system

Abstract

Provide a system. The method includes: collecting data using a camera and a sensor installed inside the refrigerator; means for transmitting the collected data to a server; a means for the server to analyze the collected data using image analysis technology and generate a list of food items in the refrigerator; a means for suggesting available recipes based on the generated food list; a means for identifying foods from the food list that are close to expiry or low in stock and generating management advice; a means of informing them of suggested recipes and management advice; A means for a user to check the status of the refrigerator while away from home; A system including:

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] Food waste is a major problem in modern households. Many households are unable to keep track of the food in their refrigerators, resulting in a lot of expired food or food that goes rotten without being consumed. People often struggle to decide what to cook for dinner, and when they go shopping, they often don't know what ingredients are in their refrigerators, which can lead to duplicate purchases. These problems result in unnecessary spending and food loss. Therefore, there is a need for a system that can monitor the food availability in the refrigerator in real time and provide appropriate recipe suggestions and replenishment advice. [Means for solving the problem]

[0005] The present invention is a system that includes means for collecting data using cameras and sensors installed inside the refrigerator, means for transmitting the collected data to a server, means for the server to analyze the collected data using image analysis technology and generate a list of foods in the refrigerator, means for suggesting usable recipes based on the generated food list, means for identifying foods from the food list that are close to their expiration date or that are low in stock and generating management advice, means for notifying the user of the suggested recipes and management advice, and means for the user to check the status of the refrigerator while away from home.This allows the user to understand the status of ingredients in the refrigerator in real time and consume or replenish them at the appropriate time, reducing food waste and leading to an efficient diet.

[0006] A "camera" is a device installed to record the situation inside the refrigerator as image data.

[0007] A "sensor" is a device used to measure physical data such as weight and temperature inside a refrigerator.

[0008] "Means for collecting data" refers to a method or device for measuring the conditions inside the refrigerator using a camera or sensor and acquiring that data.

[0009] "Server" is a computer system that analyzes collected data and provides the computing resources to generate and manage the necessary information.

[0010] "Image analysis technology" refers to algorithms and methods for identifying objects and extracting features from acquired image data.

[0011] The "food list" is a list showing the types and quantities of food stored in the refrigerator.

[0012] "Recipe suggestions" are information that provides the user with cooking instructions for dishes that can be prepared based on the list of foods in the refrigerator.

[0013] "Management advice" is information that makes consumption and purchase recommendations based on the expiration date and inventory status of food in the refrigerator.

[0014] "Means for notifying" refers to a method or device for communicating suggested recipes or management advice to the user.

[0015] "Means for checking from outside the home" refers to a method or device that allows a user to access and check the status of the refrigerator from a remote location. [Brief explanation of the drawings]

[0016] [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. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0017] 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.

[0018] First, the terms used in the following description will be explained.

[0019] 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, a 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), and an APU (Accelerated Processing Unit).

[0020] 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.

[0021] 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.

[0022] 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), Bluetooth (registered trademark), etc.

[0023] 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."

[0024] [First embodiment]

[0025] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0026] 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.

[0027] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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).

[0028] 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.

[0029] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. 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 acquires the data indicating the user input.

[0030] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The 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.

[0031] 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.

[0032] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0033] 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.

[0034] 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.

[0035] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0036] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0037] The following describes an embodiment of the present invention. The present invention is a system that monitors the inside of a refrigerator, grasps the status of ingredients, and provides appropriate recipe suggestions and management advice to the user. This system consists of a camera and sensor installed in the refrigerator, a server, and a smartphone application on the user's smartphone.

[0038] 1. Refrigerator monitoring

[0039] First, a camera and weight sensor are installed in the refrigerator. The camera periodically captures images of the inside of the refrigerator, and the weight sensor measures the weight of each shelf and food item. This allows basic data to be collected about where and how much food is in the refrigerator.

[0040] 2. Data collection and transmission

[0041] The terminal (refrigerator) sends the collected image data and weight data to the server. The input data includes information about the type and amount of ingredients.

[0042] 3. Data Analysis

[0043] The server analyzes the received image data using image analysis technology. This allows it to recognize each ingredient in the refrigerator and identify its type and location. It also calculates the amount of each ingredient from the weight data. For example, if the server analyzes an image of a tomato and the weight sensor provides the tomato's weight, the server can determine how many tomatoes are left in the refrigerator.

[0044] Based on the recognized ingredients and their quantities, the server generates a list of the food items in the refrigerator, which includes all the ingredients and their respective quantities.

[0045] 4. Recipe suggestions

[0046] The server searches for available recipes based on the generated food list. Recipe suggestions take into account the user's food preferences, allergy information, and nutritional balance. For example, if the user is vegetarian, meat-free recipes will be suggested first.

[0047] The server filters the possible recipes and selects the appropriate recipe for the user, allowing them to create a dish that makes the most of the ingredients in their refrigerator.

[0048] 5. Management Advice

[0049] The server also identifies items in the food list that are close to expiry or low in stock, such as yogurt that is expiring the next day or milk that is low in stock, and uses this information to generate food consumption priorities and replenishment advice.

[0050] 6. Notice and Information

[0051] The terminal (smartphone application) receives suggested recipes and management advice from the server and notifies the user. The user can then check the suggested recipes and ingredients to purchase via their smartphone.

[0052] The app also provides a feature that allows users to check the status of their refrigerators while they are out. For example, if a user opens the app while shopping at a supermarket, they can check in real time what ingredients are in the refrigerator and what they need.

[0053] Specific examples

[0054] For example, if you have tomatoes, onions, and cheese in your refrigerator, the server will suggest a recipe for tomato pasta based on these ingredients, advise you to use tomatoes that are close to their expiration date soon, and notify you to buy more milk if you are running low on it.

[0055] The above is an embodiment of the present invention. In this way, it is possible to efficiently manage food ingredients in a refrigerator, reduce waste, and support the user's eating habits.

[0056] The processing flow will be explained below.

[0057] Step 1:

[0058] The terminal (refrigerator) captures image data using a camera inside the refrigerator, and a weight sensor measures the weight data of each shelf and food item.

[0059] Step 2:

[0060] The terminal (refrigerator) sends the captured image data and weight data to the server.

[0061] Step 3:

[0062] The server uses image analysis technology to analyze the received image data and recognize each food item in the refrigerator. For example, tomatoes and onions can be identified using image analysis technology.

[0063] Step 4:

[0064] The server analyzes the weight data and calculates the amount of each recognized ingredient. For example, based on the weight sensor data, it determines that there are three tomatoes and two onions.

[0065] Step 5:

[0066] The server generates a food list based on the recognized types and quantities of ingredients, which records all ingredients in the refrigerator and their quantities.

[0067] Step 6:

[0068] The server searches the database for available recipes based on the food list, taking into account the user's food preferences, nutritional balance, and allergy information.

[0069] Step 7:

[0070] The server filters the searched recipes and selects recipes that are suitable for the user, for example, if the user is vegetarian, meat-free recipes are selected.

[0071] Step 8:

[0072] The server analyzes the food list and identifies items that are nearing expiration or low in stock, for example, detecting yogurt that is about to expire or milk that is running low.

[0073] Step 9:

[0074] The server generates management advice based on food items that are nearing expiration or low in stock, such as "consume the yogurt quickly" or "buy more milk."

[0075] Step 10:

[0076] The device (smartphone application) receives recipe suggestions and management advice from the server and notifies the user. The notification content includes today's recipe suggestions and consumption / purchase advice.

[0077] Step 11:

[0078] Users can check the status of their refrigerators while they are out using a smartphone app. For example, while shopping, users can check what is left in the refrigerator in real time and purchase the ingredients they need.

[0079] Example 1

[0080] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0081] Managing food in the refrigerator manually is extremely time-consuming and prone to oversight, resulting in food waste. It is also difficult for users to accurately grasp food inventory and expiration dates, which often prevents them from making appropriate cooking plans. Furthermore, it is difficult for users to check the status of their refrigerators while away from home, which increases the risk of purchasing duplicate ingredients when shopping. To solve these issues, there is a need for an efficient, automated food management and cooking suggestion system.

[0082] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0083] In this invention, the server includes means for collecting data using a photographing device and a weight measuring device installed in the refrigerator, means for transmitting the collected data to an information processing device, means for the information processing device to analyze the collected data using image analysis technology and generate an ingredient list for the refrigerator, means for suggesting available recipes based on the generated ingredient list, means for identifying ingredients from the ingredient list that are close to their expiration date or that are low in stock and generating management advice, means for notifying the user of the suggested recipes and management advice, and means for the user to check the status of the refrigerator while away from home. This automatically manages the ingredients in the refrigerator and allows the user to obtain accurate ingredient information, enabling efficient ingredient use and reduced waste.

[0084] "Photography Device" means a camera or other photographic device installed to capture images inside the refrigerator.

[0085] "Weight measuring device" refers to a sensor or weighing device installed to measure the weight of food on each shelf or in a refrigerator.

[0086] An "information processing device" is a computing device such as a computer or server that analyzes collected data and generates various instructions.

[0087] "Image analysis technology" refers to algorithms and software that analyze captured image data and recognize specific objects or conditions.

[0088] The "ingredient list" is a data list that compiles information such as the type, quantity, and location of ingredients in the refrigerator.

[0089] "Cooking method" refers to specific steps or recipes that a user uses to cook food using ingredients.

[0090] "Management advice" refers to advice on consumption priorities and replenishment provided to the user based on the expiration dates and stock status of food ingredients in the refrigerator.

[0091] "Notification means" refers to a smartphone application or push notification system that notifies users of information and suggestions from the server.

[0092] "Means for checking from outside the home" refers to functions and services that allow users to remotely check the status of the refrigerator.

[0093] "Store deals" refers to promotional information such as price discounts and special sales offered by nearby retail stores.

[0094] MODE FOR CARRYING OUT THE INVENTION

[0095] The following describes in detail an embodiment of the present invention. The present invention is a system that monitors the inside of a refrigerator, grasps the status of ingredients, and provides appropriate recipe suggestions and management advice to the user. This system is composed of a photographing device and a weight measuring device installed in the refrigerator, an information processing device, and a smartphone application for the user.

[0096] System Hardware and Software

[0097] Hardware

[0098] Image capture device: A camera (e.g., a standard digital camera) that captures images of the inside of the refrigerator.

[0099] Weight measuring device: A sensor (e.g., a digital scale) that measures the weight of each shelf or item in the refrigerator.

[0100] Information processing device: A computer or server (e.g., a virtual server on the cloud) that analyzes data and generates various instructions.

[0101] User device: Smartphone, tablet, etc.

[0102] software

[0103] Image analysis software: Algorithms for analyzing image data and recognizing ingredients in the refrigerator (e.g., OpenCV).

[0104] Data analysis software: Software for analyzing weight data (e.g., TENSORFLOW (registered trademark)).

[0105] Smartphone application: An app (e.g., iOS, ANDROID app) that sends notifications to the user and displays the status of the refrigerator.

[0106] Data collection and analysis

[0107] Terminal (refrigerator)

[0108] A camera installed inside the refrigerator periodically captures images of the interior of the refrigerator. For example, the camera takes a picture of the interior of the refrigerator at 8:00 AM every day. In addition, weight sensors installed on each shelf measure the weight of ingredients in real time. This allows data to be collected about what ingredients are in which position in the refrigerator and how much of each ingredient is in each position.

[0109] server

[0110] The information processing device (server) receives the image data and weight data sent from the refrigerator and analyzes them using image analysis technology. It uses OpenCV to recognize ingredients from the image and TensorFlow to analyze the weight data, thereby identifying the type and quantity of each ingredient. For example, it analyzes an image of tomatoes and calculates the number of tomatoes from the weight data. Based on this, a list of ingredients in the refrigerator is generated.

[0111] Recipe suggestions and management advice

[0112] server

[0113] Based on the generated ingredient list, the server suggests recipes that take into account the user's food preferences and nutritional balance. For example, if the user is vegetarian, meat-free recipes will be suggested. It also identifies ingredients that are close to their expiration date or are low in stock, and generates advice on consumption priorities and replenishment. For example, it generates advice on yogurt whose expiration date is approaching the next day or milk that needs to be replenished.

[0114] Notifications and on-the-go visibility

[0115] Terminal (smartphone application)

[0116] The user's smartphone application receives notifications of recipe suggestions and management advice sent from the server. The user can check the suggested recipes and ingredients to purchase through their smartphone. The user can also check the status of their refrigerator in real time while they are out. For example, if a user opens the app while shopping at a supermarket, they can check the status of ingredients in their refrigerator and make a list of what they need.

[0117] Examples of concrete examples and prompts

[0118] Specific examples

[0119] For example, if there are tomatoes, onions, and cheese in the refrigerator, the information processing device will suggest a recipe for tomato pasta based on these ingredients. It will also advise the user to use tomatoes that are close to their expiration date as soon as possible, and notify the user to purchase more milk if their stock is low.

[0120] Prompt Sentence Examples

[0121] "Please explain how the system works to suggest recipes using ingredients available in the refrigerator."

[0122] "Please tell me the detailed method for monitoring the food in the refrigerator and analyzing the data."

[0123] The above is an embodiment of the present invention. By using this system, it is possible to efficiently manage food ingredients in the refrigerator, reduce waste, and support the user's eating habits.

[0124] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0125] Step 1: Collecting data from inside the refrigerator

[0126] Specific explanation

[0127] The terminal (refrigerator) collects data using a camera and a weighing device installed inside the refrigerator. The camera periodically captures images of the interior of the refrigerator, and the weighing device measures the weight of each shelf and food item.

[0128] Input and Output

[0129] Input: Image data from the imaging device, weight data from the weight measuring device

[0130] Data processing: image capture, weight measurement

[0131] Output: Image data and weight data of the inside of the refrigerator

[0132] Specific actions

[0133] The camera captures images at regular intervals, adjusting the shutter speed, and the weighing device synchronizes to generate a digital signal to record the weight of each ingredient.

[0134] Step 2: Sending data

[0135] Specific explanation

[0136] The device (refrigerator) sends the collected image and weight data to a server over the Internet. The data is encrypted and transmitted using a secure protocol (e.g., HTTPS).

[0137] Input and Output

[0138] Input: Image data of the inside of the refrigerator, weight data

[0139] Data calculation: Encrypting data and converting it into a secure protocol

[0140] Output: Image data and weight data sent to the server

[0141] Specific actions

[0142] The refrigerator's built-in modem sends the data to the server's API endpoint, and once the transmission is complete, the server responds with a response acknowledging receipt of the data.

[0143] Step 3: Image and data analysis

[0144] Specific explanation

[0145] The server analyzes the received image data and weight data. It uses image analysis technology (e.g., OpenCV) to recognize ingredients from the image and weight analysis software (e.g., TensorFlow) to analyze the weight data. This identifies the type and amount of each ingredient.

[0146] Input and Output

[0147] Input: Image data and weight data sent to the server

[0148] Data calculation: Ingredient recognition (image analysis), weight estimation (weight data analysis)

[0149] Output: Analyzed ingredient list (type, amount, location)

[0150] Specific actions

[0151] OpenCV identifies ingredients such as tomatoes, cheese, and onions from the image, and TensorFlow estimates the quantity of each ingredient based on the weight data. The analysis results are then integrated to generate a detailed list of ingredients.

[0152] Step 4: Generate a food list

[0153] Specific explanation

[0154] The server then generates a list of the food items in the refrigerator based on the analyzed data, including the type of food, the amount, and the location of each food item in the refrigerator.

[0155] Input and Output

[0156] Input: Parsed ingredient list

[0157] Data processing: data integration and classification

[0158] Output: Final food list

[0159] Specific actions

[0160] The server stores the list of ingredients in a database and updates the increase or decrease in ingredients by comparing it with past consumption data.

[0161] Step 5: Recipe suggestions

[0162] Specific explanation

[0163] The server searches for available recipes based on the generated food list, and filters suitable recipes based on the user's preferences and nutritional balance.

[0164] Input and Output

[0165] Input: Final food list, user's food preferences and nutritional information

[0166] Data calculation: recipe search, filtering

[0167] Output: Recipe suggested to the user

[0168] Specific actions

[0169] The server accesses the recipe database and searches for recipes that match the criteria, and the filtered recipes are notified to the user.

[0170] Step 6: Generate management advice

[0171] Specific explanation

[0172] The server identifies ingredients from the food list that are close to their expiration date or are low in stock, and generates consumption priorities and replenishment advice.

[0173] Input and Output

[0174] Input: Final food list

[0175] Data calculation: Expiration date confirmation, stock confirmation, advice generation

[0176] Output: Management advice

[0177] Specific actions

[0178] The server scans the food list and generates appropriate advice based on expiration dates and stock levels.

[0179] Step 7: Notify and inform

[0180] Specific explanation

[0181] The terminal (smartphone application) receives suggested recipes and management advice from the server and notifies the user, who can then check the information in real time through the app.

[0182] Input and Output

[0183] Input: Suggested recipes, management advice

[0184] Data processing: notification generation, information display

[0185] Output: User notifications and real-time information

[0186] Specific actions

[0187] The smartphone app receives the push notification and displays the latest information on the screen. The user can then operate the app to check more detailed information.

[0188] The above is a concrete explanation of the program processing of this system.

[0189] (Application example 1)

[0190] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0191] The problem is that it is not possible to efficiently manage and utilize food in the refrigerator, and that food expires or is out of stock without the user being aware of it.Furthermore, it is also an issue that users cannot immediately order the ingredients they need, making meal planning a hassle.

[0192] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0193] In this invention, the server includes means for collecting data using an image capture device and a mass measurement device installed in the refrigerator, means for transmitting the collected data to an information processing device, means for the information processing device to analyze the collected data using image processing technology and generate a list of foods in the refrigerator, means for suggesting available cooking methods based on the generated food list, means for identifying foods from the food list that are close to their expiration date or are low in stock and generating food management advice, means for notifying the user of the suggested cooking methods and food management advice, means for the user to remotely check the status of the refrigerator, and means for automatically ordering ingredients that are in short supply from a food delivery service. This enables efficient food management and use, reduces food waste, and supports the user's meal planning.

[0194] An "image capture device" is a device that captures images of the inside of a refrigerator and recognizes the type and location of food.

[0195] A "mass measuring device" is a device that measures the weight of each shelf and food item in a refrigerator to determine the amount of food.

[0196] The "information processing device" is a device that receives and analyzes the collected data and generates a food list.

[0197] "Image processing technology" is a technology that analyzes captured images to recognize the type and location of food.

[0198] A "food inventory" is a list that records all the types and quantities of food in the refrigerator.

[0199] "Cooking Instructions" are recommended cooking recipes and steps based on the food list.

[0200] "Management advice" refers to advice on consumption priorities and replenishment for food items that are close to their expiration date or that are low in stock.

[0201] The "notification means" is a means for informing the user of suggested cooking methods and management advice.

[0202] The "means for checking from a remote location" is a means by which a user can check the status of the refrigerator in real time while away from home.

[0203] The "means for automatically ordering from a food delivery service" is a means for automatically ordering missing ingredients from a food delivery service.

[0204] The present invention is a system for managing food in a refrigerator and providing users with recipe suggestions and management advice. This system uses the following hardware and software.

[0205] Hardware

[0206] Image capture device: A device that captures images inside the refrigerator, specifically using a Raspberry Pi camera module.

[0207] Mass measuring device: A device that measures the weight of each shelf or food item, specifically using a weight sensor such as the HX711.

[0208] Information processing device: A server that analyzes data and generates a food list.

[0209] User terminal: A device such as a smartphone or tablet through which a user receives information.

[0210] software

[0211] Image processing technology: OpenCV is used for image analysis to identify the type and location of food.

[0212] Data analysis techniques: Analyze mass measurement results and calculate food volume. Use Python's Flask framework and TensorFlow.

[0213] Backend system: A system for collecting data, analyzing it, generating lists, and suggesting recipes, using Firebase as a database.

[0214] Specific processing flow

[0215] Data collection

[0216] 1. An image capture device periodically captures images of the inside of the refrigerator, and a mass measurement device measures the weight of each food item.

[0217] 2. The collected data is sent to an information processing device (server) via Raspberry Pi.

[0218] Data analysis

[0219] 1. The server analyzes the received image data using OpenCV to identify the type and location of the food.

[0220] 2. Based on the data from the mass measuring device, calculate the weight of each food item and determine the amount of food.

[0221] 3. The server integrates this data and generates a list of the food items in the refrigerator.

[0222] Recipe suggestions and management advice

[0223] 1. Based on the food list, the server uses a generative AI model using TensorFlow to suggest suitable recipes.

[0224] 2. Identify foods that are close to expiry date or low in stock from the food inventory and generate management advice based on this.

[0225] 3. Management advice and recipe suggestions are sent to the user's device via Firebase.

[0226] Automated ordering for food delivery services

[0227] 1. Based on the recipe suggestions, identify the missing ingredients.

[0228] 2. The server automatically calls the food delivery service API and orders the necessary ingredients.

[0229] Examples of use

[0230] If you have tomatoes, onions, and cheese in your fridge, the server will suggest a tomato pasta recipe based on that information. If you're low on pasta, the server will automatically order it from a food delivery service and notify you.

[0231] Prompt Sentence Examples

[0232] "If I have tomatoes, onions, and cheese in the fridge, I want to suggest a recipe using them and order any missing ingredients using a food delivery service API. What specific program would you use and what framework would you use?"

[0233] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0234] Step 1:

[0235] Data inside the refrigerator is collected. An image capture device (Raspberry Pi camera module) periodically captures images inside the refrigerator, and a mass measurement device (HX711) measures the weight of each food item. The input is real-time images inside the refrigerator and the weight data of each food item, and the output is a set of these data.

[0236] Step 2:

[0237] The collected data is sent to an information processing device (server). The Raspberry Pi sends image data and weight data to the information processing device. The input is the image and weight data acquired inside the refrigerator, and the output is the data sent to the server.

[0238] Step 3:

[0239] The server analyzes the received data. First, it performs image analysis on the image data using OpenCV to identify the type and location of the food. Next, it calculates the weight of each food item based on the weight data. The input is the transmitted image data and weight data, and the output is a list of the analyzed food types, locations, and weights.

[0240] Step 4:

[0241] A food list is generated based on the analyzed data. The server integrates the identified food information and generates a list of the food in the refrigerator. The input is a list of food types, locations, and weights, and the output is the food list.

[0242] Step 5:

[0243] Recipes are suggested based on a food list. The server uses a generative AI model using TensorFlow to search for and suggest appropriate recipes from a food list. The input is a food list, and the output is a suggested recipe.

[0244] Step 6:

[0245] Generate management advice. The server identifies foods that are close to their expiration date or low in stock from the food list, and generates management advice based on that. The input is the food list, and the output is management advice.

[0246] Step 7:

[0247] Suggested recipes and management advice are sent to the user's device. The server sends the suggested recipes and management advice to the user's smartphone via Firebase. The input is the suggested recipes and management advice, and the output is a notification sent to the user's device.

[0248] Step 8:

[0249] Automatically orders missing ingredients. The server identifies missing ingredients based on the proposed recipe and calls the food delivery service's API to automatically place the order. The input is the proposed recipe and a list of foods, and the output is the order data sent to the food delivery service.

[0250] Specific operations at each step include, for example, feature extraction in image analysis, calibration of weight data, and consideration of user preferences when proposing recipes.

[0251] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0252] Below, we will explain an embodiment of the present invention that combines a user emotion engine. The present invention combines an emotion engine that recognizes the user's emotions with a system that monitors the inside of a refrigerator, grasps the status of ingredients, and provides the user with appropriate recipe suggestions and management advice. This system consists of a camera and sensor installed in the refrigerator, a server, a user's smartphone application, and an emotion engine.

[0253] 1. Refrigerator monitoring

[0254] First, a camera and weight sensor are installed in the refrigerator. The camera periodically captures images of the inside of the refrigerator, and the weight sensor measures the weight of each shelf and food item. This allows basic data to be collected about where and how much food is in the refrigerator.

[0255] 2. Data collection and transmission

[0256] The terminal (refrigerator) sends the collected image data and weight data to the server. The input data includes information about the type and amount of ingredients.

[0257] 3. Data Analysis

[0258] The server analyzes the received image data using image analysis technology. This allows it to recognize each ingredient in the refrigerator and identify its type and location. It also calculates the amount of each ingredient from the weight data. For example, if the server analyzes an image of a tomato and the weight sensor provides the tomato's weight, the server can determine how many tomatoes are left in the refrigerator.

[0259] Based on the recognized ingredients and their quantities, the server generates a list of the food items in the refrigerator, which includes all the ingredients and their respective quantities.

[0260] 4. Recipe suggestions and emotional engine

[0261] The server searches for available recipes based on the generated food list, taking into account the user's food preferences, nutritional balance, allergy information, and even the user's current emotional state using an emotion engine.

[0262] The emotion engine evaluates the user's emotional state through facial and voice analysis. For example, if the user is very tired, easy-to-make recipes will be prioritized.

[0263] The server uses the data obtained from the emotion engine to adjust the difficulty and genre of the recipes and provide recipes that best suit the user's emotions and situation. For example, if the user is tired, simple recipes with short cooking times will be suggested, while if the user is in a happy mood, special dishes that require a little more effort will be suggested.

[0264] 5. Management advice and emotional engine

[0265] The server also identifies items in the food list that are about to expire or are low in stock, such as yogurt that is expiring the next day or milk that is low in stock.

[0266] The emotion engine adjusts the priority of management advice based on the user's emotions: for example, if the user is feeling stressed, it will highlight foods that need to be consumed immediately and encourage positive behavior.

[0267] Based on this information, the server generates advice on food consumption priorities and replenishment, allowing users to efficiently manage the ingredients in their refrigerators.

[0268] 6. Notice and Information

[0269] The device (smartphone application) receives suggested recipes and management advice from the server and notifies the user. The notification content includes today's recipe suggestions and consumption / purchase advice.

[0270] The app also provides a feature that allows users to check the status of their refrigerators while they are out. For example, if a user opens the app while shopping at a supermarket, they can check in real time what ingredients are in the refrigerator and what they need.

[0271] Specific examples

[0272] For example, if you have tomatoes, onions, and cheese in your refrigerator, the server will suggest a recipe for tomato pasta based on these ingredients, advise you to use tomatoes that are close to their expiration date soon, and notify you to buy more milk if you are running low on it.

[0273] Furthermore, if the user comes home tired, the emotion engine will recognize the user's emotional state and suggest an easy-to-make tomato salad recipe, allowing the user to prepare dinner without any stress.

[0274] The above is an embodiment of the present invention. In this way, it is possible to efficiently manage ingredients in a refrigerator, reduce waste, and provide flexible recipe suggestions and management advice that correspond to the user's emotional state.

[0275] The processing flow will be explained below.

[0276] Step 1:

[0277] The terminal (refrigerator) captures image data using a camera inside the refrigerator, and a weight sensor measures the weight data of each shelf and food item.

[0278] Step 2:

[0279] The terminal (refrigerator) sends the captured image data and weight data to the server.

[0280] Step 3:

[0281] The server uses image analysis technology to analyze the received image data and recognize each food item in the refrigerator. For example, tomatoes and onions can be identified using image analysis technology.

[0282] Step 4:

[0283] The server analyzes the weight data and calculates the amount of each recognized ingredient. For example, based on the weight sensor data, it determines that there are three tomatoes and two onions.

[0284] Step 5:

[0285] The server generates a food list based on the recognized types and quantities of ingredients, which records all ingredients in the refrigerator and their quantities.

[0286] Step 6:

[0287] The server searches the database for available recipes based on the food list, taking into account the user's food preferences, nutritional balance, and input from the emotion engine.

[0288] Step 7:

[0289] The emotion engine recognizes the user's emotional state, for example, by analyzing facial expressions and voice to determine whether the user is tired or happy.

[0290] Step 8:

[0291] The server adjusts the difficulty and genre of the recipes based on the emotion engine data and selects recipes that are suitable for the user. For example, if the user is tired, it will suggest easy recipes.

[0292] Step 9:

[0293] The server analyzes the food list and identifies items that are nearing expiration or low in stock, for example, detecting yogurt that is about to expire or milk that is running low.

[0294] Step 10:

[0295] An emotion engine adjusts the priority of management advice based on the user's emotions, for example highlighting only important items if the user is busy.

[0296] Step 11:

[0297] The server generates management advice based on food items that are close to expiry or low in stock, such as "consume yogurt quickly" or "purchase more milk."

[0298] Step 12:

[0299] The device (smartphone application) receives suggested recipes and management advice from the server and notifies the user. For example, it sends a notification such as "Today's Easy Recipe: Tomato Salad."

[0300] Step 13:

[0301] Users can check the status of their refrigerators while they are out using a smartphone app. For example, they can open the app while shopping and check the ingredients in their refrigerators and their expiration dates.

[0302] Step 14:

[0303] The app consumes and replenishes ingredients based on the recipes and management advice users receive, and with the assistance of an emotion engine, prepares dishes that suit the user's mood that day.

[0304] Example 2

[0305] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0306] Currently, there are systems on the market that aim to manage ingredients in a refrigerator and suggest recipes, but these systems do not take into account the user's emotional state, making it difficult to provide appropriate recipe suggestions or management advice when the user is tired or under stress. Furthermore, they lack the functionality to check the refrigerator status in real time while away from home, making efficient ingredient management impossible. Furthermore, even if they can identify foods that are close to their expiration date or low in stock, they lack a way to appropriately notify the user of this information based on their emotional state.

[0307] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes: means for collecting data using a camera and sensors installed in the refrigerator; means for transmitting the collected data to the server; means for analyzing the collected data using image analysis technology and generating a list of foods in the refrigerator; means for suggesting available recipes based on the generated food list; means for identifying foods from the food list that are close to their expiration date or low in stock and generating management advice; means for notifying the user of the suggested recipes and management advice; means for allowing the user to access the food ingredient data in the refrigerator; means for providing an emotion engine for analyzing the user's emotional state; means for adjusting the difficulty level and genre of recipes based on data obtained from the emotion engine and providing optimal recipes; means for adjusting the priority of management advice based on the user's emotional state; and means for the user to check the status of the refrigerator while away from home. This enables flexible recipe suggestions and management advice to be provided taking the user's emotional state into consideration, thereby achieving efficient food ingredient management and appropriate information notification.

[0308] The "camera" is a photographic device for capturing images inside the refrigerator.

[0309] A "sensor" is a device that measures physical data such as the weight and position of food items in a refrigerator.

[0310] The "means of collecting data" refers to a system that uses cameras and sensors to obtain information about the inside of the refrigerator.

[0311] A "server" is a computer system that receives, stores, analyzes, and processes data.

[0312] "Image analysis technology" is a technology that processes collected image data to identify the type and location of ingredients.

[0313] A "food list" is data that records the types and amounts of food ingredients present in a refrigerator.

[0314] The "means for suggesting recipes" is a mechanism that provides users with recipe suggestions based on a food list.

[0315] "Management advice" is advice on how to handle or replenish food that is nearing its expiration date or is low in stock.

[0316] The "notification means" is a communication means for conveying suggested recipes and management advice to the user.

[0317] An "emotion engine" is software and algorithms for analyzing a user's emotional state.

[0318] The "means for checking the status of the refrigerator" is a system that allows the user to view information about ingredients in the refrigerator even when they are away from home.

[0319] "Means for analyzing emotional state" refers to technology that recognizes emotions from the user's facial expressions, voice, etc.

[0320] "Means for adjusting the difficulty and genre of recipes" refers to a mechanism that appropriately changes the content of suggested recipes based on data obtained from the emotion engine.

[0321] The "means for adjusting priority" is a mechanism for changing the importance and notification order of management advice depending on the emotional state of the user.

[0322] This invention combines a system that monitors the inside of a refrigerator, grasps the status of ingredients, and provides users with appropriate recipe suggestions and management advice, with an emotion engine that recognizes the user's emotions. The system consists of a camera and sensors installed in the refrigerator, a server, a user's smartphone application, and the emotion engine.

[0323] First, a camera and weight sensor are installed inside the device (refrigerator). The camera periodically captures images of the inside of the refrigerator, and the weight sensor measures the weight of each shelf and food item. This allows data to be collected about where and how much food is in the refrigerator. For example, by capturing an image of tomatoes placed on a shelf in the refrigerator and measuring their weight, the location and quantity of tomatoes can be determined.

[0324] Next, the collected image data and weight data are sent from the terminal (refrigerator) to a server. The server receives the data and stores it in a database. Specifically, the image data is stored in storage, and the weight data is associated with each ingredient. A high-speed database management system is recommended for this process.

[0325] The server analyzes the received data using image analysis technology. For example, it uses image recognition libraries such as OpenCV and TensorFlow to identify the type and location of each ingredient. It also calculates the quantity of each ingredient based on the weight data and generates a list of the food in the refrigerator. For example, from an analyzed image of a tomato, it recognizes that the ingredient is a "tomato," and combines this with the weight data to determine how many tomatoes are in the refrigerator.

[0326] The server then searches for available recipes from a database based on the generated food list, taking into account the user's food preferences, nutritional balance, and allergy information. A database management system such as MySQL (registered trademark) can be used to retrieve filtered recipes by referencing the user's profile information.

[0327] The emotion engine evaluates the user's emotional state through facial expression and voice analysis. For example, it utilizes Microsoft® Azure® Face API and Google® Cloud's Speech-to-Text service. Based on the emotional data obtained from the emotion engine, the server adjusts the difficulty and genre of the recipe and provides the user with the most suitable recipe. Specifically, if the user is very tired, it will suggest a recipe with a short cooking time, and if the user wants to enjoy cooking, it will suggest a slightly more time-consuming dish.

[0328] The server also identifies foods from the food list that are close to expiry or low in stock and generates management advice based on that. For example, a Python script can scan the food list, extract foods that fit certain criteria (such as yogurt that is close to expiry or milk that is low in stock), and generate management advice for them.

[0329] The device (smartphone application) then receives suggested recipes and management advice from the server and notifies the user. Specifically, push notifications are sent using Firebase Cloud Messaging, and information is delivered to the user's smartphone in real time. A function is also provided that allows users to check the status of their refrigerator while they are out. For example, if you open the app while shopping at the supermarket, you can find out in real time what ingredients are in your refrigerator and what is missing.

[0330] Specific examples

[0331] For example, if the user has tomatoes, onions, and cheese in the refrigerator, the server will suggest a tomato pasta recipe based on these ingredients. It will also advise the user to use tomatoes that are close to their expiration date soon and notify the user to purchase more milk if they are running low on stock. Furthermore, if the user comes home tired, the emotion engine will recognize the user's emotional state and suggest an easy tomato salad recipe, allowing the user to prepare dinner without any stress.

[0332] Prompt Sentence Examples

[0333] An example of a prompt for a generative AI model is:

[0334] "Explain in detail the refrigerator food management system that leverages the user's emotion engine, specifically each step from food monitoring to data analysis, recipe suggestions, management advice and notifications."

[0335] This concludes the description of the specific embodiment for carrying out the invention. By using this system, users can efficiently manage ingredients in their refrigerators, reduce waste, and receive flexible recipe suggestions and management advice that reflect their emotional state.

[0336] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0337] Step 1: Data collection

[0338] The terminal (refrigerator) collects food ingredient data using a camera and weight sensor installed inside. The camera periodically takes pictures of the inside of the refrigerator, and the weight sensor measures the weight of each shelf and food item. This makes it possible to obtain data on how much food is in which location inside the refrigerator. The input data is image data and weight data inside the refrigerator, and the output is information on the location and amount of food ingredients.

[0339] Step 2: Data transmission and storage

[0340] The terminal (refrigerator) sends the collected image data and weight data to the server. The server receives this data and stores it in a database. Specifically, the data is transferred via a high-speed network connection, and the data received by the server is stored in storage. The input data is the image data and weight data sent from the terminal, and the output is the raw data stored in the server's database.

[0341] Step 3: Image analysis and ingredient list generation

[0342] The server uses image analysis technology to analyze the received image data. For example, it uses OpenCV or TensorFlow to identify each ingredient and recognize its location and type. At the same time, it calculates the quantity of each ingredient from the weight data and generates a list of the food in the refrigerator. The input data is the saved image data and weight data, and the output is a list of recognized ingredients. Specifically, it recognizes tomatoes from the image and, in combination with the weight data, identifies the number of tomatoes.

[0343] Step 4: Recipe search and suggestions

[0344] The server searches for suitable recipes from a recipe database based on the generated food list, taking into account the user's food preferences, nutritional balance, and allergy information. Specifically, it uses MySQL or a similar database management system to extract filtered recipes based on the user's profile and food list. The input data is the generated food list and the user's profile information, and the output is suggested recipes.

[0345] Step 5: Leverage the Emotion Engine

[0346] The server optimizes recipe suggestions using data obtained from the user's emotion engine. Using Microsoft Azure's Face API and Google Cloud's Speech-to-Text service, the server analyzes the user's emotional state from their facial expressions and voice, and receives this data. The input data is the user's facial expression analysis data and voice analysis data, and the output is recipe suggestions that take their emotional state into account. Specifically, if the user is tired, it selects easy-to-make recipes.

[0347] Step 6: Generate management advice

[0348] The server identifies foods from the food list that are close to their expiration date or low in stock, and generates management advice based on that. A Python script scans the food list and extracts foods that meet certain criteria (e.g., expiration date within two days, stock of one unit or less). The input data is the generated food list, and the output is management advice. Specifically, it sends a notification to "consume soon" for yogurt that is close to its expiration date.

[0349] Step 7: Notification and Information

[0350] The device (smartphone application) receives suggested recipes and management advice from the server and notifies the user. Push notifications are sent using Firebase Cloud Messaging and delivered to the user's smartphone. In addition, a function is provided that allows users to check the status of their refrigerator while they are out. The input data is the recipe suggestions and management advice sent from the server, and the output is notifications and information displayed on the user's smartphone. In concrete terms, when a user opens the app while they are out, a list of ingredients in the refrigerator and a shopping list for necessary items are displayed.

[0351] The above is the specific processing flow and operation at each step.

[0352] (Application example 2)

[0353] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0354] While conventional refrigerator management systems could manage food inventory and expiration dates, they were unable to provide flexible recipe suggestions or management advice that took into account the user's current emotional state. As a result, even when users were stressed or tired, they were unable to obtain appropriate information, leading to problems such as overcooking or wasting ingredients. Furthermore, the system lacked the functionality to check the refrigerator status while away from home, which caused the inconvenience of not being able to determine what ingredients were needed when shopping.

[0355] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0356] In this invention, the server includes means for collecting data using cameras and sensors installed inside the refrigerator, means for transmitting the collected data to the server, means for the server to analyze the collected data using image analysis technology and generate a list of foods in the refrigerator, means for suggesting available recipes based on the generated food list, means for identifying foods from the food list that are close to their expiration date or that are low in stock and generating management advice, means for notifying the user of the suggested recipes and management advice, means for the user to check the status of the refrigerator while away from home, and means for recognizing the user's emotions and adjusting the recipe suggestions and management advice based on the user's emotional state. This enables flexible and appropriate recipe suggestions and management advice to be provided in accordance with the user's emotional state.

[0357] The camera installed inside the refrigerator is an image capturing device that periodically captures the status of the food ingredients inside the refrigerator.

[0358] The sensor is a device used to measure the weight and temperature of food in the refrigerator.

[0359] The means of collection is a method for obtaining data from cameras and sensors installed inside the refrigerator.

[0360] The means for transmitting is a communication technique for sending the collected data to the server.

[0361] Image analysis technology is a technology that analyzes collected image data and extracts useful information from photos and videos.

[0362] The means for generating a food list is a method for listing the types and amounts of ingredients in the refrigerator based on the analyzed data.

[0363] A recipe suggestion method is a method for presenting available dishes to a user based on a food list.

[0364] The means for generating management advice is a means for instructing the user on the optimal way to use ingredients and replenish them based on expiration dates and stock status.

[0365] Notification means are methods for informing users of suggested recipes and management advice.

[0366] The means for checking from outside the home is a technology that allows a user who is out to remotely check the status of the refrigerator.

[0367] Emotion recognition is a technology that analyzes a user's facial expressions and voice to determine their emotional state at that time.

[0368] The means for adjusting based on the emotional state is a method for changing the content of recipe suggestions and management advice depending on the user's emotions.

[0369] Below, we will explain an embodiment of the present invention that combines a user emotion engine. The present invention combines an emotion engine that recognizes the user's emotions with a system that monitors the inside of a refrigerator, grasps the status of ingredients, and provides the user with appropriate recipe suggestions and management advice. This system consists of a camera and sensor installed in the refrigerator, a server, a user's smartphone application, and an emotion engine.

[0370] 1. Refrigerator monitoring

[0371] First, a camera and a weight sensor are installed in the refrigerator. The camera periodically captures images of the inside of the refrigerator, and the weight sensor measures the weight of each shelf and food item. This data is collected as basic data on where and how much food is in the refrigerator.

[0372] 2. Data collection and transmission

[0373] The refrigerator (terminal) sends the collected image data and weight data to a server. The data is then sent to the server via the internet via a communication module installed in the terminal. The sent data includes information about the type of ingredients and their amounts.

[0374] 3. Data Analysis

[0375] The server uses image analysis technology to analyze the received image data. This allows it to recognize each ingredient in the refrigerator and identify its type and location. It then uses data obtained from the weight sensor to calculate the quantity of each ingredient. For example, it analyzes an image of a tomato and determines how many tomatoes are in the refrigerator based on the weight data. The server then generates a list of the food in the refrigerator based on the recognized ingredients and their quantities. This list records all the types of ingredients and their respective quantities.

[0376] 4. Recipe suggestions and emotional engine

[0377] The server searches for available recipes based on the generated food list. It takes into account the user's food preferences, nutritional balance, and allergy information, as well as their current emotional state using an emotion engine. The emotion engine evaluates the user's emotional state through facial expression and voice analysis. For example, if the user is very tired, easy-to-make recipes will be prioritized.

[0378] The system analyzes the user's facial expressions using a specially trained emotion recognition model. For example, it captures the user's facial expressions using a camera on a smartphone or smart glasses and applies an emotion recognition algorithm. Based on this, the server adjusts the difficulty and genre of the recipe to provide the most appropriate recipe for the emotion and situation. For example, if the user is tired, it will suggest a simple recipe such as tomato salad.

[0379] 5. Management advice and emotional engine

[0380] The server also identifies foods from the food list that are close to expiry or low in stock. The emotion engine adjusts the priority of management advice based on the user's emotions. For example, if the user is feeling stressed, it will highlight foods that need to be consumed immediately and encourage positive action. Based on this information, the server generates food consumption priorities and replenishment advice.

[0381] 6. Notice and Information

[0382] The refrigerator (device) notifies the user of recipe suggestions and management advice from the server. The notification content includes today's recipe suggestions and consumption / purchase advice via a smartphone application. It also provides a function that allows users to check the status of their refrigerator while they are out. For example, if a user opens the application while shopping at a supermarket, they can check in real time what ingredients are in the refrigerator and what they need.

[0383] Specific examples

[0384] For example, if the user has tomatoes, onions, and cheese in the refrigerator, the server will suggest a tomato pasta recipe based on these ingredients. It will also advise the user to use tomatoes that are close to their expiration date soon and notify the user to purchase more milk if they are running low on stock. Furthermore, if the user comes home tired, the emotion engine will recognize the user's emotional state and suggest an easy tomato salad recipe, allowing the user to prepare dinner without any stress.

[0385] Prompt Sentence Examples

[0386] "There are tomatoes and cheese in the fridge. The user seems tired. Can you suggest a simple recipe?"

[0387] The above is an embodiment of the present invention. In this way, it is possible to efficiently manage ingredients in a refrigerator, reduce waste, and provide flexible recipe suggestions and management advice that correspond to the user's emotional state.

[0388] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0389] Step 1:

[0390] Refrigerator status monitoring

[0391] Input: Data from cameras and sensors installed inside the refrigerator

[0392] Specific operation: The device periodically captures images of the inside of the refrigerator using a camera and obtains shelf and food weight data from a weight sensor.

[0393] Output: Image data and weight data

[0394] Step 2:

[0395] Data transmission

[0396] Input: Acquired image data and weight data

[0397] Specific operation: The device sends the collected data to a server via the Internet. This is done using a communication module.

[0398] Output: Image data and weight data sent to the server

[0399] Step 3:

[0400] Data analysis

[0401] Input: Image data and weight data sent to the server

[0402] Specific operation: The server uses image analysis technology to analyze the image data and identify the type and location of ingredients. It also analyzes the weight data to calculate the amount of each ingredient. For example, it recognizes tomatoes in the image data and determines how many tomatoes there are using the weight data.

[0403] Output: Food list (including ingredient types and quantities)

[0404] Step 4:

[0405] Recipe suggestions

[0406] Input: Food list, user's food preferences, nutritional balance, allergy information

[0407] Specific behavior: The server searches for available recipes based on the food list and the user's personal information. For example, if there are tomatoes and cheese, it may suggest a recipe for tomato pasta.

[0408] Output: A list of suggested recipes

[0409] Step 5:

[0410] emotion recognition

[0411] Input: User facial expression image or voice data

[0412] Specific operation: When a user uses a smartphone or smart glasses, the device's camera and microphone are used to capture facial expressions and voice in real time, and the captured data is analyzed using an emotion recognition model (generative AI model). For example, a tired facial expression is captured using a smartphone camera, and the model determines that the user is "tired."

[0413] Output: Determined emotional state

[0414] Step 6:

[0415] Tailoring recipe suggestions based on emotional state

[0416] Input: Determined emotional state, suggested recipe list

[0417] Specific operation: The server selects the most suitable recipe from the list depending on the user's emotional state. For example, if the user is tired, it will prioritize recipes that are easy to cook and require short cooking times.

[0418] Output: The recipe that best suits your emotional state

[0419] Step 7:

[0420] Generate management advice

[0421] Input: Food list, expiration date, inventory information

[0422] Specific behavior: The server identifies foods from the food list that are close to expiry or low in stock, and generates management advice for them. For example, it notifies the user to use tomatoes that are close to expiry or milk that is low in stock sooner or to replenish them.

[0423] Output: Management advice list

[0424] Step 8:

[0425] Notification and Information

[0426] Input: Recipes best suited to your emotional state, management advice list

[0427] Specific operation: The device (smartphone application) notifies the user of recipes and management advice that best suit their emotional state. It also provides a function that allows users to check the status of their refrigerator in real time. For example, while shopping at the supermarket, the user can check the refrigerator inventory on the app.

[0428] Output: Notifications to the user (recipe suggestions, management advice, refrigerator inventory information)

[0429] 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.

[0430] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. 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 voice, text data indicating text, and image data indicating an image is also input. 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.

[0431] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0432] [Second embodiment]

[0433] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0434] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0435] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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).

[0436] 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.

[0437] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0438] 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 surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0439] 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.

[0440] 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.

[0441] 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 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.

[0442] 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.

[0443] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0444] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0445] The following describes an embodiment of the present invention. The present invention is a system that monitors the inside of a refrigerator, grasps the status of ingredients, and provides appropriate recipe suggestions and management advice to the user. This system consists of a camera and sensor installed in the refrigerator, a server, and a smartphone application on the user's smartphone.

[0446] 1. Refrigerator monitoring

[0447] First, a camera and weight sensor are installed in the refrigerator. The camera periodically captures images of the inside of the refrigerator, and the weight sensor measures the weight of each shelf and food item. This allows basic data to be collected about where and how much food is in the refrigerator.

[0448] 2. Data collection and transmission

[0449] The terminal (refrigerator) sends the collected image data and weight data to the server. The input data includes information about the type and amount of ingredients.

[0450] 3. Data Analysis

[0451] The server analyzes the received image data using image analysis technology. This allows it to recognize each ingredient in the refrigerator and identify its type and location. It also calculates the amount of each ingredient from the weight data. For example, if the server analyzes an image of a tomato and the weight sensor provides the tomato's weight, the server can determine how many tomatoes are left in the refrigerator.

[0452] Based on the recognized ingredients and their quantities, the server generates a list of the food items in the refrigerator, which includes all the ingredients and their respective quantities.

[0453] 4. Recipe suggestions

[0454] The server searches for available recipes based on the generated food list. Recipe suggestions take into account the user's food preferences, allergy information, and nutritional balance. For example, if the user is vegetarian, meat-free recipes will be suggested first.

[0455] The server filters the possible recipes and selects the appropriate recipe for the user, allowing them to create a dish that makes the most of the ingredients in their refrigerator.

[0456] 5. Management Advice

[0457] The server also identifies items in the food list that are close to expiry or low in stock, such as yogurt that is expiring the next day or milk that is low in stock, and uses this information to generate food consumption priorities and replenishment advice.

[0458] 6. Notice and Information

[0459] The terminal (smartphone application) receives suggested recipes and management advice from the server and notifies the user. The user can then check the suggested recipes and ingredients to purchase via their smartphone.

[0460] The app also provides a feature that allows users to check the status of their refrigerators while they are out. For example, if a user opens the app while shopping at a supermarket, they can check in real time what ingredients are in the refrigerator and what they need.

[0461] Specific examples

[0462] For example, if you have tomatoes, onions, and cheese in your refrigerator, the server will suggest a recipe for tomato pasta based on these ingredients, advise you to use tomatoes that are close to their expiration date soon, and notify you to buy more milk if you are running low on it.

[0463] The above is an embodiment of the present invention. In this way, it is possible to efficiently manage food ingredients in a refrigerator, reduce waste, and support the user's eating habits.

[0464] The processing flow will be explained below.

[0465] Step 1:

[0466] The terminal (refrigerator) captures image data using a camera inside the refrigerator, and a weight sensor measures the weight data of each shelf and food item.

[0467] Step 2:

[0468] The terminal (refrigerator) sends the captured image data and weight data to the server.

[0469] Step 3:

[0470] The server uses image analysis technology to analyze the received image data and recognize each food item in the refrigerator. For example, tomatoes and onions can be identified using image analysis technology.

[0471] Step 4:

[0472] The server analyzes the weight data and calculates the amount of each recognized ingredient. For example, based on the weight sensor data, it determines that there are three tomatoes and two onions.

[0473] Step 5:

[0474] The server generates a food list based on the recognized types and quantities of ingredients, which records all ingredients in the refrigerator and their quantities.

[0475] Step 6:

[0476] The server searches the database for available recipes based on the food list, taking into account the user's food preferences, nutritional balance, and allergy information.

[0477] Step 7:

[0478] The server filters the searched recipes and selects recipes that are suitable for the user, for example, if the user is vegetarian, meat-free recipes are selected.

[0479] Step 8:

[0480] The server analyzes the food list and identifies items that are nearing expiration or low in stock, for example, detecting yogurt that is about to expire or milk that is running low.

[0481] Step 9:

[0482] The server generates management advice based on food items that are nearing expiration or low in stock, such as "consume the yogurt quickly" or "buy more milk."

[0483] Step 10:

[0484] The device (smartphone application) receives recipe suggestions and management advice from the server and notifies the user. The notification content includes today's recipe suggestions and consumption / purchase advice.

[0485] Step 11:

[0486] Users can check the status of their refrigerators while they are out using a smartphone app. For example, while shopping, users can check what is left in the refrigerator in real time and purchase the ingredients they need.

[0487] Example 1

[0488] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0489] Managing food in the refrigerator manually is extremely time-consuming and prone to oversight, resulting in food waste. It is also difficult for users to accurately grasp food inventory and expiration dates, which often prevents them from making appropriate cooking plans. Furthermore, it is difficult for users to check the status of their refrigerators while away from home, which increases the risk of purchasing duplicate ingredients when shopping. To solve these issues, there is a need for an efficient, automated food management and cooking suggestion system.

[0490] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0491] In this invention, the server includes means for collecting data using a photographing device and a weight measuring device installed in the refrigerator, means for transmitting the collected data to an information processing device, means for the information processing device to analyze the collected data using image analysis technology and generate an ingredient list for the refrigerator, means for suggesting available recipes based on the generated ingredient list, means for identifying ingredients from the ingredient list that are close to their expiration date or that are low in stock and generating management advice, means for notifying the user of the suggested recipes and management advice, and means for the user to check the status of the refrigerator while away from home. This automatically manages the ingredients in the refrigerator and allows the user to obtain accurate ingredient information, enabling efficient ingredient use and reduced waste.

[0492] "Photography Device" means a camera or other photographic device installed to capture images inside the refrigerator.

[0493] "Weight measuring device" refers to a sensor or weighing device installed to measure the weight of food on each shelf or in a refrigerator.

[0494] An "information processing device" is a computing device such as a computer or server that analyzes collected data and generates various instructions.

[0495] "Image analysis technology" refers to algorithms and software that analyze captured image data and recognize specific objects or conditions.

[0496] The "ingredient list" is a data list that compiles information such as the type, quantity, and location of ingredients in the refrigerator.

[0497] "Cooking method" refers to specific steps or recipes that a user uses to cook food using ingredients.

[0498] "Management advice" refers to advice on consumption priorities and replenishment provided to the user based on the expiration dates and stock status of food ingredients in the refrigerator.

[0499] "Notification means" refers to a smartphone application or push notification system that notifies users of information and suggestions from the server.

[0500] "Means for checking from outside the home" refers to functions and services that allow users to remotely check the status of the refrigerator.

[0501] "Store deals" refers to promotional information such as price discounts and special sales offered by nearby retail stores.

[0502] MODE FOR CARRYING OUT THE INVENTION

[0503] The following describes in detail an embodiment of the present invention. The present invention is a system that monitors the inside of a refrigerator, grasps the status of ingredients, and provides appropriate recipe suggestions and management advice to the user. This system is composed of a photographing device and a weight measuring device installed in the refrigerator, an information processing device, and a smartphone application for the user.

[0504] System Hardware and Software

[0505] Hardware

[0506] Image capture device: A camera (e.g., a standard digital camera) that captures images of the inside of the refrigerator.

[0507] Weight measuring device: A sensor (e.g., a digital scale) that measures the weight of each shelf or item in the refrigerator.

[0508] Information processing device: A computer or server (e.g., a virtual server on the cloud) that analyzes data and generates various instructions.

[0509] User device: Smartphone, tablet, etc.

[0510] software

[0511] Image analysis software: Algorithms for analyzing image data and recognizing ingredients in the refrigerator (e.g., OpenCV).

[0512] Data analysis software: Software for analyzing weight data (e.g., TensorFlow).

[0513] Smartphone application: An app to send notifications to the user and display the status of the refrigerator (e.g. iOS, Android app).

[0514] Data collection and analysis

[0515] Terminal (refrigerator)

[0516] A camera installed inside the refrigerator periodically captures images of the interior of the refrigerator. For example, the camera takes a picture of the interior of the refrigerator at 8:00 AM every day. In addition, weight sensors installed on each shelf measure the weight of ingredients in real time. This allows data to be collected about what ingredients are in which position in the refrigerator and how much of each ingredient is in each position.

[0517] server

[0518] The information processing device (server) receives the image data and weight data sent from the refrigerator and analyzes them using image analysis technology. It uses OpenCV to recognize ingredients from the image and TensorFlow to analyze the weight data, thereby identifying the type and quantity of each ingredient. For example, it analyzes an image of tomatoes and calculates the number of tomatoes from the weight data. Based on this, a list of ingredients in the refrigerator is generated.

[0519] Recipe suggestions and management advice

[0520] server

[0521] Based on the generated ingredient list, the server suggests recipes that take into account the user's food preferences and nutritional balance. For example, if the user is vegetarian, meat-free recipes will be suggested. It also identifies ingredients that are close to their expiration date or are low in stock, and generates advice on consumption priorities and replenishment. For example, it generates advice on yogurt whose expiration date is approaching the next day or milk that needs to be replenished.

[0522] Notifications and on-the-go visibility

[0523] Terminal (smartphone application)

[0524] The user's smartphone application receives notifications of recipe suggestions and management advice sent from the server. The user can check the suggested recipes and ingredients to purchase through their smartphone. The user can also check the status of their refrigerator in real time while they are out. For example, if a user opens the app while shopping at a supermarket, they can check the status of ingredients in their refrigerator and make a list of what they need.

[0525] Examples of concrete examples and prompts

[0526] Specific examples

[0527] For example, if there are tomatoes, onions, and cheese in the refrigerator, the information processing device will suggest a recipe for tomato pasta based on these ingredients. It will also advise the user to use tomatoes that are close to their expiration date as soon as possible, and notify the user to purchase more milk if their stock is low.

[0528] Prompt Sentence Examples

[0529] "Please explain how the system works to suggest recipes using ingredients available in the refrigerator."

[0530] "Please tell me the detailed method for monitoring the food in the refrigerator and analyzing the data."

[0531] The above is an embodiment of the present invention. By using this system, it is possible to efficiently manage food ingredients in the refrigerator, reduce waste, and support the user's eating habits.

[0532] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0533] Step 1: Collecting data from inside the refrigerator

[0534] Specific explanation

[0535] The terminal (refrigerator) collects data using a camera and a weighing device installed inside the refrigerator. The camera periodically captures images of the interior of the refrigerator, and the weighing device measures the weight of each shelf and food item.

[0536] Input and Output

[0537] Input: Image data from the imaging device, weight data from the weight measuring device

[0538] Data processing: image capture, weight measurement

[0539] Output: Image data and weight data of the inside of the refrigerator

[0540] Specific actions

[0541] The camera captures images at regular intervals, adjusting the shutter speed, and the weighing device synchronizes to generate a digital signal to record the weight of each ingredient.

[0542] Step 2: Sending data

[0543] Specific explanation

[0544] The device (refrigerator) sends the collected image and weight data to a server over the Internet. The data is encrypted and transmitted using a secure protocol (e.g., HTTPS).

[0545] Input and Output

[0546] Input: Image data of the inside of the refrigerator, weight data

[0547] Data calculation: Encrypting data and converting it into a secure protocol

[0548] Output: Image data and weight data sent to the server

[0549] Specific actions

[0550] The refrigerator's built-in modem sends the data to the server's API endpoint, and once the transmission is complete, the server responds with a response acknowledging receipt of the data.

[0551] Step 3: Image and data analysis

[0552] Specific explanation

[0553] The server analyzes the received image data and weight data. It uses image analysis technology (e.g., OpenCV) to recognize ingredients from the image and weight analysis software (e.g., TensorFlow) to analyze the weight data. This identifies the type and amount of each ingredient.

[0554] Input and Output

[0555] Input: Image data and weight data sent to the server

[0556] Data calculation: Ingredient recognition (image analysis), weight estimation (weight data analysis)

[0557] Output: Analyzed ingredient list (type, amount, location)

[0558] Specific actions

[0559] OpenCV identifies ingredients such as tomatoes, cheese, and onions from the image, and TensorFlow estimates the quantity of each ingredient based on the weight data. The analysis results are then integrated to generate a detailed list of ingredients.

[0560] Step 4: Generate a food list

[0561] Specific explanation

[0562] The server then generates a list of the food items in the refrigerator based on the analyzed data, including the type of food, the amount, and the location of each food item in the refrigerator.

[0563] Input and Output

[0564] Input: Parsed ingredient list

[0565] Data processing: data integration and classification

[0566] Output: Final food list

[0567] Specific actions

[0568] The server stores the list of ingredients in a database and updates the increase or decrease in ingredients by comparing it with past consumption data.

[0569] Step 5: Recipe suggestions

[0570] Specific explanation

[0571] The server searches for available recipes based on the generated food list, and filters suitable recipes based on the user's preferences and nutritional balance.

[0572] Input and Output

[0573] Input: Final food list, user's food preferences and nutritional information

[0574] Data calculation: recipe search, filtering

[0575] Output: Recipe suggested to the user

[0576] Specific actions

[0577] The server accesses the recipe database and searches for recipes that match the criteria, and the filtered recipes are notified to the user.

[0578] Step 6: Generate management advice

[0579] Specific explanation

[0580] The server identifies ingredients from the food list that are close to their expiration date or are low in stock, and generates consumption priorities and replenishment advice.

[0581] Input and Output

[0582] Input: Final food list

[0583] Data calculation: Expiration date confirmation, stock confirmation, advice generation

[0584] Output: Management advice

[0585] Specific actions

[0586] The server scans the food list and generates appropriate advice based on expiration dates and stock levels.

[0587] Step 7: Notify and inform

[0588] Specific explanation

[0589] The terminal (smartphone application) receives suggested recipes and management advice from the server and notifies the user, who can then check the information in real time through the app.

[0590] Input and Output

[0591] Input: Suggested recipes, management advice

[0592] Data processing: notification generation, information display

[0593] Output: User notifications and real-time information

[0594] Specific actions

[0595] The smartphone app receives the push notification and displays the latest information on the screen. The user can then operate the app to check more detailed information.

[0596] The above is a concrete explanation of the program processing of this system.

[0597] (Application example 1)

[0598] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0599] The problem is that it is not possible to efficiently manage and utilize food in the refrigerator, and that food expires or is out of stock without the user being aware of it.Furthermore, it is also an issue that users cannot immediately order the ingredients they need, making meal planning a hassle.

[0600] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0601] In this invention, the server includes means for collecting data using an image capture device and a mass measurement device installed in the refrigerator, means for transmitting the collected data to an information processing device, means for the information processing device to analyze the collected data using image processing technology and generate a list of foods in the refrigerator, means for suggesting available cooking methods based on the generated food list, means for identifying foods from the food list that are close to their expiration date or are low in stock and generating food management advice, means for notifying the user of the suggested cooking methods and food management advice, means for the user to remotely check the status of the refrigerator, and means for automatically ordering ingredients that are in short supply from a food delivery service. This enables efficient food management and use, reduces food waste, and supports the user's meal planning.

[0602] An "image capture device" is a device that captures images of the inside of a refrigerator and recognizes the type and location of food.

[0603] A "mass measuring device" is a device that measures the weight of each shelf and food item in a refrigerator to determine the amount of food.

[0604] The "information processing device" is a device that receives and analyzes the collected data and generates a food list.

[0605] "Image processing technology" is a technology that analyzes captured images to recognize the type and location of food.

[0606] A "food inventory" is a list that records all the types and quantities of food in the refrigerator.

[0607] "Cooking Instructions" are recommended cooking recipes and steps based on the food list.

[0608] "Management advice" refers to advice on consumption priorities and replenishment for food items that are close to their expiration date or that are low in stock.

[0609] The "notification means" is a means for informing the user of suggested cooking methods and management advice.

[0610] The "means for checking from a remote location" is a means by which a user can check the status of the refrigerator in real time while away from home.

[0611] The "means for automatically ordering from a food delivery service" is a means for automatically ordering missing ingredients from a food delivery service.

[0612] The present invention is a system for managing food in a refrigerator and providing users with recipe suggestions and management advice. This system uses the following hardware and software.

[0613] Hardware

[0614] Image capture device: A device that captures images inside the refrigerator, specifically using a Raspberry Pi camera module.

[0615] Mass measuring device: A device that measures the weight of each shelf or food item, specifically using a weight sensor such as the HX711.

[0616] Information processing device: A server that analyzes data and generates a food list.

[0617] User terminal: A device such as a smartphone or tablet through which a user receives information.

[0618] software

[0619] Image processing technology: OpenCV is used for image analysis to identify the type and location of food.

[0620] Data analysis techniques: Analyze mass measurement results and calculate food volume. Use Python's Flask framework and TensorFlow.

[0621] Backend system: A system for collecting data, analyzing it, generating lists, and suggesting recipes, using Firebase as a database.

[0622] Specific processing flow

[0623] Data collection

[0624] 1. An image capture device periodically captures images of the inside of the refrigerator, and a mass measurement device measures the weight of each food item.

[0625] 2. The collected data is sent to an information processing device (server) via Raspberry Pi.

[0626] Data analysis

[0627] 1. The server analyzes the received image data using OpenCV to identify the type and location of the food.

[0628] 2. Based on the data from the mass measuring device, calculate the weight of each food item and determine the amount of food.

[0629] 3. The server integrates this data and generates a list of the food items in the refrigerator.

[0630] Recipe suggestions and management advice

[0631] 1. Based on the food list, the server uses a generative AI model using TensorFlow to suggest suitable recipes.

[0632] 2. Identify foods that are close to expiry date or low in stock from the food inventory and generate management advice based on this.

[0633] 3. Management advice and recipe suggestions are sent to the user's device via Firebase.

[0634] Automated ordering for food delivery services

[0635] 1. Based on the recipe suggestions, identify the missing ingredients.

[0636] 2. The server automatically calls the food delivery service API and orders the necessary ingredients.

[0637] Examples of use

[0638] If you have tomatoes, onions, and cheese in your fridge, the server will suggest a tomato pasta recipe based on that information. If you're low on pasta, the server will automatically order it from a food delivery service and notify you.

[0639] Prompt Sentence Examples

[0640] "If I have tomatoes, onions, and cheese in the fridge, I want to suggest a recipe using them and order any missing ingredients using a food delivery service API. What specific program would you use and what framework would you use?"

[0641] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0642] Step 1:

[0643] Data inside the refrigerator is collected. An image capture device (Raspberry Pi camera module) periodically captures images inside the refrigerator, and a mass measurement device (HX711) measures the weight of each food item. The input is real-time images inside the refrigerator and the weight data of each food item, and the output is a set of these data.

[0644] Step 2:

[0645] The collected data is sent to an information processing device (server). The Raspberry Pi sends image data and weight data to the information processing device. The input is the image and weight data acquired inside the refrigerator, and the output is the data sent to the server.

[0646] Step 3:

[0647] The server analyzes the received data. First, it performs image analysis on the image data using OpenCV to identify the type and location of the food. Next, it calculates the weight of each food item based on the weight data. The input is the transmitted image data and weight data, and the output is a list of the analyzed food types, locations, and weights.

[0648] Step 4:

[0649] A food list is generated based on the analyzed data. The server integrates the identified food information and generates a list of the food in the refrigerator. The input is a list of food types, locations, and weights, and the output is the food list.

[0650] Step 5:

[0651] Recipes are suggested based on a food list. The server uses a generative AI model using TensorFlow to search for and suggest appropriate recipes from a food list. The input is a food list, and the output is a suggested recipe.

[0652] Step 6:

[0653] Generate management advice. The server identifies foods that are close to their expiration date or low in stock from the food list, and generates management advice based on that. The input is the food list, and the output is management advice.

[0654] Step 7:

[0655] Suggested recipes and management advice are sent to the user's device. The server sends the suggested recipes and management advice to the user's smartphone via Firebase. The input is the suggested recipes and management advice, and the output is a notification sent to the user's device.

[0656] Step 8:

[0657] Automatically orders missing ingredients. The server identifies missing ingredients based on the proposed recipe and calls the food delivery service's API to automatically place the order. The input is the proposed recipe and a list of foods, and the output is the order data sent to the food delivery service.

[0658] Specific operations at each step include, for example, feature extraction in image analysis, calibration of weight data, and consideration of user preferences when proposing recipes.

[0659] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0660] Below, we will explain an embodiment of the present invention that combines a user emotion engine. The present invention combines an emotion engine that recognizes the user's emotions with a system that monitors the inside of a refrigerator, grasps the status of ingredients, and provides the user with appropriate recipe suggestions and management advice. This system consists of a camera and sensor installed in the refrigerator, a server, a user's smartphone application, and an emotion engine.

[0661] 1. Refrigerator monitoring

[0662] First, a camera and weight sensor are installed in the refrigerator. The camera periodically captures images of the inside of the refrigerator, and the weight sensor measures the weight of each shelf and food item. This allows basic data to be collected about where and how much food is in the refrigerator.

[0663] 2. Data collection and transmission

[0664] The terminal (refrigerator) sends the collected image data and weight data to the server. The input data includes information about the type and amount of ingredients.

[0665] 3. Data Analysis

[0666] The server analyzes the received image data using image analysis technology. This allows it to recognize each ingredient in the refrigerator and identify its type and location. It also calculates the amount of each ingredient from the weight data. For example, if the server analyzes an image of a tomato and the weight sensor provides the tomato's weight, the server can determine how many tomatoes are left in the refrigerator.

[0667] Based on the recognized ingredients and their quantities, the server generates a list of the food items in the refrigerator, which includes all the ingredients and their respective quantities.

[0668] 4. Recipe suggestions and emotional engine

[0669] The server searches for available recipes based on the generated food list, taking into account the user's food preferences, nutritional balance, allergy information, and even the user's current emotional state using an emotion engine.

[0670] The emotion engine evaluates the user's emotional state through facial and voice analysis. For example, if the user is very tired, easy-to-make recipes will be prioritized.

[0671] The server uses the data obtained from the emotion engine to adjust the difficulty and genre of the recipes and provide recipes that best suit the user's emotions and situation. For example, if the user is tired, simple recipes with short cooking times will be suggested, while if the user is in a happy mood, special dishes that require a little more effort will be suggested.

[0672] 5. Management advice and emotional engine

[0673] The server also identifies items in the food list that are about to expire or are low in stock, such as yogurt that is expiring the next day or milk that is low in stock.

[0674] The emotion engine adjusts the priority of management advice based on the user's emotions: for example, if the user is feeling stressed, it will highlight foods that need to be consumed immediately and encourage positive behavior.

[0675] Based on this information, the server generates advice on food consumption priorities and replenishment, allowing users to efficiently manage the ingredients in their refrigerators.

[0676] 6. Notice and Information

[0677] The device (smartphone application) receives suggested recipes and management advice from the server and notifies the user. The notification content includes today's recipe suggestions and consumption / purchase advice.

[0678] The app also provides a feature that allows users to check the status of their refrigerators while they are out. For example, if a user opens the app while shopping at a supermarket, they can check in real time what ingredients are in the refrigerator and what they need.

[0679] Specific examples

[0680] For example, if you have tomatoes, onions, and cheese in your refrigerator, the server will suggest a recipe for tomato pasta based on these ingredients, advise you to use tomatoes that are close to their expiration date soon, and notify you to buy more milk if you are running low on it.

[0681] Furthermore, if the user comes home tired, the emotion engine will recognize the user's emotional state and suggest an easy-to-make tomato salad recipe, allowing the user to prepare dinner without any stress.

[0682] The above is an embodiment of the present invention. In this way, it is possible to efficiently manage ingredients in a refrigerator, reduce waste, and provide flexible recipe suggestions and management advice that correspond to the user's emotional state.

[0683] The processing flow will be explained below.

[0684] Step 1:

[0685] The terminal (refrigerator) captures image data using a camera inside the refrigerator, and a weight sensor measures the weight data of each shelf and food item.

[0686] Step 2:

[0687] The terminal (refrigerator) sends the captured image data and weight data to the server.

[0688] Step 3:

[0689] The server uses image analysis technology to analyze the received image data and recognize each food item in the refrigerator. For example, tomatoes and onions can be identified using image analysis technology.

[0690] Step 4:

[0691] The server analyzes the weight data and calculates the amount of each recognized ingredient. For example, based on the weight sensor data, it determines that there are three tomatoes and two onions.

[0692] Step 5:

[0693] The server generates a food list based on the recognized types and quantities of ingredients, which records all ingredients in the refrigerator and their quantities.

[0694] Step 6:

[0695] The server searches the database for available recipes based on the food list, taking into account the user's food preferences, nutritional balance, and input from the emotion engine.

[0696] Step 7:

[0697] The emotion engine recognizes the user's emotional state, for example, by analyzing facial expressions and voice to determine whether the user is tired or happy.

[0698] Step 8:

[0699] The server adjusts the difficulty and genre of the recipes based on the emotion engine data and selects recipes that are suitable for the user. For example, if the user is tired, it will suggest easy recipes.

[0700] Step 9:

[0701] The server analyzes the food list and identifies items that are nearing expiration or low in stock, for example, detecting yogurt that is about to expire or milk that is running low.

[0702] Step 10:

[0703] An emotion engine adjusts the priority of management advice based on the user's emotions, for example highlighting only important items if the user is busy.

[0704] Step 11:

[0705] The server generates management advice based on food items that are close to expiry or low in stock, such as "consume yogurt quickly" or "purchase more milk."

[0706] Step 12:

[0707] The device (smartphone application) receives suggested recipes and management advice from the server and notifies the user. For example, it sends a notification such as "Today's Easy Recipe: Tomato Salad."

[0708] Step 13:

[0709] Users can check the status of their refrigerators while they are out using a smartphone app. For example, they can open the app while shopping and check the ingredients in their refrigerators and their expiration dates.

[0710] Step 14:

[0711] The app consumes and replenishes ingredients based on the recipes and management advice users receive, and with the assistance of an emotion engine, prepares dishes that suit the user's mood that day.

[0712] Example 2

[0713] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0714] Currently, there are systems on the market that aim to manage ingredients in a refrigerator and suggest recipes, but these systems do not take into account the user's emotional state, making it difficult to provide appropriate recipe suggestions or management advice when the user is tired or under stress. Furthermore, they lack the functionality to check the refrigerator status in real time while away from home, making efficient ingredient management impossible. Furthermore, even if they can identify foods that are close to their expiration date or low in stock, they lack a way to appropriately notify the user of this information based on their emotional state.

[0715] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes: means for collecting data using a camera and sensors installed in the refrigerator; means for transmitting the collected data to the server; means for analyzing the collected data using image analysis technology and generating a list of foods in the refrigerator; means for suggesting available recipes based on the generated food list; means for identifying foods from the food list that are close to their expiration date or low in stock and generating management advice; means for notifying the user of the suggested recipes and management advice; means for allowing the user to access the food ingredient data in the refrigerator; means for providing an emotion engine for analyzing the user's emotional state; means for adjusting the difficulty level and genre of recipes based on data obtained from the emotion engine and providing optimal recipes; means for adjusting the priority of management advice based on the user's emotional state; and means for the user to check the status of the refrigerator while away from home. This enables flexible recipe suggestions and management advice to be provided taking the user's emotional state into consideration, thereby achieving efficient food ingredient management and appropriate information notification.

[0716] The "camera" is a photographic device for capturing images inside the refrigerator.

[0717] A "sensor" is a device that measures physical data such as the weight and position of food items in a refrigerator.

[0718] The "means of collecting data" refers to a system that uses cameras and sensors to obtain information about the inside of the refrigerator.

[0719] A "server" is a computer system that receives, stores, analyzes, and processes data.

[0720] "Image analysis technology" is a technology that processes collected image data to identify the type and location of ingredients.

[0721] A "food list" is data that records the types and amounts of food ingredients present in a refrigerator.

[0722] The "means for suggesting recipes" is a mechanism that provides users with recipe suggestions based on a food list.

[0723] "Management advice" is advice on how to handle or replenish food that is nearing its expiration date or is low in stock.

[0724] The "notification means" is a communication means for conveying suggested recipes and management advice to the user.

[0725] An "emotion engine" is software and algorithms for analyzing a user's emotional state.

[0726] The "means for checking the status of the refrigerator" is a system that allows the user to view information about ingredients in the refrigerator even when they are away from home.

[0727] "Means for analyzing emotional state" refers to technology that recognizes emotions from the user's facial expressions, voice, etc.

[0728] "Means for adjusting the difficulty and genre of recipes" refers to a mechanism that appropriately changes the content of suggested recipes based on data obtained from the emotion engine.

[0729] The "means for adjusting priority" is a mechanism for changing the importance and notification order of management advice depending on the emotional state of the user.

[0730] This invention combines a system that monitors the inside of a refrigerator, grasps the status of ingredients, and provides users with appropriate recipe suggestions and management advice, with an emotion engine that recognizes the user's emotions. The system consists of a camera and sensors installed in the refrigerator, a server, a user's smartphone application, and the emotion engine.

[0731] First, a camera and weight sensor are installed inside the device (refrigerator). The camera periodically captures images of the inside of the refrigerator, and the weight sensor measures the weight of each shelf and food item. This allows data to be collected about where and how much food is in the refrigerator. For example, by capturing an image of tomatoes placed on a shelf in the refrigerator and measuring their weight, the location and quantity of tomatoes can be determined.

[0732] Next, the collected image data and weight data are sent from the terminal (refrigerator) to a server. The server receives the data and stores it in a database. Specifically, the image data is stored in storage, and the weight data is associated with each ingredient. A high-speed database management system is recommended for this process.

[0733] The server analyzes the received data using image analysis technology. For example, it uses image recognition libraries such as OpenCV and TensorFlow to identify the type and location of each ingredient. It also calculates the quantity of each ingredient based on the weight data and generates a list of the food in the refrigerator. For example, from an analyzed image of a tomato, it recognizes that the ingredient is a "tomato," and combines this with the weight data to determine how many tomatoes are in the refrigerator.

[0734] The server then searches for available recipes from a database based on the generated food list, taking into account the user's food preferences, nutritional balance, and allergy information. This can be done using a database management system such as MySQL, which can reference the user's profile information to extract filtered recipes.

[0735] The emotion engine evaluates the user's emotional state through facial and voice analysis. For example, it utilizes Microsoft Azure's Face API and Google Cloud's Speech-to-Text service. Based on the emotional data obtained from the emotion engine, the server adjusts the difficulty and genre of the recipe and provides the most suitable recipe to the user. Specifically, if the user is very tired, it will suggest recipes with short cooking times, and if the user wants to enjoy cooking, it will suggest dishes that require a little more effort.

[0736] The server also identifies foods from the food list that are close to expiry or low in stock and generates management advice based on that. For example, a Python script can scan the food list, extract foods that fit certain criteria (such as yogurt that is close to expiry or milk that is low in stock), and generate management advice for them.

[0737] The device (smartphone application) then receives suggested recipes and management advice from the server and notifies the user. Specifically, push notifications are sent using Firebase Cloud Messaging, and information is delivered to the user's smartphone in real time. A function is also provided that allows users to check the status of their refrigerator while they are out. For example, if you open the app while shopping at the supermarket, you can find out in real time what ingredients are in your refrigerator and what is missing.

[0738] Specific examples

[0739] For example, if the user has tomatoes, onions, and cheese in the refrigerator, the server will suggest a tomato pasta recipe based on these ingredients. It will also advise the user to use tomatoes that are close to their expiration date soon and notify the user to purchase more milk if they are running low on stock. Furthermore, if the user comes home tired, the emotion engine will recognize the user's emotional state and suggest an easy tomato salad recipe, allowing the user to prepare dinner without any stress.

[0740] Prompt Sentence Examples

[0741] An example of a prompt for a generative AI model is:

[0742] "Explain in detail the refrigerator food management system that leverages the user's emotion engine, specifically each step from food monitoring to data analysis, recipe suggestions, management advice and notifications."

[0743] This concludes the description of the specific embodiment for carrying out the invention. By using this system, users can efficiently manage ingredients in their refrigerators, reduce waste, and receive flexible recipe suggestions and management advice that reflect their emotional state.

[0744] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0745] Step 1: Data collection

[0746] The terminal (refrigerator) collects food ingredient data using a camera and weight sensor installed inside. The camera periodically takes pictures of the inside of the refrigerator, and the weight sensor measures the weight of each shelf and food item. This makes it possible to obtain data on how much food is in which location inside the refrigerator. The input data is image data and weight data inside the refrigerator, and the output is information on the location and amount of food ingredients.

[0747] Step 2: Data transmission and storage

[0748] The terminal (refrigerator) sends the collected image data and weight data to the server. The server receives this data and stores it in a database. Specifically, the data is transferred via a high-speed network connection, and the data received by the server is stored in storage. The input data is the image data and weight data sent from the terminal, and the output is the raw data stored in the server's database.

[0749] Step 3: Image analysis and ingredient list generation

[0750] The server uses image analysis technology to analyze the received image data. For example, it uses OpenCV or TensorFlow to identify each ingredient and recognize its location and type. At the same time, it calculates the quantity of each ingredient from the weight data and generates a list of the food in the refrigerator. The input data is the saved image data and weight data, and the output is a list of recognized ingredients. Specifically, it recognizes tomatoes from the image and, in combination with the weight data, identifies the number of tomatoes.

[0751] Step 4: Recipe search and suggestions

[0752] The server searches for suitable recipes from a recipe database based on the generated food list, taking into account the user's food preferences, nutritional balance, and allergy information. Specifically, it uses MySQL or a similar database management system to extract filtered recipes based on the user's profile and food list. The input data is the generated food list and the user's profile information, and the output is suggested recipes.

[0753] Step 5: Leverage the Emotion Engine

[0754] The server optimizes recipe suggestions using data obtained from the user's emotion engine. Using Microsoft Azure's Face API and Google Cloud's Speech-to-Text service, the server analyzes the user's emotional state from their facial expressions and voice, and receives this data. The input data is the user's facial expression analysis data and voice analysis data, and the output is recipe suggestions that take their emotional state into account. Specifically, if the user is tired, it selects easy-to-make recipes.

[0755] Step 6: Generate management advice

[0756] The server identifies foods from the food list that are close to their expiration date or low in stock, and generates management advice based on that. A Python script scans the food list and extracts foods that meet certain criteria (e.g., expiration date within two days, stock of one unit or less). The input data is the generated food list, and the output is management advice. Specifically, it sends a notification to "consume soon" for yogurt that is close to its expiration date.

[0757] Step 7: Notification and Information

[0758] The device (smartphone application) receives suggested recipes and management advice from the server and notifies the user. Push notifications are sent using Firebase Cloud Messaging and delivered to the user's smartphone. In addition, a function is provided that allows users to check the status of their refrigerator while they are out. The input data is the recipe suggestions and management advice sent from the server, and the output is notifications and information displayed on the user's smartphone. In concrete terms, when a user opens the app while they are out, a list of ingredients in the refrigerator and a shopping list for necessary items are displayed.

[0759] The above is the specific processing flow and operation at each step.

[0760] (Application example 2)

[0761] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0762] While conventional refrigerator management systems could manage food inventory and expiration dates, they were unable to provide flexible recipe suggestions or management advice that took into account the user's current emotional state. As a result, even when users were stressed or tired, they were unable to obtain appropriate information, leading to problems such as overcooking or wasting ingredients. Furthermore, the system lacked the functionality to check the refrigerator status while away from home, which caused the inconvenience of not being able to determine what ingredients were needed when shopping.

[0763] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0764] In this invention, the server includes means for collecting data using cameras and sensors installed inside the refrigerator, means for transmitting the collected data to the server, means for the server to analyze the collected data using image analysis technology and generate a list of foods in the refrigerator, means for suggesting available recipes based on the generated food list, means for identifying foods from the food list that are close to their expiration date or that are low in stock and generating management advice, means for notifying the user of the suggested recipes and management advice, means for the user to check the status of the refrigerator while away from home, and means for recognizing the user's emotions and adjusting the recipe suggestions and management advice based on the user's emotional state. This enables flexible and appropriate recipe suggestions and management advice to be provided in accordance with the user's emotional state.

[0765] The camera installed inside the refrigerator is an image capturing device that periodically captures the status of the food ingredients inside the refrigerator.

[0766] The sensor is a device used to measure the weight and temperature of food in the refrigerator.

[0767] The means of collection is a method for obtaining data from cameras and sensors installed inside the refrigerator.

[0768] The means for transmitting is a communication technique for sending the collected data to the server.

[0769] Image analysis technology is a technology that analyzes collected image data and extracts useful information from photos and videos.

[0770] The means for generating a food list is a method for listing the types and amounts of ingredients in the refrigerator based on the analyzed data.

[0771] A recipe suggestion method is a method for presenting available dishes to a user based on a food list.

[0772] The means for generating management advice is a means for instructing the user on the optimal way to use ingredients and replenish them based on expiration dates and stock status.

[0773] Notification means are methods for informing users of suggested recipes and management advice.

[0774] The means for checking from outside the home is a technology that allows a user who is out to remotely check the status of the refrigerator.

[0775] Emotion recognition is a technology that analyzes a user's facial expressions and voice to determine their emotional state at that time.

[0776] The means for adjusting based on the emotional state is a method for changing the content of recipe suggestions and management advice depending on the user's emotions.

[0777] Below, we will explain an embodiment of the present invention that combines a user emotion engine. The present invention combines an emotion engine that recognizes the user's emotions with a system that monitors the inside of a refrigerator, grasps the status of ingredients, and provides the user with appropriate recipe suggestions and management advice. This system consists of a camera and sensor installed in the refrigerator, a server, a user's smartphone application, and an emotion engine.

[0778] 1. Refrigerator monitoring

[0779] First, a camera and a weight sensor are installed in the refrigerator. The camera periodically captures images of the inside of the refrigerator, and the weight sensor measures the weight of each shelf and food item. This data is collected as basic data on where and how much food is in the refrigerator.

[0780] 2. Data collection and transmission

[0781] The refrigerator (terminal) sends the collected image data and weight data to a server. The data is then sent to the server via the internet via a communication module installed in the terminal. The sent data includes information about the type of ingredients and their amounts.

[0782] 3. Data Analysis

[0783] The server uses image analysis technology to analyze the received image data. This allows it to recognize each ingredient in the refrigerator and identify its type and location. It then uses data obtained from the weight sensor to calculate the quantity of each ingredient. For example, it analyzes an image of a tomato and determines how many tomatoes are in the refrigerator based on the weight data. The server then generates a list of the food in the refrigerator based on the recognized ingredients and their quantities. This list records all the types of ingredients and their respective quantities.

[0784] 4. Recipe suggestions and emotional engine

[0785] The server searches for available recipes based on the generated food list. It takes into account the user's food preferences, nutritional balance, and allergy information, as well as their current emotional state using an emotion engine. The emotion engine evaluates the user's emotional state through facial expression and voice analysis. For example, if the user is very tired, easy-to-make recipes will be prioritized.

[0786] The system analyzes the user's facial expressions using a specially trained emotion recognition model. For example, it captures the user's facial expressions using a camera on a smartphone or smart glasses and applies an emotion recognition algorithm. Based on this, the server adjusts the difficulty and genre of the recipe to provide the most appropriate recipe for the emotion and situation. For example, if the user is tired, it will suggest a simple recipe such as tomato salad.

[0787] 5. Management advice and emotional engine

[0788] The server also identifies foods from the food list that are close to expiry or low in stock. The emotion engine adjusts the priority of management advice based on the user's emotions. For example, if the user is feeling stressed, it will highlight foods that need to be consumed immediately and encourage positive action. Based on this information, the server generates food consumption priorities and replenishment advice.

[0789] 6. Notice and Information

[0790] The refrigerator (device) notifies the user of recipe suggestions and management advice from the server. The notification content includes today's recipe suggestions and consumption / purchase advice via a smartphone application. It also provides a function that allows users to check the status of their refrigerator while they are out. For example, if a user opens the application while shopping at a supermarket, they can check in real time what ingredients are in the refrigerator and what they need.

[0791] Specific examples

[0792] For example, if the user has tomatoes, onions, and cheese in the refrigerator, the server will suggest a tomato pasta recipe based on these ingredients. It will also advise the user to use tomatoes that are close to their expiration date soon and notify the user to purchase more milk if they are running low on stock. Furthermore, if the user comes home tired, the emotion engine will recognize the user's emotional state and suggest an easy tomato salad recipe, allowing the user to prepare dinner without any stress.

[0793] Prompt Sentence Examples

[0794] "There are tomatoes and cheese in the fridge. The user seems tired. Can you suggest a simple recipe?"

[0795] The above is an embodiment of the present invention. In this way, it is possible to efficiently manage ingredients in a refrigerator, reduce waste, and provide flexible recipe suggestions and management advice that correspond to the user's emotional state.

[0796] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0797] Step 1:

[0798] Refrigerator status monitoring

[0799] Input: Data from cameras and sensors installed inside the refrigerator

[0800] Specific operation: The device periodically captures images of the inside of the refrigerator using a camera and obtains shelf and food weight data from a weight sensor.

[0801] Output: Image data and weight data

[0802] Step 2:

[0803] Data transmission

[0804] Input: Acquired image data and weight data

[0805] Specific operation: The device sends the collected data to a server via the Internet. This is done using a communication module.

[0806] Output: Image data and weight data sent to the server

[0807] Step 3:

[0808] Data analysis

[0809] Input: Image data and weight data sent to the server

[0810] Specific operation: The server uses image analysis technology to analyze the image data and identify the type and location of ingredients. It also analyzes the weight data to calculate the amount of each ingredient. For example, it recognizes tomatoes in the image data and determines how many tomatoes there are using the weight data.

[0811] Output: Food list (including ingredient types and quantities)

[0812] Step 4:

[0813] Recipe suggestions

[0814] Input: Food list, user's food preferences, nutritional balance, allergy information

[0815] Specific behavior: The server searches for available recipes based on the food list and the user's personal information. For example, if there are tomatoes and cheese, it may suggest a recipe for tomato pasta.

[0816] Output: A list of suggested recipes

[0817] Step 5:

[0818] emotion recognition

[0819] Input: User facial expression image or voice data

[0820] Specific operation: When a user uses a smartphone or smart glasses, the device's camera and microphone are used to capture facial expressions and voice in real time, and the captured data is analyzed using an emotion recognition model (generative AI model). For example, a tired facial expression is captured using a smartphone camera, and the model determines that the user is "tired."

[0821] Output: Determined emotional state

[0822] Step 6:

[0823] Tailoring recipe suggestions based on emotional state

[0824] Input: Determined emotional state, suggested recipe list

[0825] Specific operation: The server selects the most suitable recipe from the list depending on the user's emotional state. For example, if the user is tired, it will prioritize recipes that are easy to cook and require short cooking times.

[0826] Output: The recipe that best suits your emotional state

[0827] Step 7:

[0828] Generate management advice

[0829] Input: Food list, expiration date, inventory information

[0830] Specific behavior: The server identifies foods from the food list that are close to expiry or low in stock, and generates management advice for them. For example, it notifies the user to use tomatoes that are close to expiry or milk that is low in stock sooner or to replenish them.

[0831] Output: Management advice list

[0832] Step 8:

[0833] Notification and Information

[0834] Input: Recipes best suited to your emotional state, management advice list

[0835] Specific operation: The device (smartphone application) notifies the user of recipes and management advice that best suit their emotional state. It also provides a function that allows users to check the status of their refrigerator in real time. For example, while shopping at the supermarket, the user can check the refrigerator inventory on the app.

[0836] Output: Notifications to the user (recipe suggestions, management advice, refrigerator inventory information)

[0837] 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.

[0838] 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> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. 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 voice, text data indicating text, and image data indicating an image is also input. 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.

[0839] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0840] [Third embodiment]

[0841] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0842] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0843] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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).

[0844] 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.

[0845] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0846] 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 surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0847] 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.

[0848] 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.

[0849] 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 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.

[0850] 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.

[0851] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0852] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0853] The following describes an embodiment of the present invention. The present invention is a system that monitors the inside of a refrigerator, grasps the status of ingredients, and provides appropriate recipe suggestions and management advice to the user. This system consists of a camera and sensor installed in the refrigerator, a server, and a smartphone application on the user's smartphone.

[0854] 1. Refrigerator monitoring

[0855] First, a camera and weight sensor are installed in the refrigerator. The camera periodically captures images of the inside of the refrigerator, and the weight sensor measures the weight of each shelf and food item. This allows basic data to be collected about where and how much food is in the refrigerator.

[0856] 2. Data collection and transmission

[0857] The terminal (refrigerator) sends the collected image data and weight data to the server. The input data includes information about the type and amount of ingredients.

[0858] 3. Data Analysis

[0859] The server analyzes the received image data using image analysis technology. This allows it to recognize each ingredient in the refrigerator and identify its type and location. It also calculates the amount of each ingredient from the weight data. For example, if the server analyzes an image of a tomato and the weight sensor provides the tomato's weight, the server can determine how many tomatoes are left in the refrigerator.

[0860] Based on the recognized ingredients and their quantities, the server generates a list of the food items in the refrigerator, which includes all the ingredients and their respective quantities.

[0861] 4. Recipe suggestions

[0862] The server searches for available recipes based on the generated food list. Recipe suggestions take into account the user's food preferences, allergy information, and nutritional balance. For example, if the user is vegetarian, meat-free recipes will be suggested first.

[0863] The server filters the possible recipes and selects the appropriate recipe for the user, allowing them to create a dish that makes the most of the ingredients in their refrigerator.

[0864] 5. Management Advice

[0865] The server also identifies items in the food list that are close to expiry or low in stock, such as yogurt that is expiring the next day or milk that is low in stock, and uses this information to generate food consumption priorities and replenishment advice.

[0866] 6. Notice and Information

[0867] The terminal (smartphone application) receives suggested recipes and management advice from the server and notifies the user. The user can then check the suggested recipes and ingredients to purchase via their smartphone.

[0868] The app also provides a feature that allows users to check the status of their refrigerators while they are out. For example, if a user opens the app while shopping at a supermarket, they can check in real time what ingredients are in the refrigerator and what they need.

[0869] Specific examples

[0870] For example, if you have tomatoes, onions, and cheese in your refrigerator, the server will suggest a recipe for tomato pasta based on these ingredients, advise you to use tomatoes that are close to their expiration date soon, and notify you to buy more milk if you are running low on it.

[0871] The above is an embodiment of the present invention. In this way, it is possible to efficiently manage food ingredients in a refrigerator, reduce waste, and support the user's eating habits.

[0872] The processing flow will be explained below.

[0873] Step 1:

[0874] The terminal (refrigerator) captures image data using a camera inside the refrigerator, and a weight sensor measures the weight data of each shelf and food item.

[0875] Step 2:

[0876] The terminal (refrigerator) sends the captured image data and weight data to the server.

[0877] Step 3:

[0878] The server uses image analysis technology to analyze the received image data and recognize each food item in the refrigerator. For example, tomatoes and onions can be identified using image analysis technology.

[0879] Step 4:

[0880] The server analyzes the weight data and calculates the amount of each recognized ingredient. For example, based on the weight sensor data, it determines that there are three tomatoes and two onions.

[0881] Step 5:

[0882] The server generates a food list based on the recognized types and quantities of ingredients, which records all ingredients in the refrigerator and their quantities.

[0883] Step 6:

[0884] The server searches the database for available recipes based on the food list, taking into account the user's food preferences, nutritional balance, and allergy information.

[0885] Step 7:

[0886] The server filters the searched recipes and selects recipes that are suitable for the user, for example, if the user is vegetarian, meat-free recipes are selected.

[0887] Step 8:

[0888] The server analyzes the food list and identifies items that are nearing expiration or low in stock, for example, detecting yogurt that is about to expire or milk that is running low.

[0889] Step 9:

[0890] The server generates management advice based on food items that are nearing expiration or low in stock, such as "consume the yogurt quickly" or "buy more milk."

[0891] Step 10:

[0892] The device (smartphone application) receives recipe suggestions and management advice from the server and notifies the user. The notification content includes today's recipe suggestions and consumption / purchase advice.

[0893] Step 11:

[0894] Users can check the status of their refrigerators while they are out using a smartphone app. For example, while shopping, users can check what is left in the refrigerator in real time and purchase the ingredients they need.

[0895] Example 1

[0896] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0897] Managing food in the refrigerator manually is extremely time-consuming and prone to oversight, resulting in food waste. It is also difficult for users to accurately grasp food inventory and expiration dates, which often prevents them from making appropriate cooking plans. Furthermore, it is difficult for users to check the status of their refrigerators while away from home, which increases the risk of purchasing duplicate ingredients when shopping. To solve these issues, there is a need for an efficient, automated food management and cooking suggestion system.

[0898] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0899] In this invention, the server includes means for collecting data using a photographing device and a weight measuring device installed in the refrigerator, means for transmitting the collected data to an information processing device, means for the information processing device to analyze the collected data using image analysis technology and generate an ingredient list for the refrigerator, means for suggesting available recipes based on the generated ingredient list, means for identifying ingredients from the ingredient list that are close to their expiration date or that are low in stock and generating management advice, means for notifying the user of the suggested recipes and management advice, and means for the user to check the status of the refrigerator while away from home. This automatically manages the ingredients in the refrigerator and allows the user to obtain accurate ingredient information, enabling efficient ingredient use and reduced waste.

[0900] "Photography Device" means a camera or other photographic device installed to capture images inside the refrigerator.

[0901] "Weight measuring device" refers to a sensor or weighing device installed to measure the weight of food on each shelf or in a refrigerator.

[0902] An "information processing device" is a computing device such as a computer or server that analyzes collected data and generates various instructions.

[0903] "Image analysis technology" refers to algorithms and software that analyze captured image data and recognize specific objects or conditions.

[0904] The "ingredient list" is a data list that compiles information such as the type, quantity, and location of ingredients in the refrigerator.

[0905] "Cooking method" refers to specific steps or recipes that a user uses to cook food using ingredients.

[0906] "Management advice" refers to advice on consumption priorities and replenishment provided to the user based on the expiration dates and stock status of food ingredients in the refrigerator.

[0907] "Notification means" refers to a smartphone application or push notification system that notifies users of information and suggestions from the server.

[0908] "Means for checking from outside the home" refers to functions and services that allow users to remotely check the status of the refrigerator.

[0909] "Store deals" refers to promotional information such as price discounts and special sales offered by nearby retail stores.

[0910] MODE FOR CARRYING OUT THE INVENTION

[0911] The following describes in detail an embodiment of the present invention. The present invention is a system that monitors the inside of a refrigerator, grasps the status of ingredients, and provides appropriate recipe suggestions and management advice to the user. This system is composed of a photographing device and a weight measuring device installed in the refrigerator, an information processing device, and a smartphone application for the user.

[0912] System Hardware and Software

[0913] Hardware

[0914] Image capture device: A camera (e.g., a standard digital camera) that captures images of the inside of the refrigerator.

[0915] Weight measuring device: A sensor (e.g., a digital scale) that measures the weight of each shelf or item in the refrigerator.

[0916] Information processing device: A computer or server (e.g., a virtual server on the cloud) that analyzes data and generates various instructions.

[0917] User device: Smartphone, tablet, etc.

[0918] software

[0919] Image analysis software: Algorithms for analyzing image data and recognizing ingredients in the refrigerator (e.g., OpenCV).

[0920] Data analysis software: Software for analyzing weight data (e.g., TensorFlow).

[0921] Smartphone application: An app to send notifications to the user and display the status of the refrigerator (e.g. iOS, Android app).

[0922] Data collection and analysis

[0923] Terminal (refrigerator)

[0924] A camera installed inside the refrigerator periodically captures images of the interior of the refrigerator. For example, the camera takes a picture of the interior of the refrigerator at 8:00 AM every day. In addition, weight sensors installed on each shelf measure the weight of ingredients in real time. This allows data to be collected about what ingredients are in which position in the refrigerator and how much of each ingredient is in each position.

[0925] server

[0926] The information processing device (server) receives the image data and weight data sent from the refrigerator and analyzes them using image analysis technology. It uses OpenCV to recognize ingredients from the image and TensorFlow to analyze the weight data, thereby identifying the type and quantity of each ingredient. For example, it analyzes an image of tomatoes and calculates the number of tomatoes from the weight data. Based on this, a list of ingredients in the refrigerator is generated.

[0927] Recipe suggestions and management advice

[0928] server

[0929] Based on the generated ingredient list, the server suggests recipes that take into account the user's food preferences and nutritional balance. For example, if the user is vegetarian, meat-free recipes will be suggested. It also identifies ingredients that are close to their expiration date or are low in stock, and generates advice on consumption priorities and replenishment. For example, it generates advice on yogurt whose expiration date is approaching the next day or milk that needs to be replenished.

[0930] Notifications and on-the-go visibility

[0931] Terminal (smartphone application)

[0932] The user's smartphone application receives notifications of recipe suggestions and management advice sent from the server. The user can check the suggested recipes and ingredients to purchase through their smartphone. The user can also check the status of their refrigerator in real time while they are out. For example, if a user opens the app while shopping at a supermarket, they can check the status of ingredients in their refrigerator and make a list of what they need.

[0933] Examples of concrete examples and prompts

[0934] Specific examples

[0935] For example, if there are tomatoes, onions, and cheese in the refrigerator, the information processing device will suggest a recipe for tomato pasta based on these ingredients. It will also advise the user to use tomatoes that are close to their expiration date as soon as possible, and notify the user to purchase more milk if their stock is low.

[0936] Prompt Sentence Examples

[0937] "Please explain how the system works to suggest recipes using ingredients available in the refrigerator."

[0938] "Please tell me the detailed method for monitoring the food in the refrigerator and analyzing the data."

[0939] The above is an embodiment of the present invention. By using this system, it is possible to efficiently manage food ingredients in the refrigerator, reduce waste, and support the user's eating habits.

[0940] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0941] Step 1: Collecting data from inside the refrigerator

[0942] Specific explanation

[0943] The terminal (refrigerator) collects data using a camera and a weighing device installed inside the refrigerator. The camera periodically captures images of the interior of the refrigerator, and the weighing device measures the weight of each shelf and food item.

[0944] Input and Output

[0945] Input: Image data from the imaging device, weight data from the weight measuring device

[0946] Data processing: image capture, weight measurement

[0947] Output: Image data and weight data of the inside of the refrigerator

[0948] Specific actions

[0949] The camera captures images at regular intervals, adjusting the shutter speed, and the weighing device synchronizes to generate a digital signal to record the weight of each ingredient.

[0950] Step 2: Sending data

[0951] Specific explanation

[0952] The device (refrigerator) sends the collected image and weight data to a server over the Internet. The data is encrypted and transmitted using a secure protocol (e.g., HTTPS).

[0953] Input and Output

[0954] Input: Image data of the inside of the refrigerator, weight data

[0955] Data calculation: Encrypting data and converting it into a secure protocol

[0956] Output: Image data and weight data sent to the server

[0957] Specific actions

[0958] The refrigerator's built-in modem sends the data to the server's API endpoint, and once the transmission is complete, the server responds with a response acknowledging receipt of the data.

[0959] Step 3: Image and data analysis

[0960] Specific explanation

[0961] The server analyzes the received image data and weight data. It uses image analysis technology (e.g., OpenCV) to recognize ingredients from the image and weight analysis software (e.g., TensorFlow) to analyze the weight data. This identifies the type and amount of each ingredient.

[0962] Input and Output

[0963] Input: Image data and weight data sent to the server

[0964] Data calculation: Ingredient recognition (image analysis), weight estimation (weight data analysis)

[0965] Output: Analyzed ingredient list (type, amount, location)

[0966] Specific actions

[0967] OpenCV identifies ingredients such as tomatoes, cheese, and onions from the image, and TensorFlow estimates the quantity of each ingredient based on the weight data. The analysis results are then integrated to generate a detailed list of ingredients.

[0968] Step 4: Generate a food list

[0969] Specific explanation

[0970] The server then generates a list of the food items in the refrigerator based on the analyzed data, including the type of food, the amount, and the location of each food item in the refrigerator.

[0971] Input and Output

[0972] Input: Parsed ingredient list

[0973] Data processing: data integration and classification

[0974] Output: Final food list

[0975] Specific actions

[0976] The server stores the list of ingredients in a database and updates the increase or decrease in ingredients by comparing it with past consumption data.

[0977] Step 5: Recipe suggestions

[0978] Specific explanation

[0979] The server searches for available recipes based on the generated food list, and filters suitable recipes based on the user's preferences and nutritional balance.

[0980] Input and Output

[0981] Input: Final food list, user's food preferences and nutritional information

[0982] Data calculation: recipe search, filtering

[0983] Output: Recipe suggested to the user

[0984] Specific actions

[0985] The server accesses the recipe database and searches for recipes that match the criteria, and the filtered recipes are notified to the user.

[0986] Step 6: Generate management advice

[0987] Specific explanation

[0988] The server identifies ingredients from the food list that are close to their expiration date or are low in stock, and generates consumption priorities and replenishment advice.

[0989] Input and Output

[0990] Input: Final food list

[0991] Data calculation: Expiration date confirmation, stock confirmation, advice generation

[0992] Output: Management advice

[0993] Specific actions

[0994] The server scans the food list and generates appropriate advice based on expiration dates and stock levels.

[0995] Step 7: Notify and inform

[0996] Specific explanation

[0997] The terminal (smartphone application) receives suggested recipes and management advice from the server and notifies the user, who can then check the information in real time through the app.

[0998] Input and Output

[0999] Input: Suggested recipes, management advice

[1000] Data processing: notification generation, information display

[1001] Output: User notifications and real-time information

[1002] Specific actions

[1003] The smartphone app receives the push notification and displays the latest information on the screen. The user can then operate the app to check more detailed information.

[1004] The above is a concrete explanation of the program processing of this system.

[1005] (Application example 1)

[1006] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1007] The problem is that it is not possible to efficiently manage and utilize food in the refrigerator, and that food expires or is out of stock without the user being aware of it.Furthermore, it is also an issue that users cannot immediately order the ingredients they need, making meal planning a hassle.

[1008] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1009] In this invention, the server includes means for collecting data using an image capture device and a mass measurement device installed in the refrigerator, means for transmitting the collected data to an information processing device, means for the information processing device to analyze the collected data using image processing technology and generate a list of foods in the refrigerator, means for suggesting available cooking methods based on the generated food list, means for identifying foods from the food list that are close to their expiration date or are low in stock and generating food management advice, means for notifying the user of the suggested cooking methods and food management advice, means for the user to remotely check the status of the refrigerator, and means for automatically ordering ingredients that are in short supply from a food delivery service. This enables efficient food management and use, reduces food waste, and supports the user's meal planning.

[1010] An "image capture device" is a device that captures images of the inside of a refrigerator and recognizes the type and location of food.

[1011] A "mass measuring device" is a device that measures the weight of each shelf and food item in a refrigerator to determine the amount of food.

[1012] The "information processing device" is a device that receives and analyzes the collected data and generates a food list.

[1013] "Image processing technology" is a technology that analyzes captured images to recognize the type and location of food.

[1014] A "food inventory" is a list that records all the types and quantities of food in the refrigerator.

[1015] "Cooking Instructions" are recommended cooking recipes and steps based on the food list.

[1016] "Management advice" refers to advice on consumption priorities and replenishment for food items that are close to their expiration date or that are low in stock.

[1017] The "notification means" is a means for informing the user of suggested cooking methods and management advice.

[1018] The "means for checking from a remote location" is a means by which a user can check the status of the refrigerator in real time while away from home.

[1019] The "means for automatically ordering from a food delivery service" is a means for automatically ordering missing ingredients from a food delivery service.

[1020] The present invention is a system for managing food in a refrigerator and providing users with recipe suggestions and management advice. This system uses the following hardware and software.

[1021] Hardware

[1022] Image capture device: A device that captures images inside the refrigerator, specifically using a Raspberry Pi camera module.

[1023] Mass measuring device: A device that measures the weight of each shelf or food item, specifically using a weight sensor such as the HX711.

[1024] Information processing device: A server that analyzes data and generates a food list.

[1025] User terminal: A device such as a smartphone or tablet through which a user receives information.

[1026] software

[1027] Image processing technology: OpenCV is used for image analysis to identify the type and location of food.

[1028] Data analysis techniques: Analyze mass measurement results and calculate food volume. Use Python's Flask framework and TensorFlow.

[1029] Backend system: A system for collecting data, analyzing it, generating lists, and suggesting recipes, using Firebase as a database.

[1030] Specific processing flow

[1031] Data collection

[1032] 1. An image capture device periodically captures images of the inside of the refrigerator, and a mass measurement device measures the weight of each food item.

[1033] 2. The collected data is sent to an information processing device (server) via Raspberry Pi.

[1034] Data analysis

[1035] 1. The server analyzes the received image data using OpenCV to identify the type and location of the food.

[1036] 2. Based on the data from the mass measuring device, calculate the weight of each food item and determine the amount of food.

[1037] 3. The server integrates this data and generates a list of the food items in the refrigerator.

[1038] Recipe suggestions and management advice

[1039] 1. Based on the food list, the server uses a generative AI model using TensorFlow to suggest suitable recipes.

[1040] 2. Identify foods that are close to expiry date or low in stock from the food inventory and generate management advice based on this.

[1041] 3. Management advice and recipe suggestions are sent to the user's device via Firebase.

[1042] Automated ordering for food delivery services

[1043] 1. Based on the recipe suggestions, identify the missing ingredients.

[1044] 2. The server automatically calls the food delivery service API and orders the necessary ingredients.

[1045] Examples of use

[1046] If you have tomatoes, onions, and cheese in your fridge, the server will suggest a tomato pasta recipe based on that information. If you're low on pasta, the server will automatically order it from a food delivery service and notify you.

[1047] Prompt Sentence Examples

[1048] "If I have tomatoes, onions, and cheese in the fridge, I want to suggest a recipe using them and order any missing ingredients using a food delivery service API. What specific program would you use and what framework would you use?"

[1049] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1050] Step 1:

[1051] Data inside the refrigerator is collected. An image capture device (Raspberry Pi camera module) periodically captures images inside the refrigerator, and a mass measurement device (HX711) measures the weight of each food item. The input is real-time images inside the refrigerator and the weight data of each food item, and the output is a set of these data.

[1052] Step 2:

[1053] The collected data is sent to an information processing device (server). The Raspberry Pi sends image data and weight data to the information processing device. The input is the image and weight data acquired inside the refrigerator, and the output is the data sent to the server.

[1054] Step 3:

[1055] The server analyzes the received data. First, it performs image analysis on the image data using OpenCV to identify the type and location of the food. Next, it calculates the weight of each food item based on the weight data. The input is the transmitted image data and weight data, and the output is a list of the analyzed food types, locations, and weights.

[1056] Step 4:

[1057] A food list is generated based on the analyzed data. The server integrates the identified food information and generates a list of the food in the refrigerator. The input is a list of food types, locations, and weights, and the output is the food list.

[1058] Step 5:

[1059] Recipes are suggested based on a food list. The server uses a generative AI model using TensorFlow to search for and suggest appropriate recipes from a food list. The input is a food list, and the output is a suggested recipe.

[1060] Step 6:

[1061] Generate management advice. The server identifies foods that are close to their expiration date or low in stock from the food list, and generates management advice based on that. The input is the food list, and the output is management advice.

[1062] Step 7:

[1063] Suggested recipes and management advice are sent to the user's device. The server sends the suggested recipes and management advice to the user's smartphone via Firebase. The input is the suggested recipes and management advice, and the output is a notification sent to the user's device.

[1064] Step 8:

[1065] Automatically orders missing ingredients. The server identifies missing ingredients based on the proposed recipe and calls the food delivery service's API to automatically place the order. The input is the proposed recipe and a list of foods, and the output is the order data sent to the food delivery service.

[1066] Specific operations at each step include, for example, feature extraction in image analysis, calibration of weight data, and consideration of user preferences when proposing recipes.

[1067] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1068] Below, we will explain an embodiment of the present invention that combines a user emotion engine. The present invention combines an emotion engine that recognizes the user's emotions with a system that monitors the inside of a refrigerator, grasps the status of ingredients, and provides the user with appropriate recipe suggestions and management advice. This system consists of a camera and sensor installed in the refrigerator, a server, a user's smartphone application, and an emotion engine.

[1069] 1. Refrigerator monitoring

[1070] First, a camera and weight sensor are installed in the refrigerator. The camera periodically captures images of the inside of the refrigerator, and the weight sensor measures the weight of each shelf and food item. This allows basic data to be collected about where and how much food is in the refrigerator.

[1071] 2. Data collection and transmission

[1072] The terminal (refrigerator) sends the collected image data and weight data to the server. The input data includes information about the type and amount of ingredients.

[1073] 3. Data Analysis

[1074] The server analyzes the received image data using image analysis technology. This allows it to recognize each ingredient in the refrigerator and identify its type and location. It also calculates the amount of each ingredient from the weight data. For example, if the server analyzes an image of a tomato and the weight sensor provides the tomato's weight, the server can determine how many tomatoes are left in the refrigerator.

[1075] Based on the recognized ingredients and their quantities, the server generates a list of the food items in the refrigerator, which includes all the ingredients and their respective quantities.

[1076] 4. Recipe suggestions and emotional engine

[1077] The server searches for available recipes based on the generated food list, taking into account the user's food preferences, nutritional balance, allergy information, and even the user's current emotional state using an emotion engine.

[1078] The emotion engine evaluates the user's emotional state through facial and voice analysis. For example, if the user is very tired, easy-to-make recipes will be prioritized.

[1079] The server uses the data obtained from the emotion engine to adjust the difficulty and genre of the recipes and provide recipes that best suit the user's emotions and situation. For example, if the user is tired, simple recipes with short cooking times will be suggested, while if the user is in a happy mood, special dishes that require a little more effort will be suggested.

[1080] 5. Management advice and emotional engine

[1081] The server also identifies items in the food list that are about to expire or are low in stock, such as yogurt that is expiring the next day or milk that is low in stock.

[1082] The emotion engine adjusts the priority of management advice based on the user's emotions: for example, if the user is feeling stressed, it will highlight foods that need to be consumed immediately and encourage positive behavior.

[1083] Based on this information, the server generates advice on food consumption priorities and replenishment, allowing users to efficiently manage the ingredients in their refrigerators.

[1084] 6. Notice and Information

[1085] The device (smartphone application) receives suggested recipes and management advice from the server and notifies the user. The notification content includes today's recipe suggestions and consumption / purchase advice.

[1086] The app also provides a feature that allows users to check the status of their refrigerators while they are out. For example, if a user opens the app while shopping at a supermarket, they can check in real time what ingredients are in the refrigerator and what they need.

[1087] Specific examples

[1088] For example, if you have tomatoes, onions, and cheese in your refrigerator, the server will suggest a recipe for tomato pasta based on these ingredients, advise you to use tomatoes that are close to their expiration date soon, and notify you to buy more milk if you are running low on it.

[1089] Furthermore, if the user comes home tired, the emotion engine will recognize the user's emotional state and suggest an easy-to-make tomato salad recipe, allowing the user to prepare dinner without any stress.

[1090] The above is an embodiment of the present invention. In this way, it is possible to efficiently manage ingredients in a refrigerator, reduce waste, and provide flexible recipe suggestions and management advice that correspond to the user's emotional state.

[1091] The processing flow will be explained below.

[1092] Step 1:

[1093] The terminal (refrigerator) captures image data using a camera inside the refrigerator, and a weight sensor measures the weight data of each shelf and food item.

[1094] Step 2:

[1095] The terminal (refrigerator) sends the captured image data and weight data to the server.

[1096] Step 3:

[1097] The server uses image analysis technology to analyze the received image data and recognize each food item in the refrigerator. For example, tomatoes and onions can be identified using image analysis technology.

[1098] Step 4:

[1099] The server analyzes the weight data and calculates the amount of each recognized ingredient. For example, based on the weight sensor data, it determines that there are three tomatoes and two onions.

[1100] Step 5:

[1101] The server generates a food list based on the recognized types and quantities of ingredients, which records all ingredients in the refrigerator and their quantities.

[1102] Step 6:

[1103] The server searches the database for available recipes based on the food list, taking into account the user's food preferences, nutritional balance, and input from the emotion engine.

[1104] Step 7:

[1105] The emotion engine recognizes the user's emotional state, for example, by analyzing facial expressions and voice to determine whether the user is tired or happy.

[1106] Step 8:

[1107] The server adjusts the difficulty and genre of the recipes based on the emotion engine data and selects recipes that are suitable for the user. For example, if the user is tired, it will suggest easy recipes.

[1108] Step 9:

[1109] The server analyzes the food list and identifies items that are nearing expiration or low in stock, for example, detecting yogurt that is about to expire or milk that is running low.

[1110] Step 10:

[1111] An emotion engine adjusts the priority of management advice based on the user's emotions, for example highlighting only important items if the user is busy.

[1112] Step 11:

[1113] The server generates management advice based on food items that are close to expiry or low in stock, such as "consume yogurt quickly" or "purchase more milk."

[1114] Step 12:

[1115] The device (smartphone application) receives suggested recipes and management advice from the server and notifies the user. For example, it sends a notification such as "Today's Easy Recipe: Tomato Salad."

[1116] Step 13:

[1117] Users can check the status of their refrigerators while they are out using a smartphone app. For example, they can open the app while shopping and check the ingredients in their refrigerators and their expiration dates.

[1118] Step 14:

[1119] The app consumes and replenishes ingredients based on the recipes and management advice users receive, and with the assistance of an emotion engine, prepares dishes that suit the user's mood that day.

[1120] Example 2

[1121] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1122] Currently, there are systems on the market that aim to manage ingredients in a refrigerator and suggest recipes, but these systems do not take into account the user's emotional state, making it difficult to provide appropriate recipe suggestions or management advice when the user is tired or under stress. Furthermore, they lack the functionality to check the refrigerator status in real time while away from home, making efficient ingredient management impossible. Furthermore, even if they can identify foods that are close to their expiration date or low in stock, they lack a way to appropriately notify the user of this information based on their emotional state.

[1123] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes: means for collecting data using a camera and sensors installed in the refrigerator; means for transmitting the collected data to the server; means for analyzing the collected data using image analysis technology and generating a list of foods in the refrigerator; means for suggesting available recipes based on the generated food list; means for identifying foods from the food list that are close to their expiration date or low in stock and generating management advice; means for notifying the user of the suggested recipes and management advice; means for allowing the user to access the food ingredient data in the refrigerator; means for providing an emotion engine for analyzing the user's emotional state; means for adjusting the difficulty level and genre of recipes based on data obtained from the emotion engine and providing optimal recipes; means for adjusting the priority of management advice based on the user's emotional state; and means for the user to check the status of the refrigerator while away from home. This enables flexible recipe suggestions and management advice to be provided taking the user's emotional state into consideration, thereby achieving efficient food ingredient management and appropriate information notification.

[1124] The "camera" is a photographic device for capturing images inside the refrigerator.

[1125] A "sensor" is a device that measures physical data such as the weight and position of food items in a refrigerator.

[1126] The "means of collecting data" refers to a system that uses cameras and sensors to obtain information about the inside of the refrigerator.

[1127] A "server" is a computer system that receives, stores, analyzes, and processes data.

[1128] "Image analysis technology" is a technology that processes collected image data to identify the type and location of ingredients.

[1129] A "food list" is data that records the types and amounts of food ingredients present in a refrigerator.

[1130] The "means for suggesting recipes" is a mechanism that provides users with recipe suggestions based on a food list.

[1131] "Management advice" is advice on how to handle or replenish food that is nearing its expiration date or is low in stock.

[1132] The "notification means" is a communication means for conveying suggested recipes and management advice to the user.

[1133] An "emotion engine" is software and algorithms for analyzing a user's emotional state.

[1134] The "means for checking the status of the refrigerator" is a system that allows the user to view information about ingredients in the refrigerator even when they are away from home.

[1135] "Means for analyzing emotional state" refers to technology that recognizes emotions from the user's facial expressions, voice, etc.

[1136] "Means for adjusting the difficulty and genre of recipes" refers to a mechanism that appropriately changes the content of suggested recipes based on data obtained from the emotion engine.

[1137] The "means for adjusting priority" is a mechanism for changing the importance and notification order of management advice depending on the emotional state of the user.

[1138] This invention combines a system that monitors the inside of a refrigerator, grasps the status of ingredients, and provides users with appropriate recipe suggestions and management advice, with an emotion engine that recognizes the user's emotions. The system consists of a camera and sensors installed in the refrigerator, a server, a user's smartphone application, and the emotion engine.

[1139] First, a camera and weight sensor are installed inside the device (refrigerator). The camera periodically captures images of the inside of the refrigerator, and the weight sensor measures the weight of each shelf and food item. This allows data to be collected about where and how much food is in the refrigerator. For example, by capturing an image of tomatoes placed on a shelf in the refrigerator and measuring their weight, the location and quantity of tomatoes can be determined.

[1140] Next, the collected image data and weight data are sent from the terminal (refrigerator) to a server. The server receives the data and stores it in a database. Specifically, the image data is stored in storage, and the weight data is associated with each ingredient. A high-speed database management system is recommended for this process.

[1141] The server analyzes the received data using image analysis technology. For example, it uses image recognition libraries such as OpenCV and TensorFlow to identify the type and location of each ingredient. It also calculates the quantity of each ingredient based on the weight data and generates a list of the food in the refrigerator. For example, from an analyzed image of a tomato, it recognizes that the ingredient is a "tomato," and combines this with the weight data to determine how many tomatoes are in the refrigerator.

[1142] The server then searches for available recipes from a database based on the generated food list, taking into account the user's food preferences, nutritional balance, and allergy information. This can be done using a database management system such as MySQL, which can reference the user's profile information to extract filtered recipes.

[1143] The emotion engine evaluates the user's emotional state through facial and voice analysis. For example, it utilizes Microsoft Azure's Face API and Google Cloud's Speech-to-Text service. Based on the emotional data obtained from the emotion engine, the server adjusts the difficulty and genre of the recipe and provides the most suitable recipe to the user. Specifically, if the user is very tired, it will suggest recipes with short cooking times, and if the user wants to enjoy cooking, it will suggest dishes that require a little more effort.

[1144] The server also identifies foods from the food list that are close to expiry or low in stock and generates management advice based on that. For example, a Python script can scan the food list, extract foods that fit certain criteria (such as yogurt that is close to expiry or milk that is low in stock), and generate management advice for them.

[1145] The device (smartphone application) then receives suggested recipes and management advice from the server and notifies the user. Specifically, push notifications are sent using Firebase Cloud Messaging, and information is delivered to the user's smartphone in real time. A function is also provided that allows users to check the status of their refrigerator while they are out. For example, if you open the app while shopping at the supermarket, you can find out in real time what ingredients are in your refrigerator and what is missing.

[1146] Specific examples

[1147] For example, if the user has tomatoes, onions, and cheese in the refrigerator, the server will suggest a tomato pasta recipe based on these ingredients. It will also advise the user to use tomatoes that are close to their expiration date soon and notify the user to purchase more milk if they are running low on stock. Furthermore, if the user comes home tired, the emotion engine will recognize the user's emotional state and suggest an easy tomato salad recipe, allowing the user to prepare dinner without any stress.

[1148] Prompt Sentence Examples

[1149] An example of a prompt for a generative AI model is:

[1150] "Explain in detail the refrigerator food management system that leverages the user's emotion engine, specifically each step from food monitoring to data analysis, recipe suggestions, management advice and notifications."

[1151] This concludes the description of the specific embodiment for carrying out the invention. By using this system, users can efficiently manage ingredients in their refrigerators, reduce waste, and receive flexible recipe suggestions and management advice that reflect their emotional state.

[1152] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1153] Step 1: Data collection

[1154] The terminal (refrigerator) collects food ingredient data using a camera and weight sensor installed inside. The camera periodically takes pictures of the inside of the refrigerator, and the weight sensor measures the weight of each shelf and food item. This makes it possible to obtain data on how much food is in which location inside the refrigerator. The input data is image data and weight data inside the refrigerator, and the output is information on the location and amount of food ingredients.

[1155] Step 2: Data transmission and storage

[1156] The terminal (refrigerator) sends the collected image data and weight data to the server. The server receives this data and stores it in a database. Specifically, the data is transferred via a high-speed network connection, and the data received by the server is stored in storage. The input data is the image data and weight data sent from the terminal, and the output is the raw data stored in the server's database.

[1157] Step 3: Image analysis and ingredient list generation

[1158] The server uses image analysis technology to analyze the received image data. For example, it uses OpenCV or TensorFlow to identify each ingredient and recognize its location and type. At the same time, it calculates the quantity of each ingredient from the weight data and generates a list of the food in the refrigerator. The input data is the saved image data and weight data, and the output is a list of recognized ingredients. Specifically, it recognizes tomatoes from the image and, in combination with the weight data, identifies the number of tomatoes.

[1159] Step 4: Recipe search and suggestions

[1160] The server searches for suitable recipes from a recipe database based on the generated food list, taking into account the user's food preferences, nutritional balance, and allergy information. Specifically, it uses MySQL or a similar database management system to extract filtered recipes based on the user's profile and food list. The input data is the generated food list and the user's profile information, and the output is suggested recipes.

[1161] Step 5: Leverage the Emotion Engine

[1162] The server optimizes recipe suggestions using data obtained from the user's emotion engine. Using Microsoft Azure's Face API and Google Cloud's Speech-to-Text service, the server analyzes the user's emotional state from their facial expressions and voice, and receives this data. The input data is the user's facial expression analysis data and voice analysis data, and the output is recipe suggestions that take their emotional state into account. Specifically, if the user is tired, it selects easy-to-make recipes.

[1163] Step 6: Generate management advice

[1164] The server identifies foods from the food list that are close to their expiration date or low in stock, and generates management advice based on that. A Python script scans the food list and extracts foods that meet certain criteria (e.g., expiration date within two days, stock of one unit or less). The input data is the generated food list, and the output is management advice. Specifically, it sends a notification to "consume soon" for yogurt that is close to its expiration date.

[1165] Step 7: Notification and Information

[1166] The device (smartphone application) receives suggested recipes and management advice from the server and notifies the user. Push notifications are sent using Firebase Cloud Messaging and delivered to the user's smartphone. In addition, a function is provided that allows users to check the status of their refrigerator while they are out. The input data is the recipe suggestions and management advice sent from the server, and the output is notifications and information displayed on the user's smartphone. In concrete terms, when a user opens the app while they are out, a list of ingredients in the refrigerator and a shopping list for necessary items are displayed.

[1167] The above is the specific processing flow and operation at each step.

[1168] (Application example 2)

[1169] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1170] While conventional refrigerator management systems could manage food inventory and expiration dates, they were unable to provide flexible recipe suggestions or management advice that took into account the user's current emotional state. As a result, even when users were stressed or tired, they were unable to obtain appropriate information, leading to problems such as overcooking or wasting ingredients. Furthermore, the system lacked the functionality to check the refrigerator status while away from home, which caused the inconvenience of not being able to determine what ingredients were needed when shopping.

[1171] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1172] In this invention, the server includes means for collecting data using cameras and sensors installed inside the refrigerator, means for transmitting the collected data to the server, means for the server to analyze the collected data using image analysis technology and generate a list of foods in the refrigerator, means for suggesting available recipes based on the generated food list, means for identifying foods from the food list that are close to their expiration date or that are low in stock and generating management advice, means for notifying the user of the suggested recipes and management advice, means for the user to check the status of the refrigerator while away from home, and means for recognizing the user's emotions and adjusting the recipe suggestions and management advice based on the user's emotional state. This enables flexible and appropriate recipe suggestions and management advice to be provided in accordance with the user's emotional state.

[1173] The camera installed inside the refrigerator is an image capturing device that periodically captures the status of the food ingredients inside the refrigerator.

[1174] The sensor is a device used to measure the weight and temperature of food in the refrigerator.

[1175] The means of collection is a method for obtaining data from cameras and sensors installed inside the refrigerator.

[1176] The means for transmitting is a communication technique for sending the collected data to the server.

[1177] Image analysis technology is a technology that analyzes collected image data and extracts useful information from photos and videos.

[1178] The means for generating a food list is a method for listing the types and amounts of ingredients in the refrigerator based on the analyzed data.

[1179] A recipe suggestion method is a method for presenting available dishes to a user based on a food list.

[1180] The means for generating management advice is a means for instructing the user on the optimal way to use ingredients and replenish them based on expiration dates and stock status.

[1181] Notification means are methods for informing users of suggested recipes and management advice.

[1182] The means for checking from outside the home is a technology that allows a user who is out to remotely check the status of the refrigerator.

[1183] Emotion recognition is a technology that analyzes a user's facial expressions and voice to determine their emotional state at that time.

[1184] The means for adjusting based on the emotional state is a method for changing the content of recipe suggestions and management advice depending on the user's emotions.

[1185] Below, we will explain an embodiment of the present invention that combines a user emotion engine. The present invention combines an emotion engine that recognizes the user's emotions with a system that monitors the inside of a refrigerator, grasps the status of ingredients, and provides the user with appropriate recipe suggestions and management advice. This system consists of a camera and sensor installed in the refrigerator, a server, a user's smartphone application, and an emotion engine.

[1186] 1. Refrigerator monitoring

[1187] First, a camera and a weight sensor are installed in the refrigerator. The camera periodically captures images of the inside of the refrigerator, and the weight sensor measures the weight of each shelf and food item. This data is collected as basic data on where and how much food is in the refrigerator.

[1188] 2. Data collection and transmission

[1189] The refrigerator (terminal) sends the collected image data and weight data to a server. The data is then sent to the server via the internet via a communication module installed in the terminal. The sent data includes information about the type of ingredients and their amounts.

[1190] 3. Data Analysis

[1191] The server uses image analysis technology to analyze the received image data. This allows it to recognize each ingredient in the refrigerator and identify its type and location. It then uses data obtained from the weight sensor to calculate the quantity of each ingredient. For example, it analyzes an image of a tomato and determines how many tomatoes are in the refrigerator based on the weight data. The server then generates a list of the food in the refrigerator based on the recognized ingredients and their quantities. This list records all the types of ingredients and their respective quantities.

[1192] 4. Recipe suggestions and emotional engine

[1193] The server searches for available recipes based on the generated food list. It takes into account the user's food preferences, nutritional balance, and allergy information, as well as their current emotional state using an emotion engine. The emotion engine evaluates the user's emotional state through facial expression and voice analysis. For example, if the user is very tired, easy-to-make recipes will be prioritized.

[1194] The system analyzes the user's facial expressions using a specially trained emotion recognition model. For example, it captures the user's facial expressions using a camera on a smartphone or smart glasses and applies an emotion recognition algorithm. Based on this, the server adjusts the difficulty and genre of the recipe to provide the most appropriate recipe for the emotion and situation. For example, if the user is tired, it will suggest a simple recipe such as tomato salad.

[1195] 5. Management advice and emotional engine

[1196] The server also identifies foods from the food list that are close to expiry or low in stock. The emotion engine adjusts the priority of management advice based on the user's emotions. For example, if the user is feeling stressed, it will highlight foods that need to be consumed immediately and encourage positive action. Based on this information, the server generates food consumption priorities and replenishment advice.

[1197] 6. Notice and Information

[1198] The refrigerator (device) notifies the user of recipe suggestions and management advice from the server. The notification content includes today's recipe suggestions and consumption / purchase advice via a smartphone application. It also provides a function that allows users to check the status of their refrigerator while they are out. For example, if a user opens the application while shopping at a supermarket, they can check in real time what ingredients are in the refrigerator and what they need.

[1199] Specific examples

[1200] For example, if the user has tomatoes, onions, and cheese in the refrigerator, the server will suggest a tomato pasta recipe based on these ingredients. It will also advise the user to use tomatoes that are close to their expiration date soon and notify the user to purchase more milk if they are running low on stock. Furthermore, if the user comes home tired, the emotion engine will recognize the user's emotional state and suggest an easy tomato salad recipe, allowing the user to prepare dinner without any stress.

[1201] Prompt Sentence Examples

[1202] "There are tomatoes and cheese in the fridge. The user seems tired. Can you suggest a simple recipe?"

[1203] The above is an embodiment of the present invention. In this way, it is possible to efficiently manage ingredients in a refrigerator, reduce waste, and provide flexible recipe suggestions and management advice that correspond to the user's emotional state.

[1204] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1205] Step 1:

[1206] Refrigerator status monitoring

[1207] Input: Data from cameras and sensors installed inside the refrigerator

[1208] Specific operation: The device periodically captures images of the inside of the refrigerator using a camera and obtains shelf and food weight data from a weight sensor.

[1209] Output: Image data and weight data

[1210] Step 2:

[1211] Data transmission

[1212] Input: Acquired image data and weight data

[1213] Specific operation: The device sends the collected data to a server via the Internet. This is done using a communication module.

[1214] Output: Image data and weight data sent to the server

[1215] Step 3:

[1216] Data analysis

[1217] Input: Image data and weight data sent to the server

[1218] Specific operation: The server uses image analysis technology to analyze the image data and identify the type and location of ingredients. It also analyzes the weight data to calculate the amount of each ingredient. For example, it recognizes tomatoes in the image data and determines how many tomatoes there are using the weight data.

[1219] Output: Food list (including ingredient types and quantities)

[1220] Step 4:

[1221] Recipe suggestions

[1222] Input: Food list, user's food preferences, nutritional balance, allergy information

[1223] Specific behavior: The server searches for available recipes based on the food list and the user's personal information. For example, if there are tomatoes and cheese, it may suggest a recipe for tomato pasta.

[1224] Output: A list of suggested recipes

[1225] Step 5:

[1226] emotion recognition

[1227] Input: User facial expression image or voice data

[1228] Specific operation: When a user uses a smartphone or smart glasses, the device's camera and microphone are used to capture facial expressions and voice in real time, and the captured data is analyzed using an emotion recognition model (generative AI model). For example, a tired facial expression is captured using a smartphone camera, and the model determines that the user is "tired."

[1229] Output: Determined emotional state

[1230] Step 6:

[1231] Tailoring recipe suggestions based on emotional state

[1232] Input: Determined emotional state, suggested recipe list

[1233] Specific operation: The server selects the most suitable recipe from the list depending on the user's emotional state. For example, if the user is tired, it will prioritize recipes that are easy to cook and require short cooking times.

[1234] Output: The recipe that best suits your emotional state

[1235] Step 7:

[1236] Generate management advice

[1237] Input: Food list, expiration date, inventory information

[1238] Specific behavior: The server identifies foods from the food list that are close to expiry or low in stock, and generates management advice for them. For example, it notifies the user to use tomatoes that are close to expiry or milk that is low in stock sooner or to replenish them.

[1239] Output: Management advice list

[1240] Step 8:

[1241] Notification and Information

[1242] Input: Recipes best suited to your emotional state, management advice list

[1243] Specific operation: The device (smartphone application) notifies the user of recipes and management advice that best suit their emotional state. It also provides a function that allows users to check the status of their refrigerator in real time. For example, while shopping at the supermarket, the user can check the refrigerator inventory on the app.

[1244] Output: Notifications to the user (recipe suggestions, management advice, refrigerator inventory information)

[1245] 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.

[1246] 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> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. 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 voice, text data indicating text, and image data indicating an image is also input. 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.

[1247] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1248] [Fourth embodiment]

[1249] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1250] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1251] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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).

[1252] 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.

[1253] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1254] 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 surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1255] 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.

[1256] The control object 443 includes a display device, LEDs in the eyes, and motors for driving 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.

[1257] 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.

[1258] 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 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.

[1259] 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.

[1260] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1261] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1262] The following describes an embodiment of the present invention. The present invention is a system that monitors the inside of a refrigerator, grasps the status of ingredients, and provides appropriate recipe suggestions and management advice to the user. This system consists of a camera and sensor installed in the refrigerator, a server, and a smartphone application on the user's smartphone.

[1263] 1. Refrigerator monitoring

[1264] First, a camera and weight sensor are installed in the refrigerator. The camera periodically captures images of the inside of the refrigerator, and the weight sensor measures the weight of each shelf and food item. This allows basic data to be collected about where and how much food is in the refrigerator.

[1265] 2. Data collection and transmission

[1266] The terminal (refrigerator) sends the collected image data and weight data to the server. The input data includes information about the type and amount of ingredients.

[1267] 3. Data Analysis

[1268] The server analyzes the received image data using image analysis technology. This allows it to recognize each ingredient in the refrigerator and identify its type and location. It also calculates the amount of each ingredient from the weight data. For example, if the server analyzes an image of a tomato and the weight sensor provides the tomato's weight, the server can determine how many tomatoes are left in the refrigerator.

[1269] Based on the recognized ingredients and their quantities, the server generates a list of the food items in the refrigerator, which includes all the ingredients and their respective quantities.

[1270] 4. Recipe suggestions

[1271] The server searches for available recipes based on the generated food list. Recipe suggestions take into account the user's food preferences, allergy information, and nutritional balance. For example, if the user is vegetarian, meat-free recipes will be suggested first.

[1272] The server filters the possible recipes and selects the appropriate recipe for the user, allowing them to create a dish that makes the most of the ingredients in their refrigerator.

[1273] 5. Management Advice

[1274] The server also identifies items in the food list that are close to expiry or low in stock, such as yogurt that is expiring the next day or milk that is low in stock, and uses this information to generate food consumption priorities and replenishment advice.

[1275] 6. Notice and Information

[1276] The terminal (smartphone application) receives suggested recipes and management advice from the server and notifies the user. The user can then check the suggested recipes and ingredients to purchase via their smartphone.

[1277] The app also provides a feature that allows users to check the status of their refrigerators while they are out. For example, if a user opens the app while shopping at a supermarket, they can check in real time what ingredients are in the refrigerator and what they need.

[1278] Specific examples

[1279] For example, if you have tomatoes, onions, and cheese in your refrigerator, the server will suggest a recipe for tomato pasta based on these ingredients, advise you to use tomatoes that are close to their expiration date soon, and notify you to buy more milk if you are running low on it.

[1280] The above is an embodiment of the present invention. In this way, it is possible to efficiently manage food ingredients in a refrigerator, reduce waste, and support the user's eating habits.

[1281] The processing flow will be explained below.

[1282] Step 1:

[1283] The terminal (refrigerator) captures image data using a camera inside the refrigerator, and a weight sensor measures the weight data of each shelf and food item.

[1284] Step 2:

[1285] The terminal (refrigerator) sends the captured image data and weight data to the server.

[1286] Step 3:

[1287] The server uses image analysis technology to analyze the received image data and recognize each food item in the refrigerator. For example, tomatoes and onions can be identified using image analysis technology.

[1288] Step 4:

[1289] The server analyzes the weight data and calculates the amount of each recognized ingredient. For example, based on the weight sensor data, it determines that there are three tomatoes and two onions.

[1290] Step 5:

[1291] The server generates a food list based on the recognized types and quantities of ingredients, which records all ingredients in the refrigerator and their quantities.

[1292] Step 6:

[1293] The server searches the database for available recipes based on the food list, taking into account the user's food preferences, nutritional balance, and allergy information.

[1294] Step 7:

[1295] The server filters the searched recipes and selects recipes that are suitable for the user, for example, if the user is vegetarian, meat-free recipes are selected.

[1296] Step 8:

[1297] The server analyzes the food list and identifies items that are nearing expiration or low in stock, for example, detecting yogurt that is about to expire or milk that is running low.

[1298] Step 9:

[1299] The server generates management advice based on food items that are nearing expiration or low in stock, such as "consume the yogurt quickly" or "buy more milk."

[1300] Step 10:

[1301] The device (smartphone application) receives recipe suggestions and management advice from the server and notifies the user. The notification content includes today's recipe suggestions and consumption / purchase advice.

[1302] Step 11:

[1303] Users can check the status of their refrigerators while they are out using a smartphone app. For example, while shopping, users can check what is left in the refrigerator in real time and purchase the ingredients they need.

[1304] Example 1

[1305] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1306] Managing food in the refrigerator manually is extremely time-consuming and prone to oversight, resulting in food waste. It is also difficult for users to accurately grasp food inventory and expiration dates, which often prevents them from making appropriate cooking plans. Furthermore, it is difficult for users to check the status of their refrigerators while away from home, which increases the risk of purchasing duplicate ingredients when shopping. To solve these issues, there is a need for an efficient, automated food management and cooking suggestion system.

[1307] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1308] In this invention, the server includes means for collecting data using a photographing device and a weight measuring device installed in the refrigerator, means for transmitting the collected data to an information processing device, means for the information processing device to analyze the collected data using image analysis technology and generate an ingredient list for the refrigerator, means for suggesting available recipes based on the generated ingredient list, means for identifying ingredients from the ingredient list that are close to their expiration date or that are low in stock and generating management advice, means for notifying the user of the suggested recipes and management advice, and means for the user to check the status of the refrigerator while away from home. This automatically manages the ingredients in the refrigerator and allows the user to obtain accurate ingredient information, enabling efficient ingredient use and reduced waste.

[1309] "Photography Device" means a camera or other photographic device installed to capture images inside the refrigerator.

[1310] "Weight measuring device" refers to a sensor or weighing device installed to measure the weight of food on each shelf or in a refrigerator.

[1311] An "information processing device" is a computing device such as a computer or server that analyzes collected data and generates various instructions.

[1312] "Image analysis technology" refers to algorithms and software that analyze captured image data and recognize specific objects or conditions.

[1313] The "ingredient list" is a data list that compiles information such as the type, quantity, and location of ingredients in the refrigerator.

[1314] "Cooking method" refers to specific steps or recipes that a user uses to cook food using ingredients.

[1315] "Management advice" refers to advice on consumption priorities and replenishment provided to the user based on the expiration dates and stock status of food ingredients in the refrigerator.

[1316] "Notification means" refers to a smartphone application or push notification system that notifies users of information and suggestions from the server.

[1317] "Means for checking from outside the home" refers to functions and services that allow users to remotely check the status of the refrigerator.

[1318] "Store deals" refers to promotional information such as price discounts and special sales offered by nearby retail stores.

[1319] MODE FOR CARRYING OUT THE INVENTION

[1320] The following describes in detail an embodiment of the present invention. The present invention is a system that monitors the inside of a refrigerator, grasps the status of ingredients, and provides appropriate recipe suggestions and management advice to the user. This system is composed of a photographing device and a weight measuring device installed in the refrigerator, an information processing device, and a smartphone application for the user.

[1321] System Hardware and Software

[1322] Hardware

[1323] Image capture device: A camera (e.g., a standard digital camera) that captures images of the inside of the refrigerator.

[1324] Weight measuring device: A sensor (e.g., a digital scale) that measures the weight of each shelf or item in the refrigerator.

[1325] Information processing device: A computer or server (e.g., a virtual server on the cloud) that analyzes data and generates various instructions.

[1326] User device: Smartphone, tablet, etc.

[1327] software

[1328] Image analysis software: Algorithms for analyzing image data and recognizing ingredients in the refrigerator (e.g., OpenCV).

[1329] Data analysis software: Software for analyzing weight data (e.g., TensorFlow).

[1330] Smartphone application: An app to send notifications to the user and display the status of the refrigerator (e.g. iOS, Android app).

[1331] Data collection and analysis

[1332] Terminal (refrigerator)

[1333] A camera installed inside the refrigerator periodically captures images of the interior of the refrigerator. For example, the camera takes a picture of the interior of the refrigerator at 8:00 AM every day. In addition, weight sensors installed on each shelf measure the weight of ingredients in real time. This allows data to be collected about what ingredients are in which position in the refrigerator and how much of each ingredient is in each position.

[1334] server

[1335] The information processing device (server) receives the image data and weight data sent from the refrigerator and analyzes them using image analysis technology. It uses OpenCV to recognize ingredients from the image and TensorFlow to analyze the weight data, thereby identifying the type and quantity of each ingredient. For example, it analyzes an image of tomatoes and calculates the number of tomatoes from the weight data. Based on this, a list of ingredients in the refrigerator is generated.

[1336] Recipe suggestions and management advice

[1337] server

[1338] Based on the generated ingredient list, the server suggests recipes that take into account the user's food preferences and nutritional balance. For example, if the user is vegetarian, meat-free recipes will be suggested. It also identifies ingredients that are close to their expiration date or are low in stock, and generates advice on consumption priorities and replenishment. For example, it generates advice on yogurt whose expiration date is approaching the next day or milk that needs to be replenished.

[1339] Notifications and on-the-go visibility

[1340] Terminal (smartphone application)

[1341] The user's smartphone application receives notifications of recipe suggestions and management advice sent from the server. The user can check the suggested recipes and ingredients to purchase through their smartphone. The user can also check the status of their refrigerator in real time while they are out. For example, if a user opens the app while shopping at a supermarket, they can check the status of ingredients in their refrigerator and make a list of what they need.

[1342] Examples of concrete examples and prompts

[1343] Specific examples

[1344] For example, if there are tomatoes, onions, and cheese in the refrigerator, the information processing device will suggest a recipe for tomato pasta based on these ingredients. It will also advise the user to use tomatoes that are close to their expiration date as soon as possible, and notify the user to purchase more milk if their stock is low.

[1345] Prompt Sentence Examples

[1346] "Please explain how the system works to suggest recipes using ingredients available in the refrigerator."

[1347] "Please tell me the detailed method for monitoring the food in the refrigerator and analyzing the data."

[1348] The above is an embodiment of the present invention. By using this system, it is possible to efficiently manage food ingredients in the refrigerator, reduce waste, and support the user's eating habits.

[1349] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1350] Step 1: Collecting data from inside the refrigerator

[1351] Specific explanation

[1352] The terminal (refrigerator) collects data using a camera and a weighing device installed inside the refrigerator. The camera periodically captures images of the interior of the refrigerator, and the weighing device measures the weight of each shelf and food item.

[1353] Input and Output

[1354] Input: Image data from the imaging device, weight data from the weight measuring device

[1355] Data processing: image capture, weight measurement

[1356] Output: Image data and weight data of the inside of the refrigerator

[1357] Specific actions

[1358] The camera captures images at regular intervals, adjusting the shutter speed, and the weighing device synchronizes to generate a digital signal to record the weight of each ingredient.

[1359] Step 2: Sending data

[1360] Specific explanation

[1361] The device (refrigerator) sends the collected image and weight data to a server over the Internet. The data is encrypted and transmitted using a secure protocol (e.g., HTTPS).

[1362] Input and Output

[1363] Input: Image data of the inside of the refrigerator, weight data

[1364] Data calculation: Encrypting data and converting it into a secure protocol

[1365] Output: Image data and weight data sent to the server

[1366] Specific actions

[1367] The refrigerator's built-in modem sends the data to the server's API endpoint, and once the transmission is complete, the server responds with a response acknowledging receipt of the data.

[1368] Step 3: Image and data analysis

[1369] Specific explanation

[1370] The server analyzes the received image data and weight data. It uses image analysis technology (e.g., OpenCV) to recognize ingredients from the image and weight analysis software (e.g., TensorFlow) to analyze the weight data. This identifies the type and amount of each ingredient.

[1371] Input and Output

[1372] Input: Image data and weight data sent to the server

[1373] Data calculation: Ingredient recognition (image analysis), weight estimation (weight data analysis)

[1374] Output: Analyzed ingredient list (type, amount, location)

[1375] Specific actions

[1376] OpenCV identifies ingredients such as tomatoes, cheese, and onions from the image, and TensorFlow estimates the quantity of each ingredient based on the weight data. The analysis results are then integrated to generate a detailed list of ingredients.

[1377] Step 4: Generate a food list

[1378] Specific explanation

[1379] The server then generates a list of the food items in the refrigerator based on the analyzed data, including the type of food, the amount, and the location of each food item in the refrigerator.

[1380] Input and Output

[1381] Input: Parsed ingredient list

[1382] Data processing: data integration and classification

[1383] Output: Final food list

[1384] Specific actions

[1385] The server stores the list of ingredients in a database and updates the increase or decrease in ingredients by comparing it with past consumption data.

[1386] Step 5: Recipe suggestions

[1387] Specific explanation

[1388] The server searches for available recipes based on the generated food list, and filters suitable recipes based on the user's preferences and nutritional balance.

[1389] Input and Output

[1390] Input: Final food list, user's food preferences and nutritional information

[1391] Data calculation: recipe search, filtering

[1392] Output: Recipe suggested to the user

[1393] Specific actions

[1394] The server accesses the recipe database and searches for recipes that match the criteria, and the filtered recipes are notified to the user.

[1395] Step 6: Generate management advice

[1396] Specific explanation

[1397] The server identifies ingredients from the food list that are close to their expiration date or are low in stock, and generates consumption priorities and replenishment advice.

[1398] Input and Output

[1399] Input: Final food list

[1400] Data calculation: Expiration date confirmation, stock confirmation, advice generation

[1401] Output: Management advice

[1402] Specific actions

[1403] The server scans the food list and generates appropriate advice based on expiration dates and stock levels.

[1404] Step 7: Notify and inform

[1405] Specific explanation

[1406] The terminal (smartphone application) receives suggested recipes and management advice from the server and notifies the user, who can then check the information in real time through the app.

[1407] Input and Output

[1408] Input: Suggested recipes, management advice

[1409] Data processing: notification generation, information display

[1410] Output: User notifications and real-time information

[1411] Specific actions

[1412] The smartphone app receives the push notification and displays the latest information on the screen. The user can then operate the app to check more detailed information.

[1413] The above is a concrete explanation of the program processing of this system.

[1414] (Application example 1)

[1415] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1416] The problem is that it is not possible to efficiently manage and utilize food in the refrigerator, and that food expires or is out of stock without the user being aware of it.Furthermore, it is also an issue that users cannot immediately order the ingredients they need, making meal planning a hassle.

[1417] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1418] In this invention, the server includes means for collecting data using an image capture device and a mass measurement device installed in the refrigerator, means for transmitting the collected data to an information processing device, means for the information processing device to analyze the collected data using image processing technology and generate a list of foods in the refrigerator, means for suggesting available cooking methods based on the generated food list, means for identifying foods from the food list that are close to their expiration date or are low in stock and generating food management advice, means for notifying the user of the suggested cooking methods and food management advice, means for the user to remotely check the status of the refrigerator, and means for automatically ordering ingredients that are in short supply from a food delivery service. This enables efficient food management and use, reduces food waste, and supports the user's meal planning.

[1419] An "image capture device" is a device that captures images of the inside of a refrigerator and recognizes the type and location of food.

[1420] A "mass measuring device" is a device that measures the weight of each shelf and food item in a refrigerator to determine the amount of food.

[1421] The "information processing device" is a device that receives and analyzes the collected data and generates a food list.

[1422] "Image processing technology" is a technology that analyzes captured images to recognize the type and location of food.

[1423] A "food inventory" is a list that records all the types and quantities of food in the refrigerator.

[1424] "Cooking Instructions" are recommended cooking recipes and steps based on the food list.

[1425] "Management advice" refers to advice on consumption priorities and replenishment for food items that are close to their expiration date or that are low in stock.

[1426] The "notification means" is a means for informing the user of suggested cooking methods and management advice.

[1427] The "means for checking from a remote location" is a means by which a user can check the status of the refrigerator in real time while away from home.

[1428] The "means for automatically ordering from a food delivery service" is a means for automatically ordering missing ingredients from a food delivery service.

[1429] The present invention is a system for managing food in a refrigerator and providing users with recipe suggestions and management advice. This system uses the following hardware and software.

[1430] Hardware

[1431] Image capture device: A device that captures images inside the refrigerator, specifically using a Raspberry Pi camera module.

[1432] Mass measuring device: A device that measures the weight of each shelf or food item, specifically using a weight sensor such as the HX711.

[1433] Information processing device: A server that analyzes data and generates a food list.

[1434] User terminal: A device such as a smartphone or tablet through which a user receives information.

[1435] software

[1436] Image processing technology: OpenCV is used for image analysis to identify the type and location of food.

[1437] Data analysis techniques: Analyze mass measurement results and calculate food volume. Use Python's Flask framework and TensorFlow.

[1438] Backend system: A system for collecting data, analyzing it, generating lists, and suggesting recipes, using Firebase as a database.

[1439] Specific processing flow

[1440] Data collection

[1441] 1. An image capture device periodically captures images of the inside of the refrigerator, and a mass measurement device measures the weight of each food item.

[1442] 2. The collected data is sent to an information processing device (server) via Raspberry Pi.

[1443] Data analysis

[1444] 1. The server analyzes the received image data using OpenCV to identify the type and location of the food.

[1445] 2. Based on the data from the mass measuring device, calculate the weight of each food item and determine the amount of food.

[1446] 3. The server integrates this data and generates a list of the food items in the refrigerator.

[1447] Recipe suggestions and management advice

[1448] 1. Based on the food list, the server uses a generative AI model using TensorFlow to suggest suitable recipes.

[1449] 2. Identify foods that are close to expiry date or low in stock from the food inventory and generate management advice based on this.

[1450] 3. Management advice and recipe suggestions are sent to the user's device via Firebase.

[1451] Automated ordering for food delivery services

[1452] 1. Based on the recipe suggestions, identify the missing ingredients.

[1453] 2. The server automatically calls the food delivery service API and orders the necessary ingredients.

[1454] Examples of use

[1455] If you have tomatoes, onions, and cheese in your fridge, the server will suggest a tomato pasta recipe based on that information. If you're low on pasta, the server will automatically order it from a food delivery service and notify you.

[1456] Prompt Sentence Examples

[1457] "If I have tomatoes, onions, and cheese in the fridge, I want to suggest a recipe using them and order any missing ingredients using a food delivery service API. What specific program would you use and what framework would you use?"

[1458] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1459] Step 1:

[1460] Data inside the refrigerator is collected. An image capture device (Raspberry Pi camera module) periodically captures images inside the refrigerator, and a mass measurement device (HX711) measures the weight of each food item. The input is real-time images inside the refrigerator and the weight data of each food item, and the output is a set of these data.

[1461] Step 2:

[1462] The collected data is sent to an information processing device (server). The Raspberry Pi sends image data and weight data to the information processing device. The input is the image and weight data acquired inside the refrigerator, and the output is the data sent to the server.

[1463] Step 3:

[1464] The server analyzes the received data. First, it performs image analysis on the image data using OpenCV to identify the type and location of the food. Next, it calculates the weight of each food item based on the weight data. The input is the transmitted image data and weight data, and the output is a list of the analyzed food types, locations, and weights.

[1465] Step 4:

[1466] A food list is generated based on the analyzed data. The server integrates the identified food information and generates a list of the food in the refrigerator. The input is a list of food types, locations, and weights, and the output is the food list.

[1467] Step 5:

[1468] Recipes are suggested based on a food list. The server uses a generative AI model using TensorFlow to search for and suggest appropriate recipes from a food list. The input is a food list, and the output is a suggested recipe.

[1469] Step 6:

[1470] Generate management advice. The server identifies foods that are close to their expiration date or low in stock from the food list, and generates management advice based on that. The input is the food list, and the output is management advice.

[1471] Step 7:

[1472] Suggested recipes and management advice are sent to the user's device. The server sends the suggested recipes and management advice to the user's smartphone via Firebase. The input is the suggested recipes and management advice, and the output is a notification sent to the user's device.

[1473] Step 8:

[1474] Automatically orders missing ingredients. The server identifies missing ingredients based on the proposed recipe and calls the food delivery service's API to automatically place the order. The input is the proposed recipe and a list of foods, and the output is the order data sent to the food delivery service.

[1475] Specific operations at each step include, for example, feature extraction in image analysis, calibration of weight data, and consideration of user preferences when proposing recipes.

[1476] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1477] Below, we will explain an embodiment of the present invention that combines a user emotion engine. The present invention combines an emotion engine that recognizes the user's emotions with a system that monitors the inside of a refrigerator, grasps the status of ingredients, and provides the user with appropriate recipe suggestions and management advice. This system consists of a camera and sensor installed in the refrigerator, a server, a user's smartphone application, and an emotion engine.

[1478] 1. Refrigerator monitoring

[1479] First, a camera and weight sensor are installed in the refrigerator. The camera periodically captures images of the inside of the refrigerator, and the weight sensor measures the weight of each shelf and food item. This allows basic data to be collected about where and how much food is in the refrigerator.

[1480] 2. Data collection and transmission

[1481] The terminal (refrigerator) sends the collected image data and weight data to the server. The input data includes information about the type and amount of ingredients.

[1482] 3. Data Analysis

[1483] The server analyzes the received image data using image analysis technology. This allows it to recognize each ingredient in the refrigerator and identify its type and location. It also calculates the amount of each ingredient from the weight data. For example, if the server analyzes an image of a tomato and the weight sensor provides the tomato's weight, the server can determine how many tomatoes are left in the refrigerator.

[1484] Based on the recognized ingredients and their quantities, the server generates a list of the food items in the refrigerator, which includes all the ingredients and their respective quantities.

[1485] 4. Recipe suggestions and emotional engine

[1486] The server searches for available recipes based on the generated food list, taking into account the user's food preferences, nutritional balance, allergy information, and even the user's current emotional state using an emotion engine.

[1487] The emotion engine evaluates the user's emotional state through facial and voice analysis. For example, if the user is very tired, easy-to-make recipes will be prioritized.

[1488] The server uses the data obtained from the emotion engine to adjust the difficulty and genre of the recipes and provide recipes that best suit the user's emotions and situation. For example, if the user is tired, simple recipes with short cooking times will be suggested, while if the user is in a happy mood, special dishes that require a little more effort will be suggested.

[1489] 5. Management advice and emotional engine

[1490] The server also identifies items in the food list that are about to expire or are low in stock, such as yogurt that is expiring the next day or milk that is low in stock.

[1491] The emotion engine adjusts the priority of management advice based on the user's emotions: for example, if the user is feeling stressed, it will highlight foods that need to be consumed immediately and encourage positive behavior.

[1492] Based on this information, the server generates advice on food consumption priorities and replenishment, allowing users to efficiently manage the ingredients in their refrigerators.

[1493] 6. Notice and Information

[1494] The device (smartphone application) receives suggested recipes and management advice from the server and notifies the user. The notification content includes today's recipe suggestions and consumption / purchase advice.

[1495] The app also provides a feature that allows users to check the status of their refrigerators while they are out. For example, if a user opens the app while shopping at a supermarket, they can check in real time what ingredients are in the refrigerator and what they need.

[1496] Specific examples

[1497] For example, if you have tomatoes, onions, and cheese in your refrigerator, the server will suggest a recipe for tomato pasta based on these ingredients, advise you to use tomatoes that are close to their expiration date soon, and notify you to buy more milk if you are running low on it.

[1498] Furthermore, if the user comes home tired, the emotion engine will recognize the user's emotional state and suggest an easy-to-make tomato salad recipe, allowing the user to prepare dinner without any stress.

[1499] The above is an embodiment of the present invention. In this way, it is possible to efficiently manage ingredients in a refrigerator, reduce waste, and provide flexible recipe suggestions and management advice that correspond to the user's emotional state.

[1500] The processing flow will be explained below.

[1501] Step 1:

[1502] The terminal (refrigerator) captures image data using a camera inside the refrigerator, and a weight sensor measures the weight data of each shelf and food item.

[1503] Step 2:

[1504] The terminal (refrigerator) sends the captured image data and weight data to the server.

[1505] Step 3:

[1506] The server uses image analysis technology to analyze the received image data and recognize each food item in the refrigerator. For example, tomatoes and onions can be identified using image analysis technology.

[1507] Step 4:

[1508] The server analyzes the weight data and calculates the amount of each recognized ingredient. For example, based on the weight sensor data, it determines that there are three tomatoes and two onions.

[1509] Step 5:

[1510] The server generates a food list based on the recognized types and quantities of ingredients, which records all ingredients in the refrigerator and their quantities.

[1511] Step 6:

[1512] The server searches the database for available recipes based on the food list, taking into account the user's food preferences, nutritional balance, and input from the emotion engine.

[1513] Step 7:

[1514] The emotion engine recognizes the user's emotional state, for example, by analyzing facial expressions and voice to determine whether the user is tired or happy.

[1515] Step 8:

[1516] The server adjusts the difficulty and genre of the recipes based on the emotion engine data and selects recipes that are suitable for the user. For example, if the user is tired, it will suggest easy recipes.

[1517] Step 9:

[1518] The server analyzes the food list and identifies items that are nearing expiration or low in stock, for example, detecting yogurt that is about to expire or milk that is running low.

[1519] Step 10:

[1520] An emotion engine adjusts the priority of management advice based on the user's emotions, for example highlighting only important items if the user is busy.

[1521] Step 11:

[1522] The server generates management advice based on food items that are close to expiry or low in stock, such as "consume yogurt quickly" or "purchase more milk."

[1523] Step 12:

[1524] The device (smartphone application) receives suggested recipes and management advice from the server and notifies the user. For example, it sends a notification such as "Today's Easy Recipe: Tomato Salad."

[1525] Step 13:

[1526] Users can check the status of their refrigerators while they are out using a smartphone app. For example, they can open the app while shopping and check the ingredients in their refrigerators and their expiration dates.

[1527] Step 14:

[1528] The app consumes and replenishes ingredients based on the recipes and management advice users receive, and with the assistance of an emotion engine, prepares dishes that suit the user's mood that day.

[1529] Example 2

[1530] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1531] Currently, there are systems on the market that aim to manage ingredients in a refrigerator and suggest recipes, but these systems do not take into account the user's emotional state, making it difficult to provide appropriate recipe suggestions or management advice when the user is tired or under stress. Furthermore, they lack the functionality to check the refrigerator status in real time while away from home, making efficient ingredient management impossible. Furthermore, even if they can identify foods that are close to their expiration date or low in stock, they lack a way to appropriately notify the user of this information based on their emotional state.

[1532] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes: means for collecting data using a camera and sensors installed in the refrigerator; means for transmitting the collected data to the server; means for analyzing the collected data using image analysis technology and generating a list of foods in the refrigerator; means for suggesting available recipes based on the generated food list; means for identifying foods from the food list that are close to their expiration date or low in stock and generating management advice; means for notifying the user of the suggested recipes and management advice; means for allowing the user to access the food ingredient data in the refrigerator; means for providing an emotion engine for analyzing the user's emotional state; means for adjusting the difficulty level and genre of recipes based on data obtained from the emotion engine and providing optimal recipes; means for adjusting the priority of management advice based on the user's emotional state; and means for the user to check the status of the refrigerator while away from home. This enables flexible recipe suggestions and management advice to be provided taking the user's emotional state into consideration, thereby achieving efficient food ingredient management and appropriate information notification.

[1533] The "camera" is a photographic device for capturing images inside the refrigerator.

[1534] A "sensor" is a device that measures physical data such as the weight and position of food items in a refrigerator.

[1535] The "means of collecting data" refers to a system that uses cameras and sensors to obtain information about the inside of the refrigerator.

[1536] A "server" is a computer system that receives, stores, analyzes, and processes data.

[1537] "Image analysis technology" is a technology that processes collected image data to identify the type and location of ingredients.

[1538] A "food list" is data that records the types and amounts of food ingredients present in a refrigerator.

[1539] The "means for suggesting recipes" is a mechanism that provides users with recipe suggestions based on a food list.

[1540] "Management advice" is advice on how to handle or replenish food that is nearing its expiration date or is low in stock.

[1541] The "notification means" is a communication means for conveying suggested recipes and management advice to the user.

[1542] An "emotion engine" is software and algorithms for analyzing a user's emotional state.

[1543] The "means for checking the status of the refrigerator" is a system that allows the user to view information about ingredients in the refrigerator even when they are away from home.

[1544] "Means for analyzing emotional state" refers to technology that recognizes emotions from the user's facial expressions, voice, etc.

[1545] "Means for adjusting the difficulty and genre of recipes" refers to a mechanism that appropriately changes the content of suggested recipes based on data obtained from the emotion engine.

[1546] The "means for adjusting priority" is a mechanism for changing the importance and notification order of management advice depending on the emotional state of the user.

[1547] This invention combines a system that monitors the inside of a refrigerator, grasps the status of ingredients, and provides users with appropriate recipe suggestions and management advice, with an emotion engine that recognizes the user's emotions. The system consists of a camera and sensors installed in the refrigerator, a server, a user's smartphone application, and the emotion engine.

[1548] First, a camera and weight sensor are installed inside the device (refrigerator). The camera periodically captures images of the inside of the refrigerator, and the weight sensor measures the weight of each shelf and food item. This allows data to be collected about where and how much food is in the refrigerator. For example, by capturing an image of tomatoes placed on a shelf in the refrigerator and measuring their weight, the location and quantity of tomatoes can be determined.

[1549] Next, the collected image data and weight data are sent from the terminal (refrigerator) to a server. The server receives the data and stores it in a database. Specifically, the image data is stored in storage, and the weight data is associated with each ingredient. A high-speed database management system is recommended for this process.

[1550] The server analyzes the received data using image analysis technology. For example, it uses image recognition libraries such as OpenCV and TensorFlow to identify the type and location of each ingredient. It also calculates the quantity of each ingredient based on the weight data and generates a list of the food in the refrigerator. For example, from an analyzed image of a tomato, it recognizes that the ingredient is a "tomato," and combines this with the weight data to determine how many tomatoes are in the refrigerator.

[1551] The server then searches for available recipes from a database based on the generated food list, taking into account the user's food preferences, nutritional balance, and allergy information. This can be done using a database management system such as MySQL, which can reference the user's profile information to extract filtered recipes.

[1552] The emotion engine evaluates the user's emotional state through facial and voice analysis. For example, it utilizes Microsoft Azure's Face API and Google Cloud's Speech-to-Text service. Based on the emotional data obtained from the emotion engine, the server adjusts the difficulty and genre of the recipe and provides the most suitable recipe to the user. Specifically, if the user is very tired, it will suggest recipes with short cooking times, and if the user wants to enjoy cooking, it will suggest dishes that require a little more effort.

[1553] The server also identifies foods from the food list that are close to expiry or low in stock and generates management advice based on that. For example, a Python script can scan the food list, extract foods that fit certain criteria (such as yogurt that is close to expiry or milk that is low in stock), and generate management advice for them.

[1554] The device (smartphone application) then receives suggested recipes and management advice from the server and notifies the user. Specifically, push notifications are sent using Firebase Cloud Messaging, and information is delivered to the user's smartphone in real time. A function is also provided that allows users to check the status of their refrigerator while they are out. For example, if you open the app while shopping at the supermarket, you can find out in real time what ingredients are in your refrigerator and what is missing.

[1555] Specific examples

[1556] For example, if the user has tomatoes, onions, and cheese in the refrigerator, the server will suggest a tomato pasta recipe based on these ingredients. It will also advise the user to use tomatoes that are close to their expiration date soon and notify the user to purchase more milk if they are running low on stock. Furthermore, if the user comes home tired, the emotion engine will recognize the user's emotional state and suggest an easy tomato salad recipe, allowing the user to prepare dinner without any stress.

[1557] Prompt Sentence Examples

[1558] An example of a prompt for a generative AI model is:

[1559] "Explain in detail the refrigerator food management system that leverages the user's emotion engine, specifically each step from food monitoring to data analysis, recipe suggestions, management advice and notifications."

[1560] This concludes the description of the specific embodiment for carrying out the invention. By using this system, users can efficiently manage ingredients in their refrigerators, reduce waste, and receive flexible recipe suggestions and management advice that reflect their emotional state.

[1561] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1562] Step 1: Data collection

[1563] The terminal (refrigerator) collects food ingredient data using a camera and weight sensor installed inside. The camera periodically takes pictures of the inside of the refrigerator, and the weight sensor measures the weight of each shelf and food item. This makes it possible to obtain data on how much food is in which location inside the refrigerator. The input data is image data and weight data inside the refrigerator, and the output is information on the location and amount of food ingredients.

[1564] Step 2: Data transmission and storage

[1565] The terminal (refrigerator) sends the collected image data and weight data to the server. The server receives this data and stores it in a database. Specifically, the data is transferred via a high-speed network connection, and the data received by the server is stored in storage. The input data is the image data and weight data sent from the terminal, and the output is the raw data stored in the server's database.

[1566] Step 3: Image analysis and ingredient list generation

[1567] The server uses image analysis technology to analyze the received image data. For example, it uses OpenCV or TensorFlow to identify each ingredient and recognize its location and type. At the same time, it calculates the quantity of each ingredient from the weight data and generates a list of the food in the refrigerator. The input data is the saved image data and weight data, and the output is a list of recognized ingredients. Specifically, it recognizes tomatoes from the image and, in combination with the weight data, identifies the number of tomatoes.

[1568] Step 4: Recipe search and suggestions

[1569] The server searches for suitable recipes from a recipe database based on the generated food list, taking into account the user's food preferences, nutritional balance, and allergy information. Specifically, it uses MySQL or a similar database management system to extract filtered recipes based on the user's profile and food list. The input data is the generated food list and the user's profile information, and the output is suggested recipes.

[1570] Step 5: Leverage the Emotion Engine

[1571] The server optimizes recipe suggestions using data obtained from the user's emotion engine. Using Microsoft Azure's Face API and Google Cloud's Speech-to-Text service, the server analyzes the user's emotional state from their facial expressions and voice, and receives this data. The input data is the user's facial expression analysis data and voice analysis data, and the output is recipe suggestions that take their emotional state into account. Specifically, if the user is tired, it selects easy-to-make recipes.

[1572] Step 6: Generate management advice

[1573] The server identifies foods from the food list that are close to their expiration date or low in stock, and generates management advice based on that. A Python script scans the food list and extracts foods that meet certain criteria (e.g., expiration date within two days, stock of one unit or less). The input data is the generated food list, and the output is management advice. Specifically, it sends a notification to "consume soon" for yogurt that is close to its expiration date.

[1574] Step 7: Notification and Information

[1575] The device (smartphone application) receives suggested recipes and management advice from the server and notifies the user. Push notifications are sent using Firebase Cloud Messaging and delivered to the user's smartphone. In addition, a function is provided that allows users to check the status of their refrigerator while they are out. The input data is the recipe suggestions and management advice sent from the server, and the output is notifications and information displayed on the user's smartphone. In concrete terms, when a user opens the app while they are out, a list of ingredients in the refrigerator and a shopping list for necessary items are displayed.

[1576] The above is the specific processing flow and operation at each step.

[1577] (Application example 2)

[1578] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1579] While conventional refrigerator management systems could manage food inventory and expiration dates, they were unable to provide flexible recipe suggestions or management advice that took into account the user's current emotional state. As a result, even when users were stressed or tired, they were unable to obtain appropriate information, leading to problems such as overcooking or wasting ingredients. Furthermore, the system lacked the functionality to check the refrigerator status while away from home, which caused the inconvenience of not being able to determine what ingredients were needed when shopping.

[1580] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1581] In this invention, the server includes means for collecting data using cameras and sensors installed inside the refrigerator, means for transmitting the collected data to the server, means for the server to analyze the collected data using image analysis technology and generate a list of foods in the refrigerator, means for suggesting available recipes based on the generated food list, means for identifying foods from the food list that are close to their expiration date or that are low in stock and generating management advice, means for notifying the user of the suggested recipes and management advice, means for the user to check the status of the refrigerator while away from home, and means for recognizing the user's emotions and adjusting the recipe suggestions and management advice based on the user's emotional state. This enables flexible and appropriate recipe suggestions and management advice to be provided in accordance with the user's emotional state.

[1582] The camera installed inside the refrigerator is an image capturing device that periodically captures the status of the food ingredients inside the refrigerator.

[1583] The sensor is a device used to measure the weight and temperature of food in the refrigerator.

[1584] The means of collection is a method for obtaining data from cameras and sensors installed inside the refrigerator.

[1585] The means for transmitting is a communication technique for sending the collected data to the server.

[1586] Image analysis technology is a technology that analyzes collected image data and extracts useful information from photos and videos.

[1587] The means for generating a food list is a method for listing the types and amounts of ingredients in the refrigerator based on the analyzed data.

[1588] A recipe suggestion method is a method for presenting available dishes to a user based on a food list.

[1589] The means for generating management advice is a means for instructing the user on the optimal way to use ingredients and replenish them based on expiration dates and stock status.

[1590] Notification means are methods for informing users of suggested recipes and management advice.

[1591] The means for checking from outside the home is a technology that allows a user who is out to remotely check the status of the refrigerator.

[1592] Emotion recognition is a technology that analyzes a user's facial expressions and voice to determine their emotional state at that time.

[1593] The means for adjusting based on the emotional state is a method for changing the content of recipe suggestions and management advice depending on the user's emotions.

[1594] Below, we will explain an embodiment of the present invention that combines a user emotion engine. The present invention combines an emotion engine that recognizes the user's emotions with a system that monitors the inside of a refrigerator, grasps the status of ingredients, and provides the user with appropriate recipe suggestions and management advice. This system consists of a camera and sensor installed in the refrigerator, a server, a user's smartphone application, and an emotion engine.

[1595] 1. Refrigerator monitoring

[1596] First, a camera and a weight sensor are installed in the refrigerator. The camera periodically captures images of the inside of the refrigerator, and the weight sensor measures the weight of each shelf and food item. This data is collected as basic data on where and how much food is in the refrigerator.

[1597] 2. Data collection and transmission

[1598] The refrigerator (terminal) sends the collected image data and weight data to a server. The data is then sent to the server via the internet via a communication module installed in the terminal. The sent data includes information about the type of ingredients and their amounts.

[1599] 3. Data Analysis

[1600] The server uses image analysis technology to analyze the received image data. This allows it to recognize each ingredient in the refrigerator and identify its type and location. It then uses data obtained from the weight sensor to calculate the quantity of each ingredient. For example, it analyzes an image of a tomato and determines how many tomatoes are in the refrigerator based on the weight data. The server then generates a list of the food in the refrigerator based on the recognized ingredients and their quantities. This list records all the types of ingredients and their respective quantities.

[1601] 4. Recipe suggestions and emotional engine

[1602] The server searches for available recipes based on the generated food list. It takes into account the user's food preferences, nutritional balance, and allergy information, as well as their current emotional state using an emotion engine. The emotion engine evaluates the user's emotional state through facial expression and voice analysis. For example, if the user is very tired, easy-to-make recipes will be prioritized.

[1603] The system analyzes the user's facial expressions using a specially trained emotion recognition model. For example, it captures the user's facial expressions using a camera on a smartphone or smart glasses and applies an emotion recognition algorithm. Based on this, the server adjusts the difficulty and genre of the recipe to provide the most appropriate recipe for the emotion and situation. For example, if the user is tired, it will suggest a simple recipe such as tomato salad.

[1604] 5. Management advice and emotional engine

[1605] The server also identifies foods from the food list that are close to expiry or low in stock. The emotion engine adjusts the priority of management advice based on the user's emotions. For example, if the user is feeling stressed, it will highlight foods that need to be consumed immediately and encourage positive action. Based on this information, the server generates food consumption priorities and replenishment advice.

[1606] 6. Notice and Information

[1607] The refrigerator (device) notifies the user of recipe suggestions and management advice from the server. The notification content includes today's recipe suggestions and consumption / purchase advice via a smartphone application. It also provides a function that allows users to check the status of their refrigerator while they are out. For example, if a user opens the application while shopping at a supermarket, they can check in real time what ingredients are in the refrigerator and what they need.

[1608] Specific examples

[1609] For example, if the user has tomatoes, onions, and cheese in the refrigerator, the server will suggest a tomato pasta recipe based on these ingredients. It will also advise the user to use tomatoes that are close to their expiration date soon and notify the user to purchase more milk if they are running low on stock. Furthermore, if the user comes home tired, the emotion engine will recognize the user's emotional state and suggest an easy tomato salad recipe, allowing the user to prepare dinner without any stress.

[1610] Prompt Sentence Examples

[1611] "There are tomatoes and cheese in the fridge. The user seems tired. Can you suggest a simple recipe?"

[1612] The above is an embodiment of the present invention. In this way, it is possible to efficiently manage ingredients in a refrigerator, reduce waste, and provide flexible recipe suggestions and management advice that correspond to the user's emotional state.

[1613] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1614] Step 1:

[1615] Refrigerator status monitoring

[1616] Input: Data from cameras and sensors installed inside the refrigerator

[1617] Specific operation: The device periodically captures images of the inside of the refrigerator using a camera and obtains shelf and food weight data from a weight sensor.

[1618] Output: Image data and weight data

[1619] Step 2:

[1620] Data transmission

[1621] Input: Acquired image data and weight data

[1622] Specific operation: The device sends the collected data to a server via the Internet. This is done using a communication module.

[1623] Output: Image data and weight data sent to the server

[1624] Step 3:

[1625] Data analysis

[1626] Input: Image data and weight data sent to the server

[1627] Specific operation: The server uses image analysis technology to analyze the image data and identify the type and location of ingredients. It also analyzes the weight data to calculate the amount of each ingredient. For example, it recognizes tomatoes in the image data and determines how many tomatoes there are using the weight data.

[1628] Output: Food list (including ingredient types and quantities)

[1629] Step 4:

[1630] Recipe suggestions

[1631] Input: Food list, user's food preferences, nutritional balance, allergy information

[1632] Specific behavior: The server searches for available recipes based on the food list and the user's personal information. For example, if there are tomatoes and cheese, it may suggest a recipe for tomato pasta.

[1633] Output: A list of suggested recipes

[1634] Step 5:

[1635] emotion recognition

[1636] Input: User facial expression image or voice data

[1637] Specific operation: When a user uses a smartphone or smart glasses, the device's camera and microphone are used to capture facial expressions and voice in real time, and the captured data is analyzed using an emotion recognition model (generative AI model). For example, a tired facial expression is captured using a smartphone camera, and the model determines that the user is "tired."

[1638] Output: Determined emotional state

[1639] Step 6:

[1640] Tailoring recipe suggestions based on emotional state

[1641] Input: Determined emotional state, suggested recipe list

[1642] Specific operation: The server selects the most suitable recipe from the list depending on the user's emotional state. For example, if the user is tired, it will prioritize recipes that are easy to cook and require short cooking times.

[1643] Output: The recipe that best suits your emotional state

[1644] Step 7:

[1645] Generate management advice

[1646] Input: Food list, expiration date, inventory information

[1647] Specific behavior: The server identifies foods from the food list that are close to expiry or low in stock, and generates management advice for them. For example, it notifies the user to use tomatoes that are close to expiry or milk that is low in stock sooner or to replenish them.

[1648] Output: Management advice list

[1649] Step 8:

[1650] Notification and Information

[1651] Input: Recipes best suited to your emotional state, management advice list

[1652] Specific operation: The device (smartphone application) notifies the user of recipes and management advice that best suit their emotional state. It also provides a function that allows users to check the status of their refrigerator in real time. For example, while shopping at the supermarket, the user can check the refrigerator inventory on the app.

[1653] Output: Notifications to the user (recipe suggestions, management advice, refrigerator inventory information)

[1654] 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.

[1655] 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> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. 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 voice, text data indicating text, and image data indicating an image is also input. 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.

[1656] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1657] 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.

[1658] FIG. 9 is a diagram illustrating 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 actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect 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.

[1659] 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.

[1660] 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).

[1661] 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 indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, 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 indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1662] 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."

[1663] 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.

[1664] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1665] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1666] 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.

[1667] 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.

[1668] 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.

[1669] The hardware resource for executing a specific process can be any of the following processors: An example of a processor 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. Another example of a processor is 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.

[1670] The hardware resource that executes the specific processing 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 processing may be a single processor.

[1671] 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.

[1672] 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.

[1673] 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.

[1674] 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.

[1675] The following is further disclosed regarding the above embodiment.

[1676] (Claim 1)

[1677] A means of collecting data using cameras and sensors installed inside the refrigerator,

[1678] means for transmitting the collected data to a server;

[1679] a means for the server to analyze the collected data using image analysis technology and generate a list of food items in the refrigerator;

[1680] a means for suggesting available recipes based on the generated food list;

[1681] a means for identifying foods from the food list that are close to expiry or low in stock and generating management advice;

[1682] a means of informing them of suggested recipes and management advice;

[1683] A means for a user to check the status of the refrigerator while away from home;

[1684] A system including:

[1685] (Claim 2)

[1686] 2. The system according to claim 1, further comprising means for suggesting recipes taking into consideration the user's food preferences and nutritional balance.

[1687] (Claim 3)

[1688] 10. The system of claim 1, further comprising means for providing food replenishment advice taking into account special offers from nearby retailers.

[1689] "Example 1"

[1690] (Claim 1)

[1691] a means for collecting data using an imaging device and a weight measuring device installed inside the refrigerator;

[1692] means for transmitting the collected data to an information processing device;

[1693] an information processing device that analyzes the collected data using image analysis technology and generates a list of ingredients in the refrigerator;

[1694] A means for suggesting available recipes based on the generated ingredient list;

[1695] a means for identifying ingredients from the list of ingredients that are close to their expiration date or that are low in stock, and generating management advice;

[1696] a means of communicating suggested cooking methods and management advice;

[1697] A means for a user to check the status of the refrigerator while away from home;

[1698] A system including:

[1699] (Claim 2)

[1700] 2. The system according to claim 1, further comprising means for suggesting recipes taking into consideration the user's food preferences and nutritional balance.

[1701] (Claim 3)

[1702] The system according to claim 1, further comprising a means for providing advice on replenishing ingredients taking into consideration bargain information from nearby stores.

[1703] "Application Example 1"

[1704] (Claim 1)

[1705] means for collecting data using an image capture device and a mass measurement device installed within the refrigerator;

[1706] means for transmitting the collected data to an information processing device;

[1707] a means for analyzing the collected data using image ...

Claims

1. A means of collecting data using cameras and sensors installed inside the refrigerator, means for transmitting the collected data to a server; a means for the server to analyze the collected data using image analysis technology and generate a list of food items in the refrigerator; a means for suggesting available recipes based on the generated food list; a means for identifying foods from the food list that are close to expiry or low in stock and generating management advice; a means of informing them of suggested recipes and management advice; A means for a user to check the status of the refrigerator while away from home; A system including:

2. The system according to claim 1, further comprising means for suggesting recipes taking into consideration the user's food preferences and nutritional balance.

3. The system of claim 1 further comprising means for providing food replenishment advice taking into account special offers from nearby retailers.

Images