredients, which can be useful for health management. The image recognition unit, for example, uses image recognition technology to identify the type of ingredient and acquires the nutritional value and allergen information from a database. For example, it displays information on the vitamins and minerals contained in a specific vegetable. The image recognition unit also acquires allergen information at the same time as recognizing the ingredient and displays a warning to users with allergies. For example, if an ingredient containing nuts is recognized, an allergy warning is issued. The image recognition unit also links the image recognition technology with a nutrition database to evaluate the nutritional value of ingredients. For example, it displays information on the calories and protein contained in a specific ingredient. This allows the acquisition of nutritional value and allergen information to be useful for health management.

[0032] The image recognition unit can optimize the placement of ingredients in the refrigerator and improve ease of access. The image recognition unit, for example, uses image recognition technology to identify the locations of ingredients in the refrigerator and suggest optimal placement. For example, frequently used ingredients are placed in positions that make them easy to access. The image recognition unit also combines image recognition technology with the user's usage history to optimize the placement of ingredients. For example, the placement of ingredients is adjusted based on past frequency of use. The image recognition unit also uses image recognition technology to suggest a placement that efficiently utilizes the space in the refrigerator. For example, the image recognition unit determines the optimal placement based on the size and shape of the ingredients. This optimizes the placement of ingredients in the refrigerator and improves ease of access.

[0033] The image recognition unit can store the recognized data of ingredients in the cloud, making it accessible from multiple devices. For example, the image recognition unit stores ingredient data acquired using image recognition technology in the cloud and makes it accessible from smartphones and tablets. For example, this allows users to check refrigerator inventory while on the go. The image recognition unit also synchronizes the ingredient data stored in the cloud across multiple devices and updates it in real time. For example, this allows all family members to share the same inventory information. The image recognition unit also stores the data acquired using image recognition technology in the cloud and uses it to create shopping lists while on the go. For example, ingredients that are low in stock can be automatically added to the list. This makes it possible to check inventory while on the go using the data stored in the cloud.

[0034] The generation AI unit can include cooking and storage methods for ingredients in the menus it suggests. For example, the generation AI unit will include detailed instructions on how to cook ingredients in the menus it suggests. For example, it will suggest how to cut vegetables and how to prepare meat. The generation AI unit will also include instructions on how to store ingredients in the menus it suggests. For example, it will suggest how to freeze and reuse leftover ingredients. The generation AI unit will also include cooking techniques to reduce food waste in the menus it suggests. For example, it will suggest recipes that use vegetable peels and stems. By including instructions on how to cook and store ingredients, waste can be further reduced.

[0035] The generation AI unit can incorporate an algorithm that considers the compatibility and flavor balance of ingredient combinations into the menus it proposes. For example, the generation AI unit may include recipes that consider the compatibility of ingredients in the menus it proposes. For example, it may propose a combination of tomatoes and basil. The generation AI unit may also include recipes that consider the balance of flavors in the menus it proposes. For example, it may suggest seasonings that balance sweetness and sourness. The generation AI unit may also incorporate an algorithm that evaluates the compatibility of ingredient combinations, allowing the generation AI to propose the optimal menu. For example, it may propose a recipe that is considered to be delicious with a specific combination of ingredients. This makes it possible to propose menus that consider the compatibility and flavor balance of ingredient combinations.

[0036] The generation AI unit can add a customization function that takes into account the user's health condition and diet goals. For example, the generation AI unit includes recipes that take into account the user's health condition in the menus it suggests. For example, it may suggest low-calorie or low-salt menus. The generation AI unit also includes recipes that take into account the user's diet goals in the menus it suggests. For example, it may suggest protein-rich menus or low-carb menus. The generation AI unit also suggests menus customized by the generation AI based on the user's health data. For example, it may suggest recipes that take into account blood pressure and blood sugar levels. This makes it possible to suggest menus that suit the user's health condition and diet goals.

[0037] The generation AI unit can optimize menus by taking into account the preferences and allergy information of all family members. For example, the generation AI unit includes recipes that take into account the preferences of all family members in the menus it suggests. For example, it suggests menus that children like and menus that adults like. The generation AI unit also includes recipes that take into account the allergy information of all family members in the menus it suggests. For example, it suggests menus that do not contain specific allergens. The generation AI unit also suggests the optimal menu based on the eating history of all family members. For example, it re-suggests menus that were popular in the past. This makes it possible to optimize menus based on the preferences and allergy information of all family members.

[0038] When suggesting a menu that uses ingredients with a short expiration date, the generation AI unit can suggest ways to store and reuse the ingredients. For example, the generation AI unit includes ingredient storage methods in a menu that uses ingredients with a short expiration date. For example, it suggests ways to freeze and reuse leftover ingredients. The generation AI unit also includes reuse methods in a menu that uses ingredients with a short expiration date. For example, it suggests another recipe using leftover ingredients. The generation AI unit also reduces food waste by including storage and reuse methods in menus that use ingredients with a short expiration date. For example, it suggests recipes that use vegetable peels and stems. This makes it possible to suggest storage and reuse methods to use ingredients with a short expiration date without waste.

[0039] The generation AI unit can implement an algorithm that takes into account the nutritional value and health benefits of ingredients when proposing menus that use ingredients with a short expiration date. For example, the generation AI unit will include recipes that take into account the nutritional value of ingredients in menus that use ingredients with a short expiration date. For example, it will propose menus that are rich in vitamins and minerals. The generation AI unit can also include recipes that take into account health benefits in menus that use ingredients with a short expiration date. For example, it will propose menus that use ingredients with antioxidant properties. The generation AI unit can also implement an algorithm that evaluates the nutritional value and health benefits of ingredients, allowing the generation AI to propose optimal menus. For example, it will propose recipes that use ingredients that are rich in specific nutrients. This makes it possible to propose menus that take into account nutritional value and health benefits.

[0040] The generation AI unit can take into account the storage conditions and deterioration of ingredients when suggesting menus that use ingredients with a short expiration date. For example, the generation AI unit will include recipes that take into account the storage conditions of ingredients in menus that use ingredients with a short expiration date. For example, it will suggest menus that use ingredients that need to be refrigerated. The generation AI unit will also include recipes that take into account the deterioration conditions in menus that use ingredients with a short expiration date. For example, it will suggest recipes for soups and stews that use slightly damaged vegetables. The generation AI unit will also introduce an algorithm that evaluates the storage conditions and deterioration conditions of ingredients, allowing the generation AI to suggest optimal menus. For example, it will suggest recipes that prioritize the use of ingredients that have been well-preserved. This makes it possible to suggest menus that take into account the storage conditions and deterioration conditions of ingredients.

[0041] The generation AI unit can refer to the inventory status and menu suggestions of other households when proposing menus that use ingredients with a short expiration date. For example, the generation AI unit refers to the inventory status of other households and proposes menus that use ingredients with a short expiration date. For example, it shares menus from households that have the same ingredients. The generation AI unit also refers to the menu suggestions of other households and proposes menus that use ingredients with a short expiration date. For example, it proposes popular recipes. The generation AI unit also stores the inventory status and menu suggestions of other households in a database, and the generation AI proposes the optimal menu. For example, it refers to the menus of households in the same area. This makes it possible to propose a wider variety of menus by referring to the inventory status and menu suggestions of other households.

[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 and menu suggestion system can further include a voice recognition unit. The voice recognition unit can recognize the user's voice commands and add or delete ingredients or suggest menus. For example, if the user says, "Add tomatoes," the voice recognition unit will add tomatoes to the database. Also, when the user asks, "What would you like for dinner tonight?" the voice recognition unit can respond by voice with a menu suggested by the generation AI unit. Furthermore, when the user checks the ingredients in the refrigerator, the voice recognition unit can also inform the user of the type of ingredients and their expiration date by voice. This allows the user to operate the system without using their hands, improving convenience.

[0044] The food waste reduction and menu suggestion system can further include a health management unit that acquires the user's health data. The health management unit can collect the user's health condition and allergy information and reflect this in the menus suggested by the generation AI unit. For example, if the user has diabetes, the health management unit will prioritize suggesting low-carb menus. The health management unit can also collect data such as the user's weight, blood pressure, and blood sugar level to suggest menus according to their health condition. Furthermore, the health management unit can suggest balanced meals taking into account the user's amount of exercise and calorie consumption. This makes it possible to provide menus that are useful for managing the user's health.

[0045] The food waste reduction and menu suggestion system can further include a purchase history management unit that manages the purchase history of ingredients. The purchase history management unit records data on ingredients purchased in the past by the user and can reflect this in the menus suggested by the generation AI unit. For example, it can suggest new recipes using the same ingredients based on ingredients purchased in the past by the user. The purchase history management unit can also identify ingredients that the user frequently purchases and prioritize suggesting menus using those ingredients. Furthermore, the purchase history management unit can manage the expiration dates of ingredients purchased by the user and suggest menus that use ingredients with an approaching expiration date. This makes it possible to utilize the user's purchase history to provide more personalized menus.

[0046] The food waste reduction and menu suggestion system can further include a storage condition monitoring unit that monitors the storage conditions of ingredients. The storage condition monitoring unit can monitor the temperature and humidity inside the refrigerator and evaluate the storage conditions of ingredients. For example, if the temperature inside the refrigerator is not appropriate, the storage condition monitoring unit can issue an alert and notify the user. The storage condition monitoring unit can also suggest the optimal storage method depending on the storage conditions of ingredients. For example, it can suggest a method for storing vegetables or a method for freezing meat. Furthermore, the storage condition monitoring unit can monitor the storage conditions of ingredients in real time and identify ingredients that are deteriorating. This makes it possible to optimize the storage conditions of ingredients and reduce waste.

[0047] The food waste reduction and menu suggestion system can further include a meal history management unit that manages the user's meal history. The meal history management unit records data on menus the user has eaten in the past and can reflect this in the menus suggested by the generation AI unit. For example, it can re-suggest menus that the user has previously enjoyed. The meal history management unit can also analyze the user's meal history and suggest nutritionally balanced menus. Furthermore, the meal history management unit can understand food consumption trends based on the user's meal history and suggest menus that reduce waste. This makes it possible to utilize the user's meal history to provide more personalized menus.

[0048] The processing flow of the first embodiment will be briefly explained below.

[0049] Step 1: A camera is installed in the refrigerator or pantry to obtain the type, quantity, and expiration date of ingredients and seasonings. For example, the camera reads the labels of vegetables, meat, and seasonings in the refrigerator and records the expiration date and quantity of each. Step 2: The image recognition unit recognizes the type, quantity, and expiration date of the ingredients and seasonings captured by the camera. For example, the image recognition unit can use deep learning technology to identify the ingredients and register them in a database. The image recognition unit can also use object detection algorithms to identify the type of ingredients. Step 3: The generation AI unit proposes a menu based on the data recognized by the image recognition unit. For example, the generation AI unit uses a text generation AI (e.g., LLM) to automatically propose a menu that can be made with ingredients available at home. The generation AI unit can also use a multimodal generation AI to propose a menu that takes into account the type, quantity, and expiration date of ingredients. This allows the food waste reduction and menu proposal system according to the embodiment to use ingredients efficiently and easily determine daily menus. For example, the generation AI unit proposes a menu that prioritizes ingredients with a short expiration date. This reduces ingredient waste and prevents food waste. The generation AI unit also proposes menus taking into account past history, the season, and the user's preferences. For example, if a user has previously enjoyed cooking Japanese food, it will prioritize Japanese recipes. The generation AI unit also proposes appropriate portions of food based on the number of people specified by the user. For example, if making dinner for four people, it will propose ingredients and recipes for four people. This allows the user to provide appropriate amounts of food without cooking excessive amounts of food.

[0050] (Example 2) The food waste reduction and menu suggestion system according to an embodiment of the present invention uses a camera installed in a refrigerator or pantry to recognize the type, quantity, and expiration date of ingredients and seasonings, and the generation AI automatically suggests menus. As a result, the food waste reduction and menu suggestion system makes it possible to use ingredients efficiently and easily decide daily menus.

[0051] A food waste reduction and menu suggestion system according to an embodiment includes a camera, an image recognition unit, and a generation AI unit. The camera is installed in a refrigerator or pantry and acquires the types, quantities, and expiration dates of ingredients and seasonings. For example, the camera scans the labels of vegetables, meat, and seasonings in the refrigerator and records their respective expiration dates and quantities. The image recognition unit recognizes the types, quantities, and expiration dates of the ingredients and seasonings acquired by the camera. For example, the image recognition unit uses deep learning technology to identify ingredients and register them in a database. The image recognition unit can also identify the type of ingredient using an object detection algorithm. The generation AI unit proposes menus based on the data recognized by the image recognition unit. For example, the generation AI unit uses text generation AI (e.g., LLM) to automatically suggest menus that can be made with ingredients available at home. The generation AI unit can also use multimodal generation AI to suggest menus that take into account the types, quantities, and expiration dates of ingredients. This allows the food waste reduction and menu suggestion system according to an embodiment to use ingredients efficiently and easily determine daily menus. For example, the generation AI unit will suggest menus that prioritize the use of ingredients with a short expiration date. This will reduce food waste and prevent food loss. The generation AI unit also considers past history, the season, and the user's preferences when suggesting menus. For example, if a user has previously enjoyed cooking Japanese food, it will prioritize Japanese recipes. The generation AI unit will also suggest appropriate portions of food based on the number of people specified by the user. For example, if making dinner for four people, it will suggest ingredients and recipes for four people. This makes it possible to provide appropriate amounts of food without making excessive amounts of food.

[0052] The image recognition unit can evaluate the freshness and quality of ingredients in real time and identify ingredients that are deteriorating. For example, the image recognition unit uses image recognition technology to analyze changes in the color and shape of the surface of ingredients to evaluate freshness and quality. For example, deterioration can be identified when the color of vegetables changes or when there are wrinkles on the surface of fruit. The image recognition unit also combines image recognition technology with a temperature sensor to evaluate the freshness of ingredients. For example, it can simultaneously monitor temperature changes inside a refrigerator and the appearance of ingredients to detect a decrease in freshness. The image recognition unit also combines image recognition technology with a chemical sensor to evaluate the quality of ingredients. For example, it can detect pesticide residues on the surface of fruit and evaluate its quality. This allows for the identification of ingredients that are deteriorating and reduces waste.

[0053] The image recognition unit simultaneously acquires information on the nutritional value and allergens of ingredients, which can be useful for health management. The image recognition unit, for example, uses image recognition technology to identify the type of ingredient and acquires the nutritional value and allergen information from a database. For example, it displays information on the vitamins and minerals contained in a specific vegetable. The image recognition unit also acquires allergen information at the same time as recognizing the ingredient and displays a warning to users with allergies. For example, if an ingredient containing nuts is recognized, an allergy warning is issued. The image recognition unit also links the image recognition technology with a nutrition database to evaluate the nutritional value of ingredients. For example, it displays information on the calories and protein contained in a specific ingredient. This allows the acquisition of nutritional value and allergen information to be useful for health management.

[0054] The image recognition unit analyzes the emotions of users when they take out ingredients and can grasp trends in preferred ingredients. For example, the image recognition unit combines image recognition technology with an emotion estimation function to analyze the facial expressions of users when they take out ingredients and estimate their emotions. For example, it detects smiling or surprised expressions. The image recognition unit also collects emotional data when users take out ingredients and analyzes trends in preferred ingredients. For example, it identifies ingredients that are associated with a high percentage of positive emotions. The image recognition unit also uses the emotion estimation function to analyze the emotions of users when they take out ingredients in real time and create a list of preferred ingredients. For example, it prioritizes displaying ingredients that are frequently taken out. This makes it possible to understand the user's preferences and suggest preferred ingredients.

[0055] The image recognition unit can optimize the placement of ingredients in the refrigerator and improve ease of access. The image recognition unit, for example, uses image recognition technology to identify the locations of ingredients in the refrigerator and suggest optimal placement. For example, frequently used ingredients are placed in positions that make them easy to access. The image recognition unit also combines image recognition technology with the user's usage history to optimize the placement of ingredients. For example, the placement of ingredients is adjusted based on past frequency of use. The image recognition unit also uses image recognition technology to suggest a placement that efficiently utilizes the space in the refrigerator. For example, the image recognition unit determines the optimal placement based on the size and shape of the ingredients. This optimizes the placement of ingredients in the refrigerator and improves ease of access.

[0056] The image recognition unit can store the recognized data of ingredients in the cloud, making it accessible from multiple devices. For example, the image recognition unit stores ingredient data acquired using image recognition technology in the cloud and makes it accessible from smartphones and tablets. For example, this allows users to check refrigerator inventory while on the go. The image recognition unit also synchronizes the ingredient data stored in the cloud across multiple devices and updates it in real time. For example, this allows all family members to share the same inventory information. The image recognition unit also stores the data acquired using image recognition technology in the cloud and uses it to create shopping lists while on the go. For example, ingredients that are low in stock can be automatically added to the list. This makes it possible to check inventory while on the go using the data stored in the cloud.

[0057] The image recognition unit can analyze the emotions expressed by the user when they open the refrigerator and improve the arrangement of ingredients and the suggestions they make. For example, the image recognition unit combines image recognition technology with an emotion estimation function to analyze the facial expression of the user when they open the refrigerator and estimate the emotion. For example, it detects expressions of surprise or dissatisfaction. The image recognition unit also improves the arrangement of ingredients in the refrigerator based on the user's emotion data. For example, it prioritizes the adoption of an arrangement that evokes a lot of positive emotions. The image recognition unit also uses the emotion estimation function to analyze the emotions expressed by the user when they open the refrigerator in real time and improve the suggestions they make. For example, it changes the suggestions they make if a lot of negative emotions are expressed. This makes it possible to improve the arrangement of ingredients and the suggestions they make based on the user's emotions.

[0058] The generation AI unit can include cooking and storage methods for ingredients in the menus it suggests. For example, the generation AI unit will include detailed instructions on how to cook ingredients in the menus it suggests. For example, it will suggest how to cut vegetables and how to prepare meat. The generation AI unit will also include instructions on how to store ingredients in the menus it suggests. For example, it will suggest how to freeze and reuse leftover ingredients. The generation AI unit will also include cooking techniques to reduce food waste in the menus it suggests. For example, it will suggest recipes that use vegetable peels and stems. By including instructions on how to cook and store ingredients, waste can be further reduced.

[0059] The generation AI unit can incorporate an algorithm that considers the compatibility and flavor balance of ingredient combinations into the menus it proposes. For example, the generation AI unit may include recipes that consider the compatibility of ingredients in the menus it proposes. For example, it may propose a combination of tomatoes and basil. The generation AI unit may also include recipes that consider the balance of flavors in the menus it proposes. For example, it may suggest seasonings that balance sweetness and sourness. The generation AI unit may also incorporate an algorithm that evaluates the compatibility of ingredient combinations, allowing the generation AI to propose the optimal menu. For example, it may propose a recipe that is considered to be delicious with a specific combination of ingredients. This makes it possible to propose menus that consider the compatibility and flavor balance of ingredient combinations.

[0060] The generation AI unit can analyze the user's emotional response to the proposed menu and prioritize suggesting the preferred menu. For example, the generation AI unit can analyze the user's emotional response to the menu proposed by the generation AI in real time and prioritize suggesting menus that receive a lot of positive responses. For example, it can detect smiling and happy facial expressions. The generation AI unit also adjusts the menus proposed by the generation AI based on the user's emotional data. For example, it can exclude menus that receive a lot of negative responses. The generation AI unit also uses an emotion estimation function to analyze the user's emotions toward the proposed menu and list the preferred menus. For example, it can select menus based on past emotional data. This makes it possible to suggest preferred menus based on the user's emotional responses.

[0061] The generation AI unit can add a customization function that takes into account the user's health condition and diet goals. For example, the generation AI unit includes recipes that take into account the user's health condition in the menus it suggests. For example, it may suggest low-calorie or low-salt menus. The generation AI unit also includes recipes that take into account the user's diet goals in the menus it suggests. For example, it may suggest protein-rich menus or low-carb menus. The generation AI unit also suggests menus customized by the generation AI based on the user's health data. For example, it may suggest recipes that take into account blood pressure and blood sugar levels. This makes it possible to suggest menus that suit the user's health condition and diet goals.

[0062] The generation AI unit can optimize menus by taking into account the preferences and allergy information of all family members. For example, the generation AI unit includes recipes that take into account the preferences of all family members in the menus it suggests. For example, it suggests menus that children like and menus that adults like. The generation AI unit also includes recipes that take into account the allergy information of all family members in the menus it suggests. For example, it suggests menus that do not contain specific allergens. The generation AI unit also suggests the optimal menu based on the eating history of all family members. For example, it re-suggests menus that were popular in the past. This makes it possible to optimize menus based on the preferences and allergy information of all family members.

[0063] The generation AI unit can analyze the emotions of users when selecting a menu in real time and suggest menus that elicit positive emotions. For example, the generation AI unit can analyze the user's emotional reactions to the menus proposed by the generation AI in real time and prioritize suggesting menus that elicit positive emotions. For example, it can detect smiling and happy facial expressions. The generation AI unit also adjusts the menus proposed by the generation AI based on the user's emotional data. For example, it can exclude menus that elicit a lot of negative reactions. The generation AI unit also uses an emotion estimation function to analyze the emotions of users when selecting a menu in real time and list menus that elicit positive emotions. For example, it can select menus based on past emotional data. This makes it possible to suggest menus that elicit positive emotions based on the user's emotions.

[0064] When suggesting a menu that uses ingredients with a short expiration date, the generation AI unit can suggest ways to store and reuse the ingredients. For example, the generation AI unit includes ingredient storage methods in a menu that uses ingredients with a short expiration date. For example, it suggests ways to freeze and reuse leftover ingredients. The generation AI unit also includes reuse methods in a menu that uses ingredients with a short expiration date. For example, it suggests another recipe using leftover ingredients. The generation AI unit also reduces food waste by including storage and reuse methods in menus that use ingredients with a short expiration date. For example, it suggests recipes that use vegetable peels and stems. This makes it possible to suggest storage and reuse methods to use ingredients with a short expiration date without waste.

[0065] The generation AI unit can implement an algorithm that takes into account the nutritional value and health benefits of ingredients when proposing menus that use ingredients with a short expiration date. For example, the generation AI unit will include recipes that take into account the nutritional value of ingredients in menus that use ingredients with a short expiration date. For example, it will propose menus that are rich in vitamins and minerals. The generation AI unit can also include recipes that take into account health benefits in menus that use ingredients with a short expiration date. For example, it will propose menus that use ingredients with antioxidant properties. The generation AI unit can also implement an algorithm that evaluates the nutritional value and health benefits of ingredients, allowing the generation AI to propose optimal menus. For example, it will propose recipes that use ingredients that are rich in specific nutrients. This makes it possible to propose menus that take into account nutritional value and health benefits.

[0066] The generation AI unit analyzes the user's emotional response to menus that use ingredients with a short shelf life, and can prioritize suggesting preferred menus. For example, the generation AI unit analyzes the user's emotional response in real time to menus that use ingredients with a short shelf life, and prioritizes suggesting menus that receive a lot of positive responses. For example, it detects smiling and happy facial expressions. The generation AI unit also adjusts menus that use ingredients with a short shelf life based on the user's emotional data. For example, it excludes menus that receive a lot of negative responses. The generation AI unit also uses an emotion estimation function to analyze the user's emotions toward menus that use ingredients with a short shelf life, and lists preferred menus. For example, it selects menus based on past emotional data. This makes it possible to suggest preferred menus based on the user's emotional responses.

[0067] The generation AI unit can take into account the storage conditions and deterioration of ingredients when suggesting menus that use ingredients with a short expiration date. For example, the generation AI unit will include recipes that take into account the storage conditions of ingredients in menus that use ingredients with a short expiration date. For example, it will suggest menus that use ingredients that need to be refrigerated. The generation AI unit will also include recipes that take into account the deterioration conditions in menus that use ingredients with a short expiration date. For example, it will suggest recipes for soups and stews that use slightly damaged vegetables. The generation AI unit will also introduce an algorithm that evaluates the storage conditions and deterioration conditions of ingredients, allowing the generation AI to suggest optimal menus. For example, it will suggest recipes that prioritize the use of ingredients that have been well-preserved. This makes it possible to suggest menus that take into account the storage conditions and deterioration conditions of ingredients.

[0068] The generation AI unit can refer to the inventory status and menu suggestions of other households when proposing menus that use ingredients with a short expiration date. For example, the generation AI unit refers to the inventory status of other households and proposes menus that use ingredients with a short expiration date. For example, it shares menus from households that have the same ingredients. The generation AI unit also refers to the menu suggestions of other households and proposes menus that use ingredients with a short expiration date. For example, it proposes popular recipes. The generation AI unit also stores the inventory status and menu suggestions of other households in a database, and the generation AI proposes the optimal menu. For example, it refers to the menus of households in the same area. This makes it possible to propose a wider variety of menus by referring to the inventory status and menu suggestions of other households.

[0069] The generation AI unit can analyze the user's emotions in real time when selecting a menu item that uses ingredients with a short shelf life, and suggest menu items that elicit positive emotions. For example, the generation AI unit can analyze the user's emotional reactions in real time to menu items that use ingredients with a short shelf life, and prioritize suggesting menu items that elicit positive emotions. For example, it can detect smiling and happy facial expressions. The generation AI unit can also adjust menu items that use ingredients with a short shelf life based on the user's emotional data. For example, it can exclude menu items that elicit a lot of negative reactions. The generation AI unit can also use its emotion estimation function to analyze the user's emotions in real time when selecting a menu item that uses ingredients with a short shelf life, and list menu items that elicit positive emotions. For example, it can select menu items based on past emotional data. This makes it possible to suggest menu items that elicit positive emotions based on the user's emotions.

[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 and menu suggestion system can further include a voice recognition unit. The voice recognition unit can recognize the user's voice commands and add or delete ingredients or suggest menus. For example, if the user says, "Add tomatoes," the voice recognition unit will add tomatoes to the database. Also, when the user asks, "What would you like for dinner tonight?" the voice recognition unit can respond by voice with a menu suggested by the generation AI unit. Furthermore, when the user checks the ingredients in the refrigerator, the voice recognition unit can also inform the user of the type of ingredients and their expiration date by voice. This allows the user to operate the system without using their hands, improving convenience.

[0072] The food waste reduction and menu suggestion system can further include a health management unit that acquires the user's health data. The health management unit can collect the user's health condition and allergy information and reflect this in the menus suggested by the generation AI unit. For example, if the user has diabetes, the health management unit will prioritize suggesting low-carb menus. The health management unit can also collect data such as the user's weight, blood pressure, and blood sugar level to suggest menus according to their health condition. Furthermore, the health management unit can suggest balanced meals taking into account the user's amount of exercise and calorie consumption. This makes it possible to provide menus that are useful for managing the user's health.

[0073] The food waste reduction and menu suggestion system can further include a purchase history management unit that manages the purchase history of ingredients. The purchase history management unit records data on ingredients purchased in the past by the user and can reflect this in the menus suggested by the generation AI unit. For example, it can suggest new recipes using the same ingredients based on ingredients purchased in the past by the user. The purchase history management unit can also identify ingredients that the user frequently purchases and prioritize suggesting menus using those ingredients. Furthermore, the purchase history management unit can manage the expiration dates of ingredients purchased by the user and suggest menus that use ingredients with an approaching expiration date. This makes it possible to utilize the user's purchase history to provide more personalized menus.

[0074] The food waste reduction and menu suggestion system can further include a storage condition monitoring unit that monitors the storage conditions of ingredients. The storage condition monitoring unit can monitor the temperature and humidity inside the refrigerator and evaluate the storage conditions of ingredients. For example, if the temperature inside the refrigerator is not appropriate, the storage condition monitoring unit can issue an alert and notify the user. The storage condition monitoring unit can also suggest the optimal storage method depending on the storage conditions of ingredients. For example, it can suggest a method for storing vegetables or a method for freezing meat. Furthermore, the storage condition monitoring unit can monitor the storage conditions of ingredients in real time and identify ingredients that are deteriorating. This makes it possible to optimize the storage conditions of ingredients and reduce waste.

[0075] The food waste reduction and menu suggestion system can further include a meal history management unit that manages the user's meal history. The meal history management unit records data on menus the user has eaten in the past and can reflect this in the menus suggested by the generation AI unit. For example, it can re-suggest menus that the user has previously enjoyed. The meal history management unit can also analyze the user's meal history and suggest nutritionally balanced menus. Furthermore, the meal history management unit can understand food consumption trends based on the user's meal history and suggest menus that reduce waste. This makes it possible to utilize the user's meal history to provide more personalized menus.

[0076] The food waste reduction and menu suggestion system can further include an emotion estimation unit that estimates the user's emotions and suggests menus based on the estimated emotions. The emotion estimation unit can estimate the user's emotions from their facial expressions and voice and reflect this in the menus suggested by the generation AI unit. For example, if the user is tired, it can suggest an easy-to-make menu. The emotion estimation unit can also suggest a special menu if the user is happy. Furthermore, the emotion estimation unit can collect the user's emotion data and suggest menus based on past emotion data. This makes it possible to provide menus that match the user's emotions.

[0077] The food waste reduction and menu suggestion system can further include a trend analysis unit that analyzes the user's ingredient selection trends and prioritizes suggesting preferred ingredients. The trend analysis unit can collect data on ingredients selected by the user in the past and analyze the trends in preferred ingredients. For example, it can identify ingredients that the user frequently selects and prioritize suggesting menus using those ingredients. The trend analysis unit can also suggest new ingredients based on the user's ingredient selection trends. For example, it can suggest new ingredients that go well with ingredients that the user prefers. Furthermore, the trend analysis unit can analyze the user's ingredient selection trends in real time and suggest optimal menus. This makes it possible to provide menus that meet the user's preferences.

[0078] The food waste reduction and menu suggestion system can also analyze the user's emotions during meals in real time to improve meal satisfaction. For example, if the user shows a smile or happy expression while eating, the system can re-suggest that menu. Also, if the user shows a dissatisfied expression while eating, the system can adjust the next menu suggestion. Furthermore, it can collect data on the user's emotions while eating and optimize the menu based on past data. This can improve the user's dining experience.

[0079] The food waste reduction and menu suggestion system can also analyze the user's emotions when selecting ingredients in real time and suggest ingredients that elicit positive emotions. For example, if the user shows a smile or a happy expression when selecting ingredients, it can prioritize menu suggestions that use those ingredients. On the other hand, if the user shows a dissatisfied expression when selecting ingredients, it can suggest menu suggestions that avoid those ingredients. Furthermore, it can collect data on the user's emotions when selecting ingredients and optimize menus based on past data. This makes it possible to suggest ingredients that elicit positive emotions based on the user's emotions.

[0080] The food waste reduction and menu suggestion system can also analyze the user's post-meal emotions in real time and reflect them in the next menu suggestion. For example, if the user shows a satisfied expression after eating, the system can re-suggest that menu. Also, if the user shows a dissatisfied expression after eating, the system can adjust the next menu suggestion. Furthermore, it can collect data on the user's post-meal emotions and optimize the menu based on past data. This can improve the user's dining experience.

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

[0082] Step 1: A camera is installed in the refrigerator or pantry to obtain the type, quantity, and expiration date of ingredients and seasonings. For example, the camera reads the labels of vegetables, meat, and seasonings in the refrigerator and records the expiration date and quantity of each. Step 2: The image recognition unit recognizes the type, quantity, and expiration date of the ingredients and seasonings captured by the camera. For example, the image recognition unit can use deep learning technology to identify the ingredients and register them in a database. The image recognition unit can also use object detection algorithms to identify the type of ingredients. Step 3: The generation AI unit proposes a menu based on the data recognized by the image recognition unit. For example, the generation AI unit uses a text generation AI (e.g., LLM) to automatically propose a menu that can be made with ingredients available at home. The generation AI unit can also use a multimodal generation AI to propose a menu that takes into account the type, quantity, and expiration date of ingredients. This allows the food waste reduction and menu proposal system according to the embodiment to use ingredients efficiently and easily determine daily menus. For example, the generation AI unit proposes a menu that prioritizes ingredients with a short expiration date. This reduces ingredient waste and prevents food waste. The generation AI unit also proposes menus taking into account past history, the season, and the user's preferences. For example, if a user has previously enjoyed cooking Japanese food, it will prioritize Japanese recipes. The generation AI unit also proposes appropriate portions of food based on the number of people specified by the user. For example, if making dinner for four people, it will propose ingredients and recipes for four people. This allows the user to provide appropriate amounts of food without cooking excessive amounts of food.

[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 a 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 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[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. Cameras installed in refrigerators and pantries, an image recognition unit that recognizes the type, amount, and expiration date of the ingredients and seasonings acquired by the camera; a generation AI unit that proposes a menu based on the data recognized by the image recognition unit; A system characterized by:

2. The image recognition unit Evaluate the freshness and quality of the ingredients in real time and identify ingredients that are deteriorating 2. The system of claim 1.

3. The image recognition unit Simultaneously obtain information on the nutritional value and allergens of the ingredients, which will be useful for health management.

2. The system of claim 1.

4. The image recognition unit Analyzing the emotions of users when they take out the ingredients and understanding trends in preferred ingredients 2. The system of claim 1.

5. The image recognition unit Optimizing the arrangement of the ingredients in the refrigerator to improve ease of taking them out 2. The system of claim 1.

Images