system

Abstract

Provide a system. A method for storing health data in a home includes: means for detecting the type and quantity of ingredients; A means for analyzing nutritional balance based on household health data and food ingredient data; A means for generating a menu based on the analysis results; A means for displaying the generated menu; 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] In modern family life, it is not easy to plan daily meals while taking into consideration the health of all family members. In particular, providing balanced meals while taking into consideration the health status of each family member (height, weight, blood pressure, blood test data, etc.) is a huge burden. Furthermore, planning meals while efficiently using ingredients in the refrigerator is also a challenge. To solve these problems, a system is needed that can automatically suggest optimal menus based on health data within the household. [Means for solving the problem]

[0005] The present invention provides a system that stores household health data, detects the types and quantities of ingredients, analyzes nutritional balance based on this data, and generates optimal menus. Specifically, the system includes a means for storing household health data, a means for detecting the types and quantities of ingredients, a means for analyzing nutritional balance based on the household health data and ingredient data, a means for generating menus based on the analysis results, and a means for displaying the generated menus. The system also includes a means for transmitting ingredient data to a server via the Internet, where the server analyzes and generates menus, and transmitting the generated menus to a client terminal for display. Furthermore, the client terminal is a refrigerator equipped with a camera and sensors for detecting ingredients in the refrigerator, which then transmit data to the server. This system automatically suggests balanced meals for the home, facilitating health management for the entire family.

[0006] "Household health data" refers to information about the health status of each family member, such as their height, weight, blood pressure, and blood test data.

[0007] "Type and quantity of ingredients" refers to the type and quantity of food stored in a refrigerator, etc.

[0008] "Analyzing" refers to the act of analyzing information based on collected data and drawing a specific conclusion or result.

[0009] "Generating a menu" refers to the act of creating a specific meal plan based on nutritional balance and health data.

[0010] "Display" refers to the act of making the generated information or results visible to a user on a display or other visual means.

[0011] "Transmitting over the Internet" refers to the act of transmitting digital information over the Internet to another device or server.

[0012] "Server" refers to a computer system that stores, analyzes, and processes data.

[0013] A "client terminal" refers to a device that is directly operated by a user, such as a refrigerator.

[0014] "Cameras and sensors" refers to photographic and measuring devices used to detect the type and quantity of ingredients.

[0015] "Nutritional balance" refers to a state in which necessary nutrients are consumed in appropriate proportions.

[0016] "User" refers to a member of a household who uses the system. [Brief explanation of the drawings]

[0017] [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

[0018] 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.

[0019] First, the terms used in the following description will be explained.

[0020] 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).

[0021] 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.

[0022] 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.

[0023] 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.

[0024] 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."

[0025] [First embodiment]

[0026] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0027] 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.

[0028] 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).

[0029] 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.

[0030] 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.

[0031] 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.

[0032] 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.

[0033] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0034] 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.

[0035] 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.

[0036] 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.

[0037] 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."

[0038] This invention is a system that combines household health data and food ingredient data stored in the refrigerator to create a menu that optimizes nutritional balance. Specifically, it stores household health data, detects the types and quantities of ingredients, analyzes nutritional balance based on this data, and creates an optimal menu. Specific embodiments of this system are described below.

[0039] Program processing explanation

[0040] Data collection and transmission

[0041] First, a user adds a new ingredient to the refrigerator. For example, say they put carrots in the refrigerator. The device (refrigerator) automatically detects this new ingredient using its built-in camera and weight sensor. The device uses image recognition technology to identify the ingredient name "carrots" and the quantity. This generates ingredient data such as "Item name: carrots, quantity: 3, expiration date: 2023-10-15." The device then sends this data to a server via the Internet.

[0042] Data analysis and menu generation

[0043] The server receives the food ingredient data sent from the device. At the same time, it references the family's health data stored in a cloud database. This includes each family member's height, weight, blood pressure, and blood test data. The server analyzes the nutritional balance based on the received food ingredient and health data. For example, if the family's intake of vitamin A is insufficient, it will suggest a meal plan to supplement that.

[0044] The server uses an AI algorithm to analyze health and food data and generate an optimal menu, such as "Breakfast: Carrot smoothie, Lunch: Carrot and chicken salad, Dinner: Carrot stew."

[0045] Menu notification and display

[0046] The server sends the generated menu information to the terminal. The menu is displayed on the refrigerator display for the user to check. The user checks the suggested menu on the refrigerator display, takes out the necessary ingredients, and starts cooking.

[0047] Specific examples

[0048] Let's say a user puts a tomato in the refrigerator. The following steps occur:

[0049] 1. Add ingredients

[0050] The user puts tomatoes in the refrigerator.

[0051] 2. Data detection and transmission

[0052] The device detects tomatoes, generates data such as "Product name: Tomato, Quantity: 5, Best before date: 2023-10-20", and sends it to the server.

[0053] 3. Data analysis and menu generation

[0054] The server analyzes the received data and the household's health data and generates a menu such as "Lunch: Tomato salad, Dinner: Tomato pasta."

[0055] 4. Notification and display of menu

[0056] The generated menu information is sent to a terminal and displayed on the refrigerator display.

[0057] This reduces the burden on users of managing ingredients and planning healthy meals, allowing them to lead an efficient and healthy diet.

[0058] The processing flow will be explained below.

[0059] Step 1:

[0060] The user adds ingredients to the refrigerator. The user puts "carrots" in the refrigerator.

[0061] Step 2:

[0062] The device (refrigerator) recognizes the ingredients. The device's built-in camera and weight sensor detect "carrots." Image recognition and barcode scanning technology are used to identify the type and quantity of ingredients.

[0063] Step 3:

[0064] The device (refrigerator) generates food ingredient data, such as "Item name: carrots, quantity: 3, expiration date: 2023-10-15" based on the recognized ingredients.

[0065] Step 4:

[0066] The device (refrigerator) sends the food ingredient data to the server, which then sends the data to the server via the Internet.

[0067] Step 5:

[0068] The server receives the ingredient data. The server receives the data sent from the terminal.

[0069] Step 6:

[0070] The server references the family's health data and obtains the family's health data (height, weight, blood pressure, blood test data, etc.) from the cloud database.

[0071] Step 7:

[0072] The server analyzes food ingredient and health data, using an AI algorithm to analyze the data taking into account nutritional balance and the health status of family members.

[0073] Step 8:

[0074] The server generates the optimal menu, such as "Breakfast: Carrot smoothie, Lunch: Carrot and chicken salad, Dinner: Carrot stew" based on the analysis results.

[0075] Step 9:

[0076] The server sends the generated menu to the terminal. The menu information is sent to the terminal.

[0077] Step 10:

[0078] The terminal (refrigerator) displays the menu. The generated menu is displayed on the screen and the user can check it.

[0079] Step 11:

[0080] The user checks the menu. The user checks the menu displayed on the screen.

[0081] Step 12:

[0082] The user starts cooking. The user follows the suggested menu, selects the necessary ingredients, and starts cooking.

[0083] Example 1

[0084] 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."

[0085] Currently, menu suggestion systems on the market lack the ability to generate menus that fully consider the user's health status. They also lack mechanisms for efficiently managing ingredients in the home, making it difficult for users to lead a healthy diet without hassle. This invention aims to solve these problems by automatically generating and presenting optimal menus that take into consideration the management of ingredients in the home and health.

[0086] 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.

[0087] In this invention, the server includes means for detecting the types and quantities of ingredients, means for generating ingredient data using image recognition technology and weight sensors, means for transmitting the ingredient data to the server via the Internet, means for the server to analyze the health data and ingredient data within the household and generate an optimal menu using an AI algorithm, and means for displaying the generated menu on the refrigerator display. This allows users to easily obtain an optimal menu based on the health data and ingredient status.

[0088] "Household health data" refers to information that indicates the individual health status of family members, such as their height, weight, blood pressure, and blood test data.

[0089] "Type and quantity of food ingredients" is information indicating the specific names and quantities of food items stored in the household.

[0090] "Image recognition technology" is a technology for analyzing images captured by a camera and identifying objects contained within them.

[0091] A "weight sensor" is a device for measuring the weight of an object.

[0092] "Food ingredient data" is a series of information including the type, quantity, expiration date, etc. of food ingredients.

[0093] "Means for transmitting ingredient data to a server via the Internet" refers to a method or system for transmitting ingredient data obtained via the Internet to a remote server.

[0094] An "AI algorithm" is a calculation procedure or rule that uses artificial intelligence technology to analyze input data and generate optimal results.

[0095] A "menu" is a meal plan that combines multiple meal menus.

[0096] A "refrigerator display" is a screen installed on a refrigerator, and is a device for displaying information to a user.

[0097] A "server" is a central processing unit for storing data, analyzing data, and providing information to other devices.

[0098] MODE FOR CARRYING OUT THE INVENTION

[0099] This invention is a system that combines household health data and food ingredient data stored in a refrigerator to create a menu that optimizes nutritional balance. Specific embodiments of this system are described below.

[0100] Data collection and transmission

[0101] A user adds a new ingredient to the refrigerator. For example, suppose the user puts carrots in the refrigerator. The device (refrigerator) automatically detects this new ingredient using its built-in camera and weight sensor. Specifically, the camera inside the refrigerator takes an image of the carrots, and the sensor measures their weight. The device uses image recognition technology (e.g., TENSORFLOW (registered trademark)) to identify the ingredient and generate data such as "Item name: carrots, quantity: 3, expiration date: 2023-10-15." The generated data is sent to a server via the Internet.

[0102] Data analysis and menu generation

[0103] The server receives the ingredient data sent by the user and stores it in a cloud database. At the same time, it retrieves the family's health data (height, weight, blood pressure, blood test data, etc.) from the cloud database. The server analyzes nutritional balance based on the health data and ingredient data. For example, if all family members are deficient in vitamin A, it generates a menu to make up for that deficiency. The server uses an AI algorithm (e.g., OpenAI's GPT-3 (registered trademark)) to suggest the optimal menu based on the health data and ingredient data. As a specific example, it generates "Breakfast: Carrot smoothie, Lunch: Carrot and chicken salad, Dinner: Carrot stew."

[0104] Menu notification and display

[0105] The server sends the generated menu information to the terminal (refrigerator). The terminal displays the received menu information on the refrigerator's display. The user checks this display, selects ingredients based on the proposed menu, and begins cooking.

[0106] Specific examples

[0107] For example, if the user puts "tomatoes" in the refrigerator, the system behaves as follows:

[0108] 1. Add ingredients

[0109] The user puts tomatoes in the refrigerator.

[0110] 2. Data detection and transmission

[0111] The device detects tomatoes, generates data such as "Product name: Tomato, Quantity: 5, Best before date: 2023-10-20" and sends it to the server.

[0112] 3. Data analysis and menu generation

[0113] The server analyzes the received data and the household's health data and generates a menu of "Lunch: Tomato salad, Dinner: Tomato pasta."

[0114] 4. Notification and display of menu

[0115] The generated menu information is sent to a terminal and displayed on the refrigerator display.

[0116] This reduces the burden on users of managing ingredients and planning healthy meals, allowing them to lead an efficient and healthy diet.

[0117] Prompt Sentence Examples

[0118] By inputting the following prompts into the generative AI model, optimal meal suggestions can be obtained:

[0119] A new ingredient has been added to your refrigerator. Please use the following data to create a meal plan that takes into account your family's health status.

[0120] Ingredients: Tomato

[0121] Quantity: 5 pieces

[0122] Best before date: 2023-10-20

[0123] Health data: The whole family has low vitamin A intake.

[0124] Please suggest one of the generated menus for breakfast, one for lunch, and one for dinner.

[0125] This invention combines the latest image recognition technology with AI algorithms to realize in-home food management and health-optimized meal planning, allowing users to lead a healthy diet without any hassle.

[0126] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0127] The flow of this system's program processing

[0128] Step 1: Adding and detecting ingredients

[0129] The user adds a new ingredient to the refrigerator. For example, the user adds "carrots" to the refrigerator.

[0130] Input: New ingredient (carrot)

[0131] Operation:

[0132] The device (refrigerator) takes a picture of the carrot using its built-in camera.

[0133] A weight sensor measures the weight of the carrot.

[0134] Data processing:

[0135] Image data acquired by the camera and weight data acquired by the weight sensor are stored in the device's internal memory.

[0136] Output: Image data and weight data

[0137] Step 2: Generate ingredient data

[0138] The device generates ingredient data using image recognition technology.

[0139] Input: Image data and weight data

[0140] Operation:

[0141] The device uses image recognition software (e.g., TensorFlow) to analyze the captured image and identify the name of the ingredient (carrot).

[0142] Determine the quantity of carrots based on the data from the weight sensor.

[0143] Data processing:

[0144] Formatted data including the ingredient name, quantity, and expiration date is generated (e.g., "Item name: Carrots, Quantity: 3, Expiration date: 2023-10-15").

[0145] Output: Food ingredient data (product name, quantity, expiration date)

[0146] Step 3: Submit ingredient data

[0147] The generated ingredient data is transmitted to a server via the Internet.

[0148] Input: Food ingredient data (product name, quantity, expiration date)

[0149] Operation:

[0150] The terminal transmits the ingredient data to a server via the Internet.

[0151] Output: Ingredient data sent to the server

[0152] Step 4: Acquire and analyze health data

[0153] The server collects health data from within the home and analyzes it along with food ingredient data.

[0154] Input: Food ingredient data (product name, quantity, expiration date), household health data (height, weight, blood pressure, blood test data)

[0155] Operation:

[0156] The server retrieves home health data from a cloud database.

[0157] The server combines the health data with the newly acquired food ingredient data and performs the analysis.

[0158] Data processing:

[0159] Analyze nutritional balance and evaluate the relationship between the family's nutritional status and new ingredients.

[0160] Output: Nutritional balance analysis results

[0161] Step 5: Create a menu

[0162] The server uses AI algorithms to generate the optimal menu.

[0163] Input: Nutritional balance analysis results

[0164] Operation:

[0165] The server uses a generative AI model (e.g., OpenAI's GPT-3) to generate the optimal menu based on the analysis results.

[0166] For example, to increase your intake of vitamin A, we suggest a menu such as "Breakfast: Carrot smoothie, Lunch: Carrot and chicken salad, Dinner: Carrot stew."

[0167] Data processing:

[0168] Health data and food ingredient data are integrated to generate optimized menus.

[0169] Output: Generated menu (specific menus for breakfast, lunch, and dinner)

[0170] Step 6: Submit and view your menu

[0171] The generated menu information is sent to be displayed on the refrigerator display.

[0172] Input: Generated menu

[0173] Operation:

[0174] The server then transmits the generated menu information to the terminal (refrigerator) again via the Internet.

[0175] The device displays the received menu information on the refrigerator's display.

[0176] Output: Menu displayed on the refrigerator display

[0177] Specific examples

[0178] The specific flow of processing when the user puts "tomatoes" in the refrigerator is as follows:

[0179] 1. Add ingredients

[0180] The user puts tomatoes in the refrigerator.

[0181] 2. Data detection and transmission

[0182] The device takes a picture of the tomato with its built-in camera, and a weight sensor measures the tomato's weight.

[0183] Using image recognition technology, "tomato" is identified and the following data is generated: "Product name: Tomato, Quantity: 5, Best before date: 2023-10-20."

[0184] The generated data is sent to the server.

[0185] 3. Data analysis and menu generation

[0186] The server receives the food ingredient data and retrieves the health data from the cloud database.

[0187] Analyze health data and food ingredient data to generate a menu of "Lunch: Tomato salad, Dinner: Tomato pasta."

[0188] 4. Notification and display of menu

[0189] The generated menu information is sent to be displayed on the refrigerator display.

[0190] The user checks the displayed menu, selects the necessary ingredients, and begins cooking.

[0191] keyword

[0192] Generative AI model, prompt sentence

[0193] (Application example 1)

[0194] 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."

[0195] While there have been systems that combine food data from a home refrigerator with health data to generate nutritionally balanced menus, there have been no systems that integrate food data from a commercial facility with food products purchased at home and generate optimal menus based on health data from the home. This has made it difficult for consumers to select products that are optimal for their health. The present invention aims to solve these problems and provide a system that provides personalized nutrition advice and product suggestions to consumers.

[0196] 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.

[0197] In this invention, the server includes means for storing health data in the home, means for detecting the types and quantities of ingredients, means for analyzing nutritional balance based on the health data and ingredient data in the home, means for generating menus based on the analysis results, means for displaying the generated menus, and means for combining products purchased in commercial facilities with ingredient data in the home to propose optimal menus. This enables consumers to select products that are optimal for their health condition and lead an efficient and healthy diet.

[0198] A "household health data storage means" is a storage device or database for storing health information (e.g., height, weight, blood pressure, blood test results, etc.) for each family member.

[0199] "Means for detecting the type and quantity of ingredients" refers to devices or technologies that automatically recognize the type and quantity of ingredients using cameras or sensors inside the refrigerator.

[0200] The "means for analyzing nutritional balance based on household health data and food ingredient data" refers to a processor or algorithm that analyzes the collected health data and food ingredient data and performs calculations to optimize the nutritional balance of each family member.

[0201] The "means for generating a menu based on the analysis results" is a system that suggests meal menus suitable for each family member based on the results of nutritional analysis.

[0202] The "means for displaying the generated menu" is a device equipped with a display or screen that displays the generated menu information so that the user can check it.

[0203] "A means of combining products purchased in a commercial facility with food ingredient data from the home to suggest the optimal menu" is a system that obtains data on products that consumers are planning to purchase in a commercial facility, analyzes this data together with food ingredient data from the home and household health data, and suggests the optimal menu.

[0204] This invention is a system that integrates the products a consumer is planning to purchase in a physical store with food data stored in the home refrigerator and household health data to propose a menu that takes into account optimal nutritional balance. Detailed embodiments of this system are described below.

[0205] System Configuration

[0206] The system consists of the following main components:

[0207] Client terminal: A device such as a smartphone or robot

[0208] Server: A central system that integrates data and performs analysis

[0209] Storage device: A database that stores health data within the home

[0210] Hardware and software used

[0211] Hardware: smartphones, robot cameras, sensors

[0212] Software: OpenCV (image processing library), Requests (HTTP request library)

[0213] Data collection and transmission

[0214] When a user scans an item in a commercial facility, the device collects information about the item and acquires the appropriate data using the device's built-in camera and sensors. The device then transmits the collected data to a server via the Internet. Specifically, image recognition technology is used to identify the product name and quantity. OpenCV is used for this process.

[0215] Data analysis and menu generation

[0216] The server receives product data sent from the device, food data in the home refrigerator, and household health data. Based on the received data, the server uses an AI algorithm to analyze nutritional balance and generate optimal menus. This analysis uses a generative AI model and analyzes the results.

[0217] Menu notification and display

[0218] The server sends the generated menu information to the device, which then presents this information to the user, who can then check the optimal menu. For example, the menu is displayed on a smartphone or robot display.

[0219] Specific examples

[0220] 1. Data collection and transmission:

[0221] A user attempts to purchase "tomatoes" at a commercial facility.

[0222] The tomato is scanned with the smartphone camera, data such as "Item name: Tomato, Quantity: 5" is generated and sent to the server.

[0223] 2. Data analysis and menu generation:

[0224] The server analyzes the received tomato data, household health data, and data on other ingredients in the refrigerator, and generates a menu such as "Lunch: Tomato salad, Dinner: Tomato pasta."

[0225] 3. Menu Notification and Display:

[0226] The generated menu information is sent to a smartphone and displayed for the user to review.

[0227] Prompt Sentence Examples

[0228] "Could you tell me how much food you have in the fridge?"

[0229] "How can I get optimal vitamin A?"

[0230] The system allows users to take their household health data into account when choosing what to buy, enabling them to lead an efficient and healthy diet.

[0231] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0232] Step 1:

[0233] Data collection

[0234] A user scans a product they are about to purchase at a commercial facility. The device (smartphone or robot) uses a camera to take a picture of the product (e.g., tomatoes) and acquires image data. This image data becomes the input. Image recognition technology (e.g., OpenCV) is used to identify the product name (tomatoes) and quantity (5 pieces). As a result, data such as "Product name: tomatoes, quantity: 5 pieces" is output.

[0235] Step 2:

[0236] Data transmission

[0237] The terminal sends the product data acquired in step 1 to the server via the Internet. In this step, an HTTP request library (e.g., Requests) is used to send the product data to the server. The input data is the product name and quantity, which are then sent to the server.

[0238] Step 3:

[0239] Integrating household health data and food ingredient data

[0240] The server retrieves food item data and health data from the cloud database. The inputs are the health data of each household member (e.g., height, weight, blood pressure) and food item data in the refrigerator (e.g., ingredients in stock and their quantities). The server integrates this data and generates a dataset for analysis.

[0241] Step 4:

[0242] Nutritional balance analysis

[0243] The server analyzes nutritional balance based on the product data and household data received in step 2. In this step, a generative AI model is used to analyze the collected data as an input dataset. For example, if the user's vitamin A intake is insufficient, it determines whether it can suggest ingredients and menus to compensate for that. As a result of the analysis, a specific action plan for adjusting nutritional balance is output.

[0244] Step 5:

[0245] Menu generation

[0246] The server generates an optimal menu based on the results of the nutritional balance analysis. The input is the analysis result data, and based on this, it proposes the optimal meal menu for each member of the household. The generated menu (e.g., "Lunch: Tomato salad, Dinner: Tomato pasta") is output.

[0247] Step 6:

[0248] Menu notification

[0249] The server sends the generated menu information to the terminal. In this step, the generated menu data is sent to the terminal using the HTTP protocol. The input is the menu data, and once it is sent to the terminal, the menu is displayed on the terminal's display. This allows the user to check the optimal menu.

[0250] Step 7:

[0251] User Verification

[0252] The user checks the menu displayed on the terminal screen, takes out the necessary ingredients, and begins cooking. The menu information displayed on the terminal is input, and the user checks the ingredients based on this and cooks. Specific actions include taking the necessary ingredients from the refrigerator and cooking as displayed on the screen. Examples of prompt sentences include, "Please tell me how much food is in the refrigerator," and "How can I get the optimal amount of vitamin A?"

[0253] 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.

[0254] This invention combines a system that generates menus that optimize nutritional balance by combining health data within the home and food data stored in the refrigerator, with an emotion engine that recognizes the user's emotions. Specific embodiments of this system are described below.

[0255] Program processing explanation

[0256] Data collection and transmission

[0257] First, the user adds a new ingredient to the refrigerator. For example, let's say the user puts "carrots" in the refrigerator. The device (refrigerator) detects the new ingredient using its built-in camera and weight sensor. The device uses image recognition technology to identify the ingredient name "carrots" and its quantity. This generates ingredient data: "Item name: carrots, quantity: 3, expiration date: 2023-10-15." The device then sends this ingredient data to a server via the Internet.

[0258] The emotion engine also detects the user's facial expressions, tone of voice, and other emotional indicators to recognize their current emotional state. For example, if the user is recognized as "feeling stressed," that emotion data is also sent to the server.

[0259] Data analysis and menu generation

[0260] The server receives the food ingredient data and emotion data sent from the device. At the same time, it references the family's health data stored in a cloud database, including each family member's height, weight, blood pressure, and blood test data. The server analyzes all the received data, taking into account nutritional balance and the user's emotions.

[0261] For example, if the household is lacking in Vitamin A intake and the user is feeling stressed, the server will generate a menu that takes this into account. Based on data from the emotion engine, it will prioritize ingredients and cooking methods that are suitable for reducing stress.

[0262] As a concrete example, the server generates a menu such as "Breakfast: Carrot smoothie (rich in vitamin A, stress-reducing), Lunch: Carrot and chicken salad, Dinner: Carrot stew."

[0263] Menu notification and display

[0264] The server sends the generated menu information to the terminal. The generated menu is displayed on the refrigerator display for the user to check. The user checks the suggested menu on the refrigerator display, takes out the necessary ingredients, and starts cooking.

[0265] Specific examples

[0266] If the user puts tomatoes in the refrigerator and the emotion engine detects the user's stress state, the following process occurs:

[0267] 1. Adding ingredients: The user puts tomatoes in the refrigerator.

[0268] 2. Data detection and transmission: The device detects tomatoes, generates the data "Product name: Tomato, Quantity: 5, Best before date: 2023-10-20", and transmits it to the server. At the same time, the emotion engine detects the user's stress level and transmits the emotion data to the server.

[0269] 3. Data analysis and menu generation: The server analyzes the received data and generates a menu rich in vitamins and minerals that is effective in reducing stress. For example, a specific menu such as "Lunch: Tomato and avocado salad, Dinner: Tomato pasta" is created.

[0270] 4. Menu notification and display: The generated menu information is sent to the terminal and displayed on the refrigerator display.

[0271] In this way, users can be provided with optimal menus based on their food management, health status, and emotional state, enabling them to lead an efficient and healthy diet.

[0272] The processing flow will be explained below.

[0273] Step 1:

[0274] The user adds ingredients to the refrigerator. The user puts "carrots" in the refrigerator.

[0275] Step 2:

[0276] The device (refrigerator) recognizes the ingredients. The device's built-in camera and weight sensor detect "carrots." Image recognition and barcode scanning technology are used to identify the type and quantity of ingredients.

[0277] Step 3:

[0278] The device (refrigerator) generates the ingredient data. Based on the recognized ingredients, the data generated is "Item name: carrots, quantity: 3, expiration date: 2023-10-15".

[0279] Step 4:

[0280] The terminal (refrigerator) sends food ingredient data to the server, which then sends this data to the server via the Internet.

[0281] Step 5:

[0282] The emotion engine of the device (refrigerator) recognizes the user's emotions. It uses facial recognition and voice analysis technology using a camera and microphone to identify the user's emotional state (for example, to recognize that the user is feeling stressed).

[0283] Step 6:

[0284] The device (refrigerator) sends the recognized emotion data to a server. The emotional state data is then sent to the server via the internet.

[0285] Step 7:

[0286] The server receives the ingredient data and emotion data. The server receives the ingredient data and emotion data transmitted from the terminal.

[0287] Step 8:

[0288] The server references the family's health data and retrieves the family's health data (height, weight, blood pressure, blood test data, etc.) stored in the cloud database.

[0289] Step 9:

[0290] The server analyzes food ingredient data, emotional data, and health data, and uses AI algorithms to analyze the data taking into account nutritional balance, the health of family members, and the user's emotional state.

[0291] Step 10:

[0292] The server generates an optimal menu based on the analysis results, such as "Breakfast: Carrot smoothie (rich in vitamin A, effective in reducing stress)," "Lunch: Carrot and chicken salad," and "Dinner: Carrot stew."

[0293] Step 11:

[0294] The server sends the generated menu to the terminal, and the menu information is sent to the terminal via the Internet.

[0295] Step 12:

[0296] The terminal (refrigerator) displays the menu. The generated menu is displayed on the screen and the user can check it.

[0297] Step 13:

[0298] The user checks the menu. The user checks the menu displayed on the screen.

[0299] Step 14:

[0300] The user starts cooking. The user follows the suggested menu, selects the necessary ingredients, and starts cooking.

[0301] Example 2

[0302] 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."

[0303] In order to efficiently manage ingredients and health at home, conventional systems generally create menus by considering only ingredient and health data. However, this method cannot propose menus that take into account the user's emotional state, making it difficult to provide menus that are truly satisfying to the user. In particular, users who are feeling stressed need menu proposals that are not only nutritionally balanced but also psychologically satisfying.

[0304] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0305] In this invention, the server includes means for storing household health data, means for detecting the types and quantities of ingredients, means for detecting the user's emotional state, means for analyzing nutritional balance based on the household health data, ingredient data, and emotional data, means for generating menus based on the analysis results, and means for displaying the generated menus. This makes it possible to propose menus that take into account not only the user's health state but also their emotional state at any given time, thereby increasing user satisfaction.

[0306] "Household health data" refers to information about each household member's individual health status, such as their height, weight, blood pressure, and blood test results.

[0307] "Type and quantity of ingredients" is information about the name and quantity of each ingredient present in the refrigerator.

[0308] "User's emotional state" is information indicating the user's psychological state obtained from the user's facial expression, tone of voice, behavior, etc.

[0309] An "emotion engine" is a system or software that analyzes emotional indicators such as a user's facial expressions and tone of voice to identify the user's emotional state.

[0310] "Means for analyzing nutritional balance" refers to the process of evaluating ingredients and nutritional balance based on collected data to find the optimal combination of nutrients.

[0311] The "means for generating a menu" is a system or software that suggests an optimal meal plan to the user based on the collected health data, ingredient data, and emotional data.

[0312] The "means for displaying the generated menu" is a display or other display device that allows the user to visually confirm the proposed menu.

[0313] "Food data" refers to information such as the type, quantity, and expiration date of food ingredients stored in the refrigerator.

[0314] A "server" is a computer system that receives, analyzes, and stores data over the Internet.

[0315] A "client terminal" is a device that can be directly operated by a user, such as a refrigerator or a tablet.

[0316] "Transmitting over the Internet" is the process of transmitting data over a network to a server.

[0317] The "cameras and sensors" are video capture devices and weight measurement devices installed inside the refrigerator.

[0318] This invention combines a system that generates menus that optimize nutritional balance by combining health data within the home and food data stored in the refrigerator, with an emotion engine that recognizes the user's emotions. Specific embodiments of this system are described below.

[0319] Data collection and transmission

[0320] First, a user adds a new ingredient to the refrigerator. For example, suppose a user puts "carrots" in the refrigerator. The device (refrigerator) detects the new ingredient using its built-in camera and weight sensor. Specifically, the camera captures video of the inside of the refrigerator and identifies the "carrots" using image recognition technology. The weight sensor measures the weight of the carrots. This generates ingredient data such as "Item name: carrots, quantity: 3, expiration date: 2023-10-15." This ingredient data is sent to a server via the Internet.

[0321] In addition, the emotion engine analyzes the user's facial expressions and tone of voice to recognize their emotional state. For example, when a user holds their face close to the refrigerator or operates it with their voice, the emotion engine recognizes that the user is "feeling stressed." This emotion data is also sent to the server.

[0322] Data analysis and menu generation

[0323] The server receives the food ingredient and emotion data sent from the device and also references the family's health data stored in a cloud database, including each family member's height, weight, blood pressure, and blood test results.

[0324] The server analyzes all the data it receives, taking into account nutritional balance and the user's emotions. For example, if the household's intake of vitamin A is insufficient and the user is feeling stressed, it will generate a menu using ingredients that are rich in vitamin A and have stress-reducing effects. Specifically, it will suggest dishes such as "Breakfast: Carrot smoothie, Lunch: Carrot and chicken salad, Dinner: Carrot stew."

[0325] Menu notification and display

[0326] The generated menu information is sent from the server to the terminal. The generated menu is displayed on the refrigerator display for the user to check. The user checks the suggested menu on the display, selects the necessary ingredients, and begins cooking.

[0327] Specific examples

[0328] For example, if a user puts tomatoes in the refrigerator and the emotion engine detects the user's stress state, the following process occurs:

[0329] 1. Adding ingredients: The user puts tomatoes in the refrigerator.

[0330] 2. Data detection and transmission: The device detects tomatoes, generates the data "Product name: Tomato, Quantity: 5, Best before date: 2023-10-20", and transmits it to the server. At the same time, the emotion engine detects the user's stress level and transmits the emotion data to the server.

[0331] 3. Data analysis and menu generation: The server analyzes the received data and generates a menu rich in vitamins and minerals that is effective in reducing stress. For example, a menu such as "Lunch: Tomato and avocado salad, Dinner: Tomato pasta" might be created.

[0332] 4. Menu notification and display: The generated menu information is sent to the terminal and displayed on the refrigerator display.

[0333] Example prompts to input to the generative AI model

[0334] When a user adds "broccoli" to the refrigerator, the emotion engine detects the user's happiness state. Combine this with the family's health data to generate appropriate menus. For example, please provide specific menu suggestions for breakfast, lunch, and dinner.

[0335] Using this prompt, the generative AI model can make specific menu suggestions.

[0336] In this way, users can manage their ingredients and be provided with optimal menus that take into account their health and emotional state, enabling them to lead an efficient, satisfying and healthy diet.

[0337] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0338] Step 1:

[0339] Add ingredients

[0340] The user adds a new ingredient to the refrigerator, for example, "carrots," which triggers data collection in the next step.

[0341] Input: New ingredient (e.g. carrot)

[0342] Output: Ingredients added to the refrigerator

[0343] Step 2:

[0344] Data detection and transmission

[0345] The device detects new ingredients using its built-in camera and weight sensor. Specifically, the camera captures video of the inside of the refrigerator and identifies "carrots" using image recognition technology. At the same time, the weight sensor measures the weight of the added ingredient. This data is compiled as "Item name: Carrots, Quantity: 3, Best before date: 2023-10-15" and sent to a server via the Internet.

[0346] Input: Video data inside the refrigerator, weight data

[0347] Output: Food data (product name: carrots, quantity: 3, expiration date: 2023-10-15)

[0348] Step 3:

[0349] Collecting and transmitting emotional data

[0350] The emotion engine analyzes the user's facial expressions and tone of voice to recognize their emotional state. For example, it collects voice data when the user approaches the refrigerator or talks to it. If the analysis determines that the user is feeling stressed, that emotional data is also sent to the server.

[0351] Input: User's facial expression data, voice data

[0352] Output: Emotion data (e.g., feeling stressed)

[0353] Step 4:

[0354] Data analysis and menu generation

[0355] The server receives the food ingredient data and emotion data sent from the device. At the same time, it also references the family's health data stored in a cloud database, including each family member's height, weight, blood pressure, and blood test results. The server then analyzes all the received data, taking into account nutritional balance and the user's emotions.

[0356] For example, if the user is experiencing stress and their household is lacking in Vitamin A, the system will generate a menu using ingredients that are rich in Vitamin A and have a stress-reducing effect. As a result, the system will suggest dishes such as "Breakfast: Carrot smoothie, Lunch: Carrot and chicken salad, Dinner: Carrot stew."

[0357] Input: Food data, emotion data, health data

[0358] Output: Generated menu (e.g. Breakfast: Carrot smoothie, Lunch: Carrot and chicken salad, Dinner: Carrot stew)

[0359] Step 5:

[0360] Menu notification and display

[0361] The generated menu information is sent from the server to the terminal, which then displays the received information on the refrigerator display so that the user can check it.

[0362] Input: Generated menu information

[0363] Output: Menu displayed on the screen

[0364] Step 6:

[0365] User operations

[0366] The user can check the menu displayed on the refrigerator, select the suggested ingredients, and begin cooking. They can also request other menu suggestions if needed.

[0367] Input: Menu information displayed on the screen

[0368] Output: Start cooking or request additional menu suggestions

[0369] (Application example 2)

[0370] 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."

[0371] While modern consumers desire a healthy diet, they must select ingredients and plan meals within limited time and information to achieve this goal. Daily emotional states and stress levels influence food choices, but a lack of meal plan suggestions that take these factors into account is problematic. Furthermore, even in brick-and-mortar shopping experiences, there is a lack of systems that can help consumers make optimal food choices while taking their personal health data into account. The objective of this invention is to provide a personalized shopping experience based on each individual's emotional state by proposing optimal meals that combine a user's health data and emotional data.

[0372] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes a means for storing household health data, a means for detecting the types and quantities of ingredients, a means for analyzing nutritional balance based on the household health data and ingredient data, a means for generating a menu based on the analysis results, a means for detecting the user's emotional state and including that data in the analysis, and a means for displaying the generated menu. This enables optimal menu suggestions based on the user's health and emotional state. Furthermore, even in a physical store shopping experience, personalized ingredient selection and menu suggestions based on health and emotions can be made possible by displaying the information on the user's smartphone, smart glasses, or in-store digital signage.

[0373] "Household health data" refers to health-related data such as the height, weight, blood pressure, and blood test data of each member of the household.

[0374] "Type and quantity of ingredients" is information on the name of a specific ingredient and its corresponding quantity.

[0375] "Means for analyzing nutritional balance" refers to techniques and methods for analyzing nutrient balance based on acquired health data and food ingredient data.

[0376] "Means for generating a menu based on the analysis results" refers to techniques and methods for determining the optimal combination of ingredients and cooking methods from the results of the nutritional balance analysis, and creating a specific menu.

[0377] The "means for displaying the generated menu" refers to a terminal or screen display technology for visually presenting the generated menu information to the user.

[0378] "Means for detecting the user's emotional state and including that data in the analysis" refers to technology or methods that detect the user's facial expression, tone of voice, etc., analyze their current emotional state based on that, and reflect this information in the nutritional balance analysis.

[0379] A "server" is a large-capacity data processing device that receives, stores, and analyzes data via the Internet.

[0380] A "client terminal" is an electronic device operated by a user to receive and display data from a server.

[0381] A "refrigerator" is an electrical device used to store food at low temperatures, and has cameras and sensors inside it that detect and transmit food ingredient data.

[0382] "Cameras and sensors" are imaging and sensing devices for detecting physical information such as images and weight.

[0383] This invention relates to a system for generating menus that optimize nutritional balance by combining a user's health data and emotional data. The system is composed of a terminal used in the home and a server that analyzes the data and generates the menus.

[0384] First, the device functions as a refrigerator, smartphone, smart glasses, digital signage in a physical store, etc., and collects the user's health data and food ingredient data. Specifically, it uses a camera and sensors to detect the type and quantity of ingredients in the refrigerator and sends this data to a server via the Internet. It also uses an emotion engine to analyze the user's facial expressions and tone of voice, recognizing their current emotional state and converting it into data.

[0385] The server receives the ingredient data and emotion data sent from the device. At the same time, it also references the household health data stored in a cloud database. Based on this data, it generates an optimal menu, taking into account nutritional balance and the user's emotional state.

[0386] The generated menus are displayed on the user's smartphone, smart glasses, refrigerator display, or in-store digital signage, making it easier for users to manage their ingredients and receive optimal menu suggestions based on their health and emotions.

[0387] Specific data processing techniques include image recognition of ingredients using Google Cloud Vision API, emotion analysis using Emotion API, and data aggregation and analysis using Firebase. Based on the analyzed data, the AI ​​generates an optimal menu, which is then notified and displayed to the user.

[0388] Examples of concrete examples and prompts

[0389] For example, if a user enters a supermarket and launches a smartphone app, the emotion engine may determine that the user is tired. If the store's camera system detects fresh ingredients such as tomatoes and avocados, the AI ​​can integrate this data and suggest the following menu items:

[0390] Breakfast: Tomato and avocado salad

[0391] Lunch: Vitamin C-rich tomato soup

[0392] Dinner: Comforting avocado pasta

[0393] An example of a prompt is as follows:

[0394] If the user is tired and has tomatoes and avocados on hand, the following menu will be suggested:

[0395] Breakfast: Tomato and avocado salad

[0396] Lunch: Vitamin C-rich tomato soup

[0397] Dinner: Comforting avocado pasta

[0398] In this way, users can receive optimal menu suggestions based on their emotional and health states, enabling them to lead an efficient and healthy diet.

[0399] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0400] Step 1:

[0401] A user adds ingredients to the refrigerator. The refrigerator's built-in camera and weight sensor detect the ingredients and use image recognition technology (e.g., Google Cloud Vision API) to identify the ingredient names and quantities. This information is generated as ingredient data.

[0402] Input: Adding ingredients to the refrigerator

[0403] Output: Food ingredient data (product name, quantity, expiration date)

[0404] Step 2:

[0405] The device (refrigerator) sends the generated ingredient data to the server. At the same time, the emotion engine analyzes the user's facial expressions and tone of voice to recognize their current emotional state and transmits it to the server.

[0406] Input: Food ingredients data, emotional data (facial expressions, voice tone analysis results)

[0407] Output: Ingredient data and emotion data sent to the server

[0408] Step 3:

[0409] The server receives the food ingredient data and emotion data sent from the device and references household health data (e.g., height, weight, blood pressure, blood test data) from a cloud database.

[0410] Input: food data, emotion data, health data

[0411] Output: Consolidated data for analysis

[0412] Step 4:

[0413] The server analyzes the nutritional balance based on the integrated data and generates an optimal menu that takes into account the user's health and emotional state. The AI ​​model used in this process analyzes the nutritional balance and emotional state and generates the menu.

[0414] Input: Integrated data (food data, emotion data, health data)

[0415] Output: Optimal menu plan

[0416] Step 5:

[0417] The server transmits the generated menu plan to a terminal, such as a smartphone, smart glasses, a refrigerator display, or in-store digital signage.

[0418] Input: Optimal menu plan

[0419] Output: Menu plan sent to device

[0420] Step 6:

[0421] Users can check the menu plan displayed on the device, select the suggested ingredients, and begin cooking. If shopping at a physical store, they can also use the in-store digital signage or a smartphone app to select ingredients to purchase.

[0422] Input: Menu plan displayed on the device

[0423] Output: User action (picking ingredients, cooking, or purchasing)

[0424] 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.

[0425] 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.

[0426] 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.

[0427] [Second embodiment]

[0428] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0429] 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.

[0430] 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).

[0431] 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.

[0432] 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.

[0433] 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).

[0434] 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.

[0435] 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.

[0436] 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.

[0437] 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.

[0438] 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.

[0439] 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."

[0440] This invention is a system that combines household health data and food ingredient data stored in the refrigerator to create a menu that optimizes nutritional balance. Specifically, it stores household health data, detects the types and quantities of ingredients, analyzes nutritional balance based on this data, and creates an optimal menu. Specific embodiments of this system are described below.

[0441] Program processing explanation

[0442] Data collection and transmission

[0443] First, a user adds a new ingredient to the refrigerator. For example, say they put carrots in the refrigerator. The device (refrigerator) automatically detects this new ingredient using its built-in camera and weight sensor. The device uses image recognition technology to identify the ingredient name "carrots" and the quantity. This generates ingredient data such as "Item name: carrots, quantity: 3, expiration date: 2023-10-15." The device then sends this data to a server via the Internet.

[0444] Data analysis and menu generation

[0445] The server receives the food ingredient data sent from the device. At the same time, it references the family's health data stored in a cloud database. This includes each family member's height, weight, blood pressure, and blood test data. The server analyzes the nutritional balance based on the received food ingredient and health data. For example, if the family's intake of vitamin A is insufficient, it will suggest a meal plan to supplement that.

[0446] The server uses an AI algorithm to analyze health and food data and generate an optimal menu, such as "Breakfast: Carrot smoothie, Lunch: Carrot and chicken salad, Dinner: Carrot stew."

[0447] Menu notification and display

[0448] The server sends the generated menu information to the terminal. The menu is displayed on the refrigerator display for the user to check. The user checks the suggested menu on the refrigerator display, takes out the necessary ingredients, and starts cooking.

[0449] Specific examples

[0450] Let's say a user puts a tomato in the refrigerator. The following steps occur:

[0451] 1. Add ingredients

[0452] The user puts tomatoes in the refrigerator.

[0453] 2. Data detection and transmission

[0454] The device detects tomatoes, generates data such as "Product name: Tomato, Quantity: 5, Best before date: 2023-10-20", and sends it to the server.

[0455] 3. Data analysis and menu generation

[0456] The server analyzes the received data and the household's health data and generates a menu such as "Lunch: Tomato salad, Dinner: Tomato pasta."

[0457] 4. Notification and display of menu

[0458] The generated menu information is sent to a terminal and displayed on the refrigerator display.

[0459] This reduces the burden on users of managing ingredients and planning healthy meals, allowing them to lead an efficient and healthy diet.

[0460] The processing flow will be explained below.

[0461] Step 1:

[0462] The user adds ingredients to the refrigerator. The user puts "carrots" in the refrigerator.

[0463] Step 2:

[0464] The device (refrigerator) recognizes the ingredients. The device's built-in camera and weight sensor detect "carrots." Image recognition and barcode scanning technology are used to identify the type and quantity of ingredients.

[0465] Step 3:

[0466] The device (refrigerator) generates food ingredient data, such as "Item name: carrots, quantity: 3, expiration date: 2023-10-15" based on the recognized ingredients.

[0467] Step 4:

[0468] The device (refrigerator) sends the food ingredient data to the server, which then sends the data to the server via the Internet.

[0469] Step 5:

[0470] The server receives the ingredient data. The server receives the data sent from the terminal.

[0471] Step 6:

[0472] The server references the family's health data and obtains the family's health data (height, weight, blood pressure, blood test data, etc.) from the cloud database.

[0473] Step 7:

[0474] The server analyzes food ingredient and health data, using an AI algorithm to analyze the data taking into account nutritional balance and the health status of family members.

[0475] Step 8:

[0476] The server generates the optimal menu, such as "Breakfast: Carrot smoothie, Lunch: Carrot and chicken salad, Dinner: Carrot stew" based on the analysis results.

[0477] Step 9:

[0478] The server sends the generated menu to the terminal. The menu information is sent to the terminal.

[0479] Step 10:

[0480] The terminal (refrigerator) displays the menu. The generated menu is displayed on the screen and the user can check it.

[0481] Step 11:

[0482] The user checks the menu. The user checks the menu displayed on the screen.

[0483] Step 12:

[0484] The user starts cooking. The user follows the suggested menu, selects the necessary ingredients, and starts cooking.

[0485] Example 1

[0486] 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."

[0487] Currently, menu suggestion systems on the market lack the ability to generate menus that fully consider the user's health status. They also lack mechanisms for efficiently managing ingredients in the home, making it difficult for users to lead a healthy diet without hassle. This invention aims to solve these problems by automatically generating and presenting optimal menus that take into consideration the management of ingredients in the home and health.

[0488] 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.

[0489] In this invention, the server includes means for detecting the types and quantities of ingredients, means for generating ingredient data using image recognition technology and weight sensors, means for transmitting the ingredient data to the server via the Internet, means for the server to analyze the health data and ingredient data within the household and generate an optimal menu using an AI algorithm, and means for displaying the generated menu on the refrigerator display. This allows users to easily obtain an optimal menu based on the health data and ingredient status.

[0490] "Household health data" refers to information that indicates the individual health status of family members, such as their height, weight, blood pressure, and blood test data.

[0491] "Type and quantity of food ingredients" is information indicating the specific names and quantities of food items stored in the household.

[0492] "Image recognition technology" is a technology for analyzing images captured by a camera and identifying objects contained within them.

[0493] A "weight sensor" is a device for measuring the weight of an object.

[0494] "Food ingredient data" is a series of information including the type, quantity, expiration date, etc. of food ingredients.

[0495] "Means for transmitting ingredient data to a server via the Internet" refers to a method or system for transmitting ingredient data obtained via the Internet to a remote server.

[0496] An "AI algorithm" is a calculation procedure or rule that uses artificial intelligence technology to analyze input data and generate optimal results.

[0497] A "menu" is a meal plan that combines multiple meal menus.

[0498] A "refrigerator display" is a screen installed on a refrigerator, and is a device for displaying information to a user.

[0499] A "server" is a central processing unit for storing data, analyzing data, and providing information to other devices.

[0500] MODE FOR CARRYING OUT THE INVENTION

[0501] This invention is a system that combines household health data and food ingredient data stored in a refrigerator to create a menu that optimizes nutritional balance. Specific embodiments of this system are described below.

[0502] Data collection and transmission

[0503] A user adds a new ingredient to the refrigerator. For example, suppose the user puts carrots in the refrigerator. The device (refrigerator) automatically detects this new ingredient using its built-in camera and weight sensor. Specifically, the camera inside the refrigerator takes a picture of the carrots, and the sensor measures their weight. The device uses image recognition technology (e.g., TensorFlow) to identify the ingredient and generates data such as "Item name: carrots, quantity: 3, expiration date: 2023-10-15." The generated data is sent to a server via the Internet.

[0504] Data analysis and menu generation

[0505] The server receives the ingredient data sent by the user and stores it in a cloud database. At the same time, it retrieves the family's health data (height, weight, blood pressure, blood test data, etc.) from the cloud database. The server analyzes nutritional balance based on the health data and ingredient data. For example, if all family members are deficient in vitamin A, it generates a menu to make up for that deficiency. The server uses an AI algorithm (e.g., OpenAI's GPT-3) to suggest the optimal menu based on the health data and ingredient data. As a specific example, it generates "Breakfast: Carrot smoothie, Lunch: Carrot and chicken salad, Dinner: Carrot stew."

[0506] Menu notification and display

[0507] The server sends the generated menu information to the terminal (refrigerator). The terminal displays the received menu information on the refrigerator's display. The user checks this display, selects ingredients based on the proposed menu, and begins cooking.

[0508] Specific examples

[0509] For example, if the user puts "tomatoes" in the refrigerator, the system behaves as follows:

[0510] 1. Add ingredients

[0511] The user puts tomatoes in the refrigerator.

[0512] 2. Data detection and transmission

[0513] The device detects tomatoes, generates data such as "Product name: Tomato, Quantity: 5, Best before date: 2023-10-20" and sends it to the server.

[0514] 3. Data analysis and menu generation

[0515] The server analyzes the received data and the household's health data and generates a menu of "Lunch: Tomato salad, Dinner: Tomato pasta."

[0516] 4. Notification and display of menu

[0517] The generated menu information is sent to a terminal and displayed on the refrigerator display.

[0518] This reduces the burden on users of managing ingredients and planning healthy meals, allowing them to lead an efficient and healthy diet.

[0519] Prompt Sentence Examples

[0520] By inputting the following prompts into the generative AI model, optimal meal suggestions can be obtained:

[0521] A new ingredient has been added to your refrigerator. Please use the following data to create a meal plan that takes into account your family's health status.

[0522] Ingredients: Tomato

[0523] Quantity: 5 pieces

[0524] Best before date: 2023-10-20

[0525] Health data: The whole family has low vitamin A intake.

[0526] Please suggest one of the generated menus for breakfast, one for lunch, and one for dinner.

[0527] This invention combines the latest image recognition technology with AI algorithms to realize in-home food management and health-optimized meal planning, allowing users to lead a healthy diet without any hassle.

[0528] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0529] The flow of this system's program processing

[0530] Step 1: Adding and detecting ingredients

[0531] The user adds a new ingredient to the refrigerator. For example, the user adds "carrots" to the refrigerator.

[0532] Input: New ingredient (carrot)

[0533] Operation:

[0534] The device (refrigerator) takes a picture of the carrot using its built-in camera.

[0535] A weight sensor measures the weight of the carrot.

[0536] Data processing:

[0537] Image data acquired by the camera and weight data acquired by the weight sensor are stored in the device's internal memory.

[0538] Output: Image data and weight data

[0539] Step 2: Generate ingredient data

[0540] The device generates ingredient data using image recognition technology.

[0541] Input: Image data and weight data

[0542] Operation:

[0543] The device uses image recognition software (e.g., TensorFlow) to analyze the captured image and identify the name of the ingredient (carrot).

[0544] Determine the quantity of carrots based on the data from the weight sensor.

[0545] Data processing:

[0546] Formatted data including the ingredient name, quantity, and expiration date is generated (e.g., "Item name: Carrots, Quantity: 3, Expiration date: 2023-10-15").

[0547] Output: Food ingredient data (product name, quantity, expiration date)

[0548] Step 3: Submit ingredient data

[0549] The generated ingredient data is transmitted to a server via the Internet.

[0550] Input: Food ingredient data (product name, quantity, expiration date)

[0551] Operation:

[0552] The terminal transmits the ingredient data to a server via the Internet.

[0553] Output: Ingredient data sent to the server

[0554] Step 4: Acquire and analyze health data

[0555] The server collects health data from within the home and analyzes it along with food ingredient data.

[0556] Input: Food ingredient data (product name, quantity, expiration date), household health data (height, weight, blood pressure, blood test data)

[0557] Operation:

[0558] The server retrieves home health data from a cloud database.

[0559] The server combines the health data with the newly acquired food ingredient data and performs the analysis.

[0560] Data processing:

[0561] Analyze nutritional balance and evaluate the relationship between the family's nutritional status and new ingredients.

[0562] Output: Nutritional balance analysis results

[0563] Step 5: Create a menu

[0564] The server uses AI algorithms to generate the optimal menu.

[0565] Input: Nutritional balance analysis results

[0566] Operation:

[0567] The server uses a generative AI model (e.g., OpenAI's GPT-3) to generate the optimal menu based on the analysis results.

[0568] For example, to increase your intake of vitamin A, we suggest a menu such as "Breakfast: Carrot smoothie, Lunch: Carrot and chicken salad, Dinner: Carrot stew."

[0569] Data processing:

[0570] Health data and food ingredient data are integrated to generate optimized menus.

[0571] Output: Generated menu (specific menus for breakfast, lunch, and dinner)

[0572] Step 6: Submit and view your menu

[0573] The generated menu information is sent to be displayed on the refrigerator display.

[0574] Input: Generated menu

[0575] Operation:

[0576] The server then transmits the generated menu information to the terminal (refrigerator) again via the Internet.

[0577] The device displays the received menu information on the refrigerator's display.

[0578] Output: Menu displayed on the refrigerator display

[0579] Specific examples

[0580] The specific flow of processing when the user puts "tomatoes" in the refrigerator is as follows:

[0581] 1. Add ingredients

[0582] The user puts tomatoes in the refrigerator.

[0583] 2. Data detection and transmission

[0584] The device takes a picture of the tomato with its built-in camera, and a weight sensor measures the tomato's weight.

[0585] Using image recognition technology, "tomato" is identified and the following data is generated: "Product name: Tomato, Quantity: 5, Best before date: 2023-10-20."

[0586] The generated data is sent to the server.

[0587] 3. Data analysis and menu generation

[0588] The server receives the food ingredient data and retrieves the health data from the cloud database.

[0589] Analyze health data and food ingredient data to generate a menu of "Lunch: Tomato salad, Dinner: Tomato pasta."

[0590] 4. Notification and display of menu

[0591] The generated menu information is sent to be displayed on the refrigerator display.

[0592] The user checks the displayed menu, selects the necessary ingredients, and begins cooking.

[0593] keyword

[0594] Generative AI model, prompt sentence

[0595] (Application example 1)

[0596] 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."

[0597] While there have been systems that combine food data from a home refrigerator with health data to generate nutritionally balanced menus, there have been no systems that integrate food data from a commercial facility with food products purchased at home and generate optimal menus based on health data from the home. This has made it difficult for consumers to select products that are optimal for their health. The present invention aims to solve these problems and provide a system that provides personalized nutrition advice and product suggestions to consumers.

[0598] 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.

[0599] In this invention, the server includes means for storing health data in the home, means for detecting the types and quantities of ingredients, means for analyzing nutritional balance based on the health data and ingredient data in the home, means for generating menus based on the analysis results, means for displaying the generated menus, and means for combining products purchased in commercial facilities with ingredient data in the home to propose optimal menus. This enables consumers to select products that are optimal for their health condition and lead an efficient and healthy diet.

[0600] A "household health data storage means" is a storage device or database for storing health information (e.g., height, weight, blood pressure, blood test results, etc.) for each family member.

[0601] "Means for detecting the type and quantity of ingredients" refers to devices or technologies that automatically recognize the type and quantity of ingredients using cameras or sensors inside the refrigerator.

[0602] The "means for analyzing nutritional balance based on household health data and food ingredient data" refers to a processor or algorithm that analyzes the collected health data and food ingredient data and performs calculations to optimize the nutritional balance of each family member.

[0603] The "means for generating a menu based on the analysis results" is a system that suggests meal menus suitable for each family member based on the results of nutritional analysis.

[0604] The "means for displaying the generated menu" is a device equipped with a display or screen that displays the generated menu information so that the user can check it.

[0605] "A means of combining products purchased in a commercial facility with food ingredient data from the home to suggest the optimal menu" is a system that obtains data on products that consumers are planning to purchase in a commercial facility, analyzes this data together with food ingredient data from the home and household health data, and suggests the optimal menu.

[0606] This invention is a system that integrates the products a consumer is planning to purchase in a physical store with food data stored in the home refrigerator and household health data to propose a menu that takes into account optimal nutritional balance. Detailed embodiments of this system are described below.

[0607] System Configuration

[0608] The system consists of the following main components:

[0609] Client terminal: A device such as a smartphone or robot

[0610] Server: A central system that integrates data and performs analysis

[0611] Storage device: A database that stores health data within the home

[0612] Hardware and software used

[0613] Hardware: smartphones, robot cameras, sensors

[0614] Software: OpenCV (image processing library), Requests (HTTP request library)

[0615] Data collection and transmission

[0616] When a user scans an item in a commercial facility, the device collects information about the item and acquires the appropriate data using the device's built-in camera and sensors. The device then transmits the collected data to a server via the Internet. Specifically, image recognition technology is used to identify the product name and quantity. OpenCV is used for this process.

[0617] Data analysis and menu generation

[0618] The server receives product data sent from the device, food data in the home refrigerator, and household health data. Based on the received data, the server uses an AI algorithm to analyze nutritional balance and generate optimal menus. This analysis uses a generative AI model and analyzes the results.

[0619] Menu notification and display

[0620] The server sends the generated menu information to the device, which then presents this information to the user, who can then check the optimal menu. For example, the menu is displayed on a smartphone or robot display.

[0621] Specific examples

[0622] 1. Data collection and transmission:

[0623] A user attempts to purchase "tomatoes" at a commercial facility.

[0624] The tomato is scanned with the smartphone camera, data such as "Item name: Tomato, Quantity: 5" is generated and sent to the server.

[0625] 2. Data analysis and menu generation:

[0626] The server analyzes the received tomato data, household health data, and data on other ingredients in the refrigerator, and generates a menu such as "Lunch: Tomato salad, Dinner: Tomato pasta."

[0627] 3. Menu Notification and Display:

[0628] The generated menu information is sent to a smartphone and displayed for the user to review.

[0629] Prompt Sentence Examples

[0630] "Could you tell me how much food you have in the fridge?"

[0631] "How can I get optimal vitamin A?"

[0632] The system allows users to take their household health data into account when choosing what to buy, enabling them to lead an efficient and healthy diet.

[0633] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0634] Step 1:

[0635] Data collection

[0636] A user scans a product they are about to purchase at a commercial facility. The device (smartphone or robot) uses a camera to take a picture of the product (e.g., tomatoes) and acquires image data. This image data becomes the input. Image recognition technology (e.g., OpenCV) is used to identify the product name (tomatoes) and quantity (5 pieces). As a result, data such as "Product name: tomatoes, quantity: 5 pieces" is output.

[0637] Step 2:

[0638] Data transmission

[0639] The terminal sends the product data acquired in step 1 to the server via the Internet. In this step, an HTTP request library (e.g., Requests) is used to send the product data to the server. The input data is the product name and quantity, which are then sent to the server.

[0640] Step 3:

[0641] Integrating household health data and food ingredient data

[0642] The server retrieves food item data and health data from the cloud database. The inputs are the health data of each household member (e.g., height, weight, blood pressure) and food item data in the refrigerator (e.g., ingredients in stock and their quantities). The server integrates this data and generates a dataset for analysis.

[0643] Step 4:

[0644] Nutritional balance analysis

[0645] The server analyzes nutritional balance based on the product data and household data received in step 2. In this step, a generative AI model is used to analyze the collected data as an input dataset. For example, if the user's vitamin A intake is insufficient, it determines whether it can suggest ingredients and menus to compensate for that. As a result of the analysis, a specific action plan for adjusting nutritional balance is output.

[0646] Step 5:

[0647] Menu generation

[0648] The server generates an optimal menu based on the results of the nutritional balance analysis. The input is the analysis result data, and based on this, it proposes the optimal meal menu for each member of the household. The generated menu (e.g., "Lunch: Tomato salad, Dinner: Tomato pasta") is output.

[0649] Step 6:

[0650] Menu notification

[0651] The server sends the generated menu information to the terminal. In this step, the generated menu data is sent to the terminal using the HTTP protocol. The input is the menu data, and once it is sent to the terminal, the menu is displayed on the terminal's display. This allows the user to check the optimal menu.

[0652] Step 7:

[0653] User Verification

[0654] The user checks the menu displayed on the terminal screen, takes out the necessary ingredients, and begins cooking. The menu information displayed on the terminal is input, and the user checks the ingredients based on this and cooks. Specific actions include taking the necessary ingredients from the refrigerator and cooking as displayed on the screen. Examples of prompt sentences include, "Please tell me how much food is in the refrigerator," and "How can I get the optimal amount of vitamin A?"

[0655] 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.

[0656] This invention combines a system that generates menus that optimize nutritional balance by combining health data within the home and food data stored in the refrigerator, with an emotion engine that recognizes the user's emotions. Specific embodiments of this system are described below.

[0657] Program processing explanation

[0658] Data collection and transmission

[0659] First, the user adds a new ingredient to the refrigerator. For example, let's say the user puts "carrots" in the refrigerator. The device (refrigerator) detects the new ingredient using its built-in camera and weight sensor. The device uses image recognition technology to identify the ingredient name "carrots" and its quantity. This generates ingredient data: "Item name: carrots, quantity: 3, expiration date: 2023-10-15." The device then sends this ingredient data to a server via the Internet.

[0660] The emotion engine also detects the user's facial expressions, tone of voice, and other emotional indicators to recognize their current emotional state. For example, if the user is recognized as "feeling stressed," that emotion data is also sent to the server.

[0661] Data analysis and menu generation

[0662] The server receives the food ingredient data and emotion data sent from the device. At the same time, it references the family's health data stored in a cloud database, including each family member's height, weight, blood pressure, and blood test data. The server analyzes all the received data, taking into account nutritional balance and the user's emotions.

[0663] For example, if the household is lacking in Vitamin A intake and the user is feeling stressed, the server will generate a menu that takes this into account. Based on data from the emotion engine, it will prioritize ingredients and cooking methods that are suitable for reducing stress.

[0664] As a concrete example, the server generates a menu such as "Breakfast: Carrot smoothie (rich in vitamin A, stress-reducing), Lunch: Carrot and chicken salad, Dinner: Carrot stew."

[0665] Menu notification and display

[0666] The server sends the generated menu information to the terminal. The generated menu is displayed on the refrigerator display for the user to check. The user checks the suggested menu on the refrigerator display, takes out the necessary ingredients, and starts cooking.

[0667] Specific examples

[0668] If the user puts tomatoes in the refrigerator and the emotion engine detects the user's stress state, the following process occurs:

[0669] 1. Adding ingredients: The user puts tomatoes in the refrigerator.

[0670] 2. Data detection and transmission: The device detects tomatoes, generates the data "Product name: Tomato, Quantity: 5, Best before date: 2023-10-20", and transmits it to the server. At the same time, the emotion engine detects the user's stress level and transmits the emotion data to the server.

[0671] 3. Data analysis and menu generation: The server analyzes the received data and generates a menu rich in vitamins and minerals that is effective in reducing stress. For example, a specific menu such as "Lunch: Tomato and avocado salad, Dinner: Tomato pasta" is created.

[0672] 4. Menu notification and display: The generated menu information is sent to the terminal and displayed on the refrigerator display.

[0673] In this way, users can be provided with optimal menus based on their food management, health status, and emotional state, enabling them to lead an efficient and healthy diet.

[0674] The processing flow will be explained below.

[0675] Step 1:

[0676] The user adds ingredients to the refrigerator. The user puts "carrots" in the refrigerator.

[0677] Step 2:

[0678] The device (refrigerator) recognizes the ingredients. The device's built-in camera and weight sensor detect "carrots." Image recognition and barcode scanning technology are used to identify the type and quantity of ingredients.

[0679] Step 3:

[0680] The device (refrigerator) generates the ingredient data. Based on the recognized ingredients, the data generated is "Item name: carrots, quantity: 3, expiration date: 2023-10-15".

[0681] Step 4:

[0682] The terminal (refrigerator) sends food ingredient data to the server, which then sends this data to the server via the Internet.

[0683] Step 5:

[0684] The emotion engine of the device (refrigerator) recognizes the user's emotions. It uses facial recognition and voice analysis technology using a camera and microphone to identify the user's emotional state (for example, to recognize that the user is feeling stressed).

[0685] Step 6:

[0686] The device (refrigerator) sends the recognized emotion data to a server. The emotional state data is then sent to the server via the internet.

[0687] Step 7:

[0688] The server receives the ingredient data and emotion data. The server receives the ingredient data and emotion data transmitted from the terminal.

[0689] Step 8:

[0690] The server references the family's health data and retrieves the family's health data (height, weight, blood pressure, blood test data, etc.) stored in the cloud database.

[0691] Step 9:

[0692] The server analyzes food ingredient data, emotional data, and health data, and uses AI algorithms to analyze the data taking into account nutritional balance, the health of family members, and the user's emotional state.

[0693] Step 10:

[0694] The server generates an optimal menu based on the analysis results, such as "Breakfast: Carrot smoothie (rich in vitamin A, effective in reducing stress)," "Lunch: Carrot and chicken salad," and "Dinner: Carrot stew."

[0695] Step 11:

[0696] The server sends the generated menu to the terminal, and the menu information is sent to the terminal via the Internet.

[0697] Step 12:

[0698] The terminal (refrigerator) displays the menu. The generated menu is displayed on the screen and the user can check it.

[0699] Step 13:

[0700] The user checks the menu. The user checks the menu displayed on the screen.

[0701] Step 14:

[0702] The user starts cooking. The user follows the suggested menu, selects the necessary ingredients, and starts cooking.

[0703] Example 2

[0704] 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."

[0705] In order to efficiently manage ingredients and health at home, conventional systems generally create menus by considering only ingredient and health data. However, this method cannot propose menus that take into account the user's emotional state, making it difficult to provide menus that are truly satisfying to the user. In particular, users who are feeling stressed need menu proposals that are not only nutritionally balanced but also psychologically satisfying.

[0706] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0707] In this invention, the server includes means for storing household health data, means for detecting the types and quantities of ingredients, means for detecting the user's emotional state, means for analyzing nutritional balance based on the household health data, ingredient data, and emotional data, means for generating menus based on the analysis results, and means for displaying the generated menus. This makes it possible to propose menus that take into account not only the user's health state but also their emotional state at any given time, thereby increasing user satisfaction.

[0708] "Household health data" refers to information about each household member's individual health status, such as their height, weight, blood pressure, and blood test results.

[0709] "Type and quantity of ingredients" is information about the name and quantity of each ingredient present in the refrigerator.

[0710] "User's emotional state" is information indicating the user's psychological state obtained from the user's facial expression, tone of voice, behavior, etc.

[0711] An "emotion engine" is a system or software that analyzes emotional indicators such as a user's facial expressions and tone of voice to identify the user's emotional state.

[0712] "Means for analyzing nutritional balance" refers to the process of evaluating ingredients and nutritional balance based on collected data to find the optimal combination of nutrients.

[0713] The "means for generating a menu" is a system or software that suggests an optimal meal plan to the user based on the collected health data, ingredient data, and emotional data.

[0714] The "means for displaying the generated menu" is a display or other display device that allows the user to visually confirm the proposed menu.

[0715] "Food data" refers to information such as the type, quantity, and expiration date of food ingredients stored in the refrigerator.

[0716] A "server" is a computer system that receives, analyzes, and stores data over the Internet.

[0717] A "client terminal" is a device that can be directly operated by a user, such as a refrigerator or a tablet.

[0718] "Transmitting over the Internet" is the process of transmitting data over a network to a server.

[0719] The "cameras and sensors" are video capture devices and weight measurement devices installed inside the refrigerator.

[0720] This invention combines a system that generates menus that optimize nutritional balance by combining health data within the home and food data stored in the refrigerator, with an emotion engine that recognizes the user's emotions. Specific embodiments of this system are described below.

[0721] Data collection and transmission

[0722] First, a user adds a new ingredient to the refrigerator. For example, suppose a user puts "carrots" in the refrigerator. The device (refrigerator) detects the new ingredient using its built-in camera and weight sensor. Specifically, the camera captures video of the inside of the refrigerator and identifies the "carrots" using image recognition technology. The weight sensor measures the weight of the carrots. This generates ingredient data such as "Item name: carrots, quantity: 3, expiration date: 2023-10-15." This ingredient data is sent to a server via the Internet.

[0723] In addition, the emotion engine analyzes the user's facial expressions and tone of voice to recognize their emotional state. For example, when a user holds their face close to the refrigerator or operates it with their voice, the emotion engine recognizes that the user is "feeling stressed." This emotion data is also sent to the server.

[0724] Data analysis and menu generation

[0725] The server receives the food ingredient and emotion data sent from the device and also references the family's health data stored in a cloud database, including each family member's height, weight, blood pressure, and blood test results.

[0726] The server analyzes all the data it receives, taking into account nutritional balance and the user's emotions. For example, if the household's intake of vitamin A is insufficient and the user is feeling stressed, it will generate a menu using ingredients that are rich in vitamin A and have stress-reducing effects. Specifically, it will suggest dishes such as "Breakfast: Carrot smoothie, Lunch: Carrot and chicken salad, Dinner: Carrot stew."

[0727] Menu notification and display

[0728] The generated menu information is sent from the server to the terminal. The generated menu is displayed on the refrigerator display for the user to check. The user checks the suggested menu on the display, selects the necessary ingredients, and begins cooking.

[0729] Specific examples

[0730] For example, if a user puts tomatoes in the refrigerator and the emotion engine detects the user's stress state, the following process occurs:

[0731] 1. Adding ingredients: The user puts tomatoes in the refrigerator.

[0732] 2. Data detection and transmission: The device detects tomatoes, generates the data "Product name: Tomato, Quantity: 5, Best before date: 2023-10-20", and transmits it to the server. At the same time, the emotion engine detects the user's stress level and transmits the emotion data to the server.

[0733] 3. Data analysis and menu generation: The server analyzes the received data and generates a menu rich in vitamins and minerals that is effective in reducing stress. For example, a menu such as "Lunch: Tomato and avocado salad, Dinner: Tomato pasta" might be created.

[0734] 4. Menu notification and display: The generated menu information is sent to the terminal and displayed on the refrigerator display.

[0735] Example prompts to input to the generative AI model

[0736] When a user adds "broccoli" to the refrigerator, the emotion engine detects the user's happiness state. Combine this with the family's health data to generate appropriate menus. For example, please provide specific menu suggestions for breakfast, lunch, and dinner.

[0737] Using this prompt, the generative AI model can make specific menu suggestions.

[0738] In this way, users can manage their ingredients and be provided with optimal menus that take into account their health and emotional state, enabling them to lead an efficient, satisfying and healthy diet.

[0739] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0740] Step 1:

[0741] Add ingredients

[0742] The user adds a new ingredient to the refrigerator, for example, "carrots," which triggers data collection in the next step.

[0743] Input: New ingredient (e.g. carrot)

[0744] Output: Ingredients added to the refrigerator

[0745] Step 2:

[0746] Data detection and transmission

[0747] The device detects new ingredients using its built-in camera and weight sensor. Specifically, the camera captures video of the inside of the refrigerator and identifies "carrots" using image recognition technology. At the same time, the weight sensor measures the weight of the added ingredient. This data is compiled as "Item name: Carrots, Quantity: 3, Best before date: 2023-10-15" and sent to a server via the Internet.

[0748] Input: Video data inside the refrigerator, weight data

[0749] Output: Food data (product name: carrots, quantity: 3, expiration date: 2023-10-15)

[0750] Step 3:

[0751] Collecting and transmitting emotional data

[0752] The emotion engine analyzes the user's facial expressions and tone of voice to recognize their emotional state. For example, it collects voice data when the user approaches the refrigerator or talks to it. If the analysis determines that the user is feeling stressed, that emotional data is also sent to the server.

[0753] Input: User's facial expression data, voice data

[0754] Output: Emotion data (e.g., feeling stressed)

[0755] Step 4:

[0756] Data analysis and menu generation

[0757] The server receives the food ingredient data and emotion data sent from the device. At the same time, it also references the family's health data stored in a cloud database, including each family member's height, weight, blood pressure, and blood test results. The server then analyzes all the received data, taking into account nutritional balance and the user's emotions.

[0758] For example, if the user is experiencing stress and their household is lacking in Vitamin A, the system will generate a menu using ingredients that are rich in Vitamin A and have a stress-reducing effect. As a result, the system will suggest dishes such as "Breakfast: Carrot smoothie, Lunch: Carrot and chicken salad, Dinner: Carrot stew."

[0759] Input: Food data, emotion data, health data

[0760] Output: Generated menu (e.g. Breakfast: Carrot smoothie, Lunch: Carrot and chicken salad, Dinner: Carrot stew)

[0761] Step 5:

[0762] Menu notification and display

[0763] The generated menu information is sent from the server to the terminal, which then displays the received information on the refrigerator display so that the user can check it.

[0764] Input: Generated menu information

[0765] Output: Menu displayed on the screen

[0766] Step 6:

[0767] User operations

[0768] The user can check the menu displayed on the refrigerator, select the suggested ingredients, and begin cooking. They can also request other menu suggestions if needed.

[0769] Input: Menu information displayed on the screen

[0770] Output: Start cooking or request additional menu suggestions

[0771] (Application example 2)

[0772] 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."

[0773] While modern consumers desire a healthy diet, they must select ingredients and plan meals within limited time and information to achieve this goal. Daily emotional states and stress levels influence food choices, but a lack of meal plan suggestions that take these factors into account is problematic. Furthermore, even in brick-and-mortar shopping experiences, there is a lack of systems that can help consumers make optimal food choices while taking their personal health data into account. The objective of this invention is to provide a personalized shopping experience based on each individual's emotional state by proposing optimal meals that combine a user's health data and emotional data.

[0774] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes a means for storing household health data, a means for detecting the types and quantities of ingredients, a means for analyzing nutritional balance based on the household health data and ingredient data, a means for generating a menu based on the analysis results, a means for detecting the user's emotional state and including that data in the analysis, and a means for displaying the generated menu. This enables optimal menu suggestions based on the user's health and emotional state. Furthermore, even in a physical store shopping experience, personalized ingredient selection and menu suggestions based on health and emotions can be made possible by displaying the information on the user's smartphone, smart glasses, or in-store digital signage.

[0775] "Household health data" refers to health-related data such as the height, weight, blood pressure, and blood test data of each member of the household.

[0776] "Type and quantity of ingredients" is information on the name of a specific ingredient and its corresponding quantity.

[0777] "Means for analyzing nutritional balance" refers to techniques and methods for analyzing nutrient balance based on acquired health data and food ingredient data.

[0778] "Means for generating a menu based on the analysis results" refers to techniques and methods for determining the optimal combination of ingredients and cooking methods from the results of the nutritional balance analysis, and creating a specific menu.

[0779] The "means for displaying the generated menu" refers to a terminal or screen display technology for visually presenting the generated menu information to the user.

[0780] "Means for detecting the user's emotional state and including that data in the analysis" refers to technology or methods that detect the user's facial expression, tone of voice, etc., analyze their current emotional state based on that, and reflect this information in the nutritional balance analysis.

[0781] A "server" is a large-capacity data processing device that receives, stores, and analyzes data via the Internet.

[0782] A "client terminal" is an electronic device operated by a user to receive and display data from a server.

[0783] A "refrigerator" is an electrical device used to store food at low temperatures, and has cameras and sensors inside it that detect and transmit food ingredient data.

[0784] "Cameras and sensors" are imaging and sensing devices for detecting physical information such as images and weight.

[0785] This invention relates to a system for generating menus that optimize nutritional balance by combining a user's health data and emotional data. The system is composed of a terminal used in the home and a server that analyzes the data and generates the menus.

[0786] First, the device functions as a refrigerator, smartphone, smart glasses, digital signage in a physical store, etc., and collects the user's health data and food ingredient data. Specifically, it uses a camera and sensors to detect the type and quantity of ingredients in the refrigerator and sends this data to a server via the Internet. It also uses an emotion engine to analyze the user's facial expressions and tone of voice, recognizing their current emotional state and converting it into data.

[0787] The server receives the ingredient data and emotion data sent from the device. At the same time, it also references the household health data stored in a cloud database. Based on this data, it generates an optimal menu, taking into account nutritional balance and the user's emotional state.

[0788] The generated menus are displayed on the user's smartphone, smart glasses, refrigerator display, or in-store digital signage, making it easier for users to manage their ingredients and receive optimal menu suggestions based on their health and emotions.

[0789] Specific data processing includes image recognition of ingredients using Google Cloud Vision API, emotion analysis using Emotion API, and data aggregation and analysis using Firebase. Based on the analyzed data, AI generates an optimal menu and notifies and displays it to the user.

[0790] Examples of concrete examples and prompts

[0791] For example, if a user enters a supermarket and launches a smartphone app, the emotion engine may determine that the user is tired. If the store's camera system detects fresh ingredients such as tomatoes and avocados, the AI ​​can integrate this data and suggest the following menu items:

[0792] Breakfast: Tomato and avocado salad

[0793] Lunch: Vitamin C-rich tomato soup

[0794] Dinner: Comforting avocado pasta

[0795] An example of a prompt is as follows:

[0796] If the user is tired and has tomatoes and avocados on hand, the following menu will be suggested:

[0797] Breakfast: Tomato and avocado salad

[0798] Lunch: Vitamin C-rich tomato soup

[0799] Dinner: Comforting avocado pasta

[0800] In this way, users can receive optimal menu suggestions based on their emotional and health states, enabling them to lead an efficient and healthy diet.

[0801] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0802] Step 1:

[0803] A user adds ingredients to the refrigerator. The refrigerator's built-in camera and weight sensor detect the ingredients and use image recognition technology (e.g., Google Cloud Vision API) to identify the ingredient names and quantities. This information is generated as ingredient data.

[0804] Input: Adding ingredients to the refrigerator

[0805] Output: Food ingredient data (product name, quantity, expiration date)

[0806] Step 2:

[0807] The device (refrigerator) sends the generated ingredient data to the server. At the same time, the emotion engine analyzes the user's facial expressions and tone of voice to recognize their current emotional state and transmits it to the server.

[0808] Input: Food ingredients data, emotional data (facial expressions, voice tone analysis results)

[0809] Output: Ingredient data and emotion data sent to the server

[0810] Step 3:

[0811] The server receives the food ingredient data and emotion data sent from the device and references household health data (e.g., height, weight, blood pressure, blood test data) from a cloud database.

[0812] Input: food data, emotion data, health data

[0813] Output: Consolidated data for analysis

[0814] Step 4:

[0815] The server analyzes the nutritional balance based on the integrated data and generates an optimal menu that takes into account the user's health and emotional state. The AI ​​model used in this process analyzes the nutritional balance and emotional state and generates the menu.

[0816] Input: Integrated data (food data, emotion data, health data)

[0817] Output: Optimal menu plan

[0818] Step 5:

[0819] The server transmits the generated menu plan to a terminal, such as a smartphone, smart glasses, a refrigerator display, or in-store digital signage.

[0820] Input: Optimal menu plan

[0821] Output: Menu plan sent to device

[0822] Step 6:

[0823] Users can check the menu plan displayed on the device, select the suggested ingredients, and begin cooking. If shopping at a physical store, they can also use the in-store digital signage or a smartphone app to select ingredients to purchase.

[0824] Input: Menu plan displayed on the device

[0825] Output: User action (picking ingredients, cooking, or purchasing)

[0826] 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.

[0827] 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.

[0828] 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.

[0829] [Third embodiment]

[0830] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0831] 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.

[0832] 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).

[0833] 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.

[0834] 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.

[0835] 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).

[0836] 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.

[0837] 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.

[0838] 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.

[0839] 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.

[0840] 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.

[0841] 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."

[0842] This invention is a system that combines household health data and food ingredient data stored in the refrigerator to create a menu that optimizes nutritional balance. Specifically, it stores household health data, detects the types and quantities of ingredients, analyzes nutritional balance based on this data, and creates an optimal menu. Specific embodiments of this system are described below.

[0843] Program processing explanation

[0844] Data collection and transmission

[0845] First, a user adds a new ingredient to the refrigerator. For example, say they put carrots in the refrigerator. The device (refrigerator) automatically detects this new ingredient using its built-in camera and weight sensor. The device uses image recognition technology to identify the ingredient name "carrots" and the quantity. This generates ingredient data such as "Item name: carrots, quantity: 3, expiration date: 2023-10-15." The device then sends this data to a server via the Internet.

[0846] Data analysis and menu generation

[0847] The server receives the food ingredient data sent from the device. At the same time, it references the family's health data stored in a cloud database. This includes each family member's height, weight, blood pressure, and blood test data. The server analyzes the nutritional balance based on the received food ingredient and health data. For example, if the family's intake of vitamin A is insufficient, it will suggest a meal plan to supplement that.

[0848] The server uses an AI algorithm to analyze health and food data and generate an optimal menu, such as "Breakfast: Carrot smoothie, Lunch: Carrot and chicken salad, Dinner: Carrot stew."

[0849] Menu notification and display

[0850] The server sends the generated menu information to the terminal. The menu is displayed on the refrigerator display for the user to check. The user checks the suggested menu on the refrigerator display, takes out the necessary ingredients, and starts cooking.

[0851] Specific examples

[0852] Let's say a user puts a tomato in the refrigerator. The following steps occur:

[0853] 1. Add ingredients

[0854] The user puts tomatoes in the refrigerator.

[0855] 2. Data detection and transmission

[0856] The device detects tomatoes, generates data such as "Product name: Tomato, Quantity: 5, Best before date: 2023-10-20", and sends it to the server.

[0857] 3. Data analysis and menu generation

[0858] The server analyzes the received data and the household's health data and generates a menu such as "Lunch: Tomato salad, Dinner: Tomato pasta."

[0859] 4. Notification and display of menu

[0860] The generated menu information is sent to a terminal and displayed on the refrigerator display.

[0861] This reduces the burden on users of managing ingredients and planning healthy meals, allowing them to lead an efficient and healthy diet.

[0862] The processing flow will be explained below.

[0863] Step 1:

[0864] The user adds ingredients to the refrigerator. The user puts "carrots" in the refrigerator.

[0865] Step 2:

[0866] The device (refrigerator) recognizes the ingredients. The device's built-in camera and weight sensor detect "carrots." Image recognition and barcode scanning technology are used to identify the type and quantity of ingredients.

[0867] Step 3:

[0868] The device (refrigerator) generates food ingredient data, such as "Item name: carrots, quantity: 3, expiration date: 2023-10-15" based on the recognized ingredients.

[0869] Step 4:

[0870] The device (refrigerator) sends the food ingredient data to the server, which then sends the data to the server via the Internet.

[0871] Step 5:

[0872] The server receives the ingredient data. The server receives the data sent from the terminal.

[0873] Step 6:

[0874] The server references the family's health data and obtains the family's health data (height, weight, blood pressure, blood test data, etc.) from the cloud database.

[0875] Step 7:

[0876] The server analyzes food ingredient and health data, using an AI algorithm to analyze the data taking into account nutritional balance and the health status of family members.

[0877] Step 8:

[0878] The server generates the optimal menu, such as "Breakfast: Carrot smoothie, Lunch: Carrot and chicken salad, Dinner: Carrot stew" based on the analysis results.

[0879] Step 9:

[0880] The server sends the generated menu to the terminal. The menu information is sent to the terminal.

[0881] Step 10:

[0882] The terminal (refrigerator) displays the menu. The generated menu is displayed on the screen and the user can check it.

[0883] Step 11:

[0884] The user checks the menu. The user checks the menu displayed on the screen.

[0885] Step 12:

[0886] The user starts cooking. The user follows the suggested menu, selects the necessary ingredients, and starts cooking.

[0887] Example 1

[0888] 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."

[0889] Currently, menu suggestion systems on the market lack the ability to generate menus that fully consider the user's health status. They also lack mechanisms for efficiently managing ingredients in the home, making it difficult for users to lead a healthy diet without hassle. This invention aims to solve these problems by automatically generating and presenting optimal menus that take into consideration the management of ingredients in the home and health.

[0890] 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.

[0891] In this invention, the server includes means for detecting the types and quantities of ingredients, means for generating ingredient data using image recognition technology and weight sensors, means for transmitting the ingredient data to the server via the Internet, means for the server to analyze the health data and ingredient data within the household and generate an optimal menu using an AI algorithm, and means for displaying the generated menu on the refrigerator display. This allows users to easily obtain an optimal menu based on the health data and ingredient status.

[0892] "Household health data" refers to information that indicates the individual health status of family members, such as their height, weight, blood pressure, and blood test data.

[0893] "Type and quantity of food ingredients" is information indicating the specific names and quantities of food items stored in the household.

[0894] "Image recognition technology" is a technology for analyzing images captured by a camera and identifying objects contained within them.

[0895] A "weight sensor" is a device for measuring the weight of an object.

[0896] "Food ingredient data" is a series of information including the type, quantity, expiration date, etc. of food ingredients.

[0897] "Means for transmitting ingredient data to a server via the Internet" refers to a method or system for transmitting ingredient data obtained via the Internet to a remote server.

[0898] An "AI algorithm" is a calculation procedure or rule that uses artificial intelligence technology to analyze input data and generate optimal results.

[0899] A "menu" is a meal plan that combines multiple meal menus.

[0900] A "refrigerator display" is a screen installed on a refrigerator, and is a device for displaying information to a user.

[0901] A "server" is a central processing unit for storing data, analyzing data, and providing information to other devices.

[0902] MODE FOR CARRYING OUT THE INVENTION

[0903] This invention is a system that combines household health data and food ingredient data stored in a refrigerator to create a menu that optimizes nutritional balance. Specific embodiments of this system are described below.

[0904] Data collection and transmission

[0905] A user adds a new ingredient to the refrigerator. For example, suppose the user puts carrots in the refrigerator. The device (refrigerator) automatically detects this new ingredient using its built-in camera and weight sensor. Specifically, the camera inside the refrigerator takes a picture of the carrots, and the sensor measures their weight. The device uses image recognition technology (e.g., TensorFlow) to identify the ingredient and generates data such as "Item name: carrots, quantity: 3, expiration date: 2023-10-15." The generated data is sent to a server via the Internet.

[0906] Data analysis and menu generation

[0907] The server receives the ingredient data sent by the user and stores it in a cloud database. At the same time, it retrieves the family's health data (height, weight, blood pressure, blood test data, etc.) from the cloud database. The server analyzes nutritional balance based on the health data and ingredient data. For example, if all family members are deficient in vitamin A, it generates a menu to make up for that deficiency. The server uses an AI algorithm (e.g., OpenAI's GPT-3) to suggest the optimal menu based on the health data and ingredient data. As a specific example, it generates "Breakfast: Carrot smoothie, Lunch: Carrot and chicken salad, Dinner: Carrot stew."

[0908] Menu notification and display

[0909] The server sends the generated menu information to the terminal (refrigerator). The terminal displays the received menu information on the refrigerator's display. The user checks this display, selects ingredients based on the proposed menu, and begins cooking.

[0910] Specific examples

[0911] For example, if the user puts "tomatoes" in the refrigerator, the system behaves as follows:

[0912] 1. Add ingredients

[0913] The user puts tomatoes in the refrigerator.

[0914] 2. Data detection and transmission

[0915] The device detects tomatoes, generates data such as "Product name: Tomato, Quantity: 5, Best before date: 2023-10-20" and sends it to the server.

[0916] 3. Data analysis and menu generation

[0917] The server analyzes the received data and the household's health data and generates a menu of "Lunch: Tomato salad, Dinner: Tomato pasta."

[0918] 4. Notification and display of menu

[0919] The generated menu information is sent to a terminal and displayed on the refrigerator display.

[0920] This reduces the burden on users of managing ingredients and planning healthy meals, allowing them to lead an efficient and healthy diet.

[0921] Prompt Sentence Examples

[0922] By inputting the following prompts into the generative AI model, optimal meal suggestions can be obtained:

[0923] A new ingredient has been added to your refrigerator. Please use the following data to create a meal plan that takes into account your family's health status.

[0924] Ingredients: Tomato

[0925] Quantity: 5 pieces

[0926] Best before date: 2023-10-20

[0927] Health data: The whole family has low vitamin A intake.

[0928] Please suggest one of the generated menus for breakfast, one for lunch, and one for dinner.

[0929] This invention combines the latest image recognition technology with AI algorithms to realize in-home food management and health-optimized meal planning, allowing users to lead a healthy diet without any hassle.

[0930] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0931] The flow of this system's program processing

[0932] Step 1: Adding and detecting ingredients

[0933] The user adds a new ingredient to the refrigerator. For example, the user adds "carrots" to the refrigerator.

[0934] Input: New ingredient (carrot)

[0935] Operation:

[0936] The device (refrigerator) takes a picture of the carrot using its built-in camera.

[0937] A weight sensor measures the weight of the carrot.

[0938] Data processing:

[0939] Image data acquired by the camera and weight data acquired by the weight sensor are stored in the device's internal memory.

[0940] Output: Image data and weight data

[0941] Step 2: Generate ingredient data

[0942] The device generates ingredient data using image recognition technology.

[0943] Input: Image data and weight data

[0944] Operation:

[0945] The device uses image recognition software (e.g., TensorFlow) to analyze the captured image and identify the name of the ingredient (carrot).

[0946] Determine the quantity of carrots based on the data from the weight sensor.

[0947] Data processing:

[0948] Formatted data including the ingredient name, quantity, and expiration date is generated (e.g., "Item name: Carrots, Quantity: 3, Expiration date: 2023-10-15").

[0949] Output: Food ingredient data (product name, quantity, expiration date)

[0950] Step 3: Submit ingredient data

[0951] The generated ingredient data is transmitted to a server via the Internet.

[0952] Input: Food ingredient data (product name, quantity, expiration date)

[0953] Operation:

[0954] The terminal transmits the ingredient data to a server via the Internet.

[0955] Output: Ingredient data sent to the server

[0956] Step 4: Acquire and analyze health data

[0957] The server collects health data from within the home and analyzes it along with food ingredient data.

[0958] Input: Food ingredient data (product name, quantity, expiration date), household health data (height, weight, blood pressure, blood test data)

[0959] Operation:

[0960] The server retrieves home health data from a cloud database.

[0961] The server combines the health data with the newly acquired food ingredient data and performs the analysis.

[0962] Data processing:

[0963] Analyze nutritional balance and evaluate the relationship between the family's nutritional status and new ingredients.

[0964] Output: Nutritional balance analysis results

[0965] Step 5: Create a menu

[0966] The server uses AI algorithms to generate the optimal menu.

[0967] Input: Nutritional balance analysis results

[0968] Operation:

[0969] The server uses a generative AI model (e.g., OpenAI's GPT-3) to generate the optimal menu based on the analysis results.

[0970] For example, to increase your intake of vitamin A, we suggest a menu such as "Breakfast: Carrot smoothie, Lunch: Carrot and chicken salad, Dinner: Carrot stew."

[0971] Data processing:

[0972] Health data and food ingredient data are integrated to generate optimized menus.

[0973] Output: Generated menu (specific menus for breakfast, lunch, and dinner)

[0974] Step 6: Submit and view your menu

[0975] The generated menu information is sent to be displayed on the refrigerator display.

[0976] Input: Generated menu

[0977] Operation:

[0978] The server then transmits the generated menu information to the terminal (refrigerator) again via the Internet.

[0979] The device displays the received menu information on the refrigerator's display.

[0980] Output: Menu displayed on the refrigerator display

[0981] Specific examples

[0982] The specific flow of processing when the user puts "tomatoes" in the refrigerator is as follows:

[0983] 1. Add ingredients

[0984] The user puts tomatoes in the refrigerator.

[0985] 2. Data detection and transmission

[0986] The device takes a picture of the tomato with its built-in camera, and a weight sensor measures the tomato's weight.

[0987] Using image recognition technology, "tomato" is identified and the following data is generated: "Product name: Tomato, Quantity: 5, Best before date: 2023-10-20."

[0988] The generated data is sent to the server.

[0989] 3. Data analysis and menu generation

[0990] The server receives the food ingredient data and retrieves the health data from the cloud database.

[0991] Analyze health data and food ingredient data to generate a menu of "Lunch: Tomato salad, Dinner: Tomato pasta."

[0992] 4. Notification and display of menu

[0993] The generated menu information is sent to be displayed on the refrigerator display.

[0994] The user checks the displayed menu, selects the necessary ingredients, and begins cooking.

[0995] keyword

[0996] Generative AI model, prompt sentence

[0997] (Application example 1)

[0998] 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."

[0999] While there have been systems that combine food data from a home refrigerator with health data to generate nutritionally balanced menus, there have been no systems that integrate food data from a commercial facility with food products purchased at home and generate optimal menus based on health data from the home. This has made it difficult for consumers to select products that are optimal for their health. The present invention aims to solve these problems and provide a system that provides personalized nutrition advice and product suggestions to consumers.

[1000] 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.

[1001] In this invention, the server includes means for storing health data in the home, means for detecting the types and quantities of ingredients, means for analyzing nutritional balance based on the health data and ingredient data in the home, means for generating menus based on the analysis results, means for displaying the generated menus, and means for combining products purchased in commercial facilities with ingredient data in the home to propose optimal menus. This enables consumers to select products that are optimal for their health condition and lead an efficient and healthy diet.

[1002] A "household health data storage means" is a storage device or database for storing health information (e.g., height, weight, blood pressure, blood test results, etc.) for each family member.

[1003] "Means for detecting the type and quantity of ingredients" refers to devices or technologies that automatically recognize the type and quantity of ingredients using cameras or sensors inside the refrigerator.

[1004] The "means for analyzing nutritional balance based on household health data and food ingredient data" refers to a processor or algorithm that analyzes the collected health data and food ingredient data and performs calculations to optimize the nutritional balance of each family member.

[1005] The "means for generating a menu based on the analysis results" is a system that suggests meal menus suitable for each family member based on the results of nutritional analysis.

[1006] The "means for displaying the generated menu" is a device equipped with a display or screen that displays the generated menu information so that the user can check it.

[1007] "A means of combining products purchased in a commercial facility with food ingredient data from the home to suggest the optimal menu" is a system that obtains data on products that consumers are planning to purchase in a commercial facility, analyzes this data together with food ingredient data from the home and household health data, and suggests the optimal menu.

[1008] This invention is a system that integrates the products a consumer is planning to purchase in a physical store with food data stored in the home refrigerator and household health data to propose a menu that takes into account optimal nutritional balance. Detailed embodiments of this system are described below.

[1009] System Configuration

[1010] The system consists of the following main components:

[1011] Client terminal: A device such as a smartphone or robot

[1012] Server: A central system that integrates data and performs analysis

[1013] Storage device: A database that stores health data within the home

[1014] Hardware and software used

[1015] Hardware: smartphones, robot cameras, sensors

[1016] Software: OpenCV (image processing library), Requests (HTTP request library)

[1017] Data collection and transmission

[1018] When a user scans an item in a commercial facility, the device collects information about the item and acquires the appropriate data using the device's built-in camera and sensors. The device then transmits the collected data to a server via the Internet. Specifically, image recognition technology is used to identify the product name and quantity. OpenCV is used for this process.

[1019] Data analysis and menu generation

[1020] The server receives product data sent from the device, food data in the home refrigerator, and household health data. Based on the received data, the server uses an AI algorithm to analyze nutritional balance and generate optimal menus. This analysis uses a generative AI model and analyzes the results.

[1021] Menu notification and display

[1022] The server sends the generated menu information to the device, which then presents this information to the user, who can then check the optimal menu. For example, the menu is displayed on a smartphone or robot display.

[1023] Specific examples

[1024] 1. Data collection and transmission:

[1025] A user attempts to purchase "tomatoes" at a commercial facility.

[1026] The tomato is scanned with the smartphone camera, data such as "Item name: Tomato, Quantity: 5" is generated and sent to the server.

[1027] 2. Data analysis and menu generation:

[1028] The server analyzes the received tomato data, household health data, and data on other ingredients in the refrigerator, and generates a menu such as "Lunch: Tomato salad, Dinner: Tomato pasta."

[1029] 3. Menu Notification and Display:

[1030] The generated menu information is sent to a smartphone and displayed for the user to review.

[1031] Prompt Sentence Examples

[1032] "Could you tell me how much food you have in the fridge?"

[1033] "How can I get optimal vitamin A?"

[1034] The system allows users to take their household health data into account when choosing what to buy, enabling them to lead an efficient and healthy diet.

[1035] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1036] Step 1:

[1037] Data collection

[1038] A user scans a product they are about to purchase at a commercial facility. The device (smartphone or robot) uses a camera to take a picture of the product (e.g., tomatoes) and acquires image data. This image data becomes the input. Image recognition technology (e.g., OpenCV) is used to identify the product name (tomatoes) and quantity (5 pieces). As a result, data such as "Product name: tomatoes, quantity: 5 pieces" is output.

[1039] Step 2:

[1040] Data transmission

[1041] The terminal sends the product data acquired in step 1 to the server via the Internet. In this step, an HTTP request library (e.g., Requests) is used to send the product data to the server. The input data is the product name and quantity, which are then sent to the server.

[1042] Step 3:

[1043] Integrating household health data and food ingredient data

[1044] The server retrieves food item data and health data from the cloud database. The inputs are the health data of each household member (e.g., height, weight, blood pressure) and food item data in the refrigerator (e.g., ingredients in stock and their quantities). The server integrates this data and generates a dataset for analysis.

[1045] Step 4:

[1046] Nutritional balance analysis

[1047] The server analyzes nutritional balance based on the product data and household data received in step 2. In this step, a generative AI model is used to analyze the collected data as an input dataset. For example, if the user's vitamin A intake is insufficient, it determines whether it can suggest ingredients and menus to compensate for that. As a result of the analysis, a specific action plan for adjusting nutritional balance is output.

[1048] Step 5:

[1049] Menu generation

[1050] The server generates an optimal menu based on the results of the nutritional balance analysis. The input is the analysis result data, and based on this, it proposes the optimal meal menu for each member of the household. The generated menu (e.g., "Lunch: Tomato salad, Dinner: Tomato pasta") is output.

[1051] Step 6:

[1052] Menu notification

[1053] The server sends the generated menu information to the terminal. In this step, the generated menu data is sent to the terminal using the HTTP protocol. The input is the menu data, and once it is sent to the terminal, the menu is displayed on the terminal's display. This allows the user to check the optimal menu.

[1054] Step 7:

[1055] User Verification

[1056] The user checks the menu displayed on the terminal screen, takes out the necessary ingredients, and begins cooking. The menu information displayed on the terminal is input, and the user checks the ingredients based on this and cooks. Specific actions include taking the necessary ingredients from the refrigerator and cooking as displayed on the screen. Examples of prompt sentences include, "Please tell me how much food is in the refrigerator," and "How can I get the optimal amount of vitamin A?"

[1057] 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.

[1058] This invention combines a system that generates menus that optimize nutritional balance by combining health data within the home and food data stored in the refrigerator, with an emotion engine that recognizes the user's emotions. Specific embodiments of this system are described below.

[1059] Program processing explanation

[1060] Data collection and transmission

[1061] First, the user adds a new ingredient to the refrigerator. For example, let's say the user puts "carrots" in the refrigerator. The device (refrigerator) detects the new ingredient using its built-in camera and weight sensor. The device uses image recognition technology to identify the ingredient name "carrots" and its quantity. This generates ingredient data: "Item name: carrots, quantity: 3, expiration date: 2023-10-15." The device then sends this ingredient data to a server via the Internet.

[1062] The emotion engine also detects the user's facial expressions, tone of voice, and other emotional indicators to recognize their current emotional state. For example, if the user is recognized as "feeling stressed," that emotion data is also sent to the server.

[1063] Data analysis and menu generation

[1064] The server receives the food ingredient data and emotion data sent from the device. At the same time, it references the family's health data stored in a cloud database, including each family member's height, weight, blood pressure, and blood test data. The server analyzes all the received data, taking into account nutritional balance and the user's emotions.

[1065] For example, if the household is lacking in Vitamin A intake and the user is feeling stressed, the server will generate a menu that takes this into account. Based on data from the emotion engine, it will prioritize ingredients and cooking methods that are suitable for reducing stress.

[1066] As a concrete example, the server generates a menu such as "Breakfast: Carrot smoothie (rich in vitamin A, stress-reducing), Lunch: Carrot and chicken salad, Dinner: Carrot stew."

[1067] Menu notification and display

[1068] The server sends the generated menu information to the terminal. The generated menu is displayed on the refrigerator display for the user to check. The user checks the suggested menu on the refrigerator display, takes out the necessary ingredients, and starts cooking.

[1069] Specific examples

[1070] If the user puts tomatoes in the refrigerator and the emotion engine detects the user's stress state, the following process occurs:

[1071] 1. Adding ingredients: The user puts tomatoes in the refrigerator.

[1072] 2. Data detection and transmission: The device detects tomatoes, generates the data "Product name: Tomato, Quantity: 5, Best before date: 2023-10-20", and transmits it to the server. At the same time, the emotion engine detects the user's stress level and transmits the emotion data to the server.

[1073] 3. Data analysis and menu generation: The server analyzes the received data and generates a menu rich in vitamins and minerals that is effective in reducing stress. For example, a specific menu such as "Lunch: Tomato and avocado salad, Dinner: Tomato pasta" is created.

[1074] 4. Menu notification and display: The generated menu information is sent to the terminal and displayed on the refrigerator display.

[1075] In this way, users can be provided with optimal menus based on their food management, health status, and emotional state, enabling them to lead an efficient and healthy diet.

[1076] The processing flow will be explained below.

[1077] Step 1:

[1078] The user adds ingredients to the refrigerator. The user puts "carrots" in the refrigerator.

[1079] Step 2:

[1080] The device (refrigerator) recognizes the ingredients. The device's built-in camera and weight sensor detect "carrots." Image recognition and barcode scanning technology are used to identify the type and quantity of ingredients.

[1081] Step 3:

[1082] The device (refrigerator) generates the ingredient data. Based on the recognized ingredients, the data generated is "Item name: carrots, quantity: 3, expiration date: 2023-10-15".

[1083] Step 4:

[1084] The terminal (refrigerator) sends food ingredient data to the server, which then sends this data to the server via the Internet.

[1085] Step 5:

[1086] The emotion engine of the device (refrigerator) recognizes the user's emotions. It uses facial recognition and voice analysis technology using a camera and microphone to identify the user's emotional state (for example, to recognize that the user is feeling stressed).

[1087] Step 6:

[1088] The device (refrigerator) sends the recognized emotion data to a server. The emotional state data is then sent to the server via the internet.

[1089] Step 7:

[1090] The server receives the ingredient data and emotion data. The server receives the ingredient data and emotion data transmitted from the terminal.

[1091] Step 8:

[1092] The server references the family's health data and retrieves the family's health data (height, weight, blood pressure, blood test data, etc.) stored in the cloud database.

[1093] Step 9:

[1094] The server analyzes food ingredient data, emotional data, and health data, and uses AI algorithms to analyze the data taking into account nutritional balance, the health of family members, and the user's emotional state.

[1095] Step 10:

[1096] The server generates an optimal menu based on the analysis results, such as "Breakfast: Carrot smoothie (rich in vitamin A, effective in reducing stress)," "Lunch: Carrot and chicken salad," and "Dinner: Carrot stew."

[1097] Step 11:

[1098] The server sends the generated menu to the terminal, and the menu information is sent to the terminal via the Internet.

[1099] Step 12:

[1100] The terminal (refrigerator) displays the menu. The generated menu is displayed on the screen and the user can check it.

[1101] Step 13:

[1102] The user checks the menu. The user checks the menu displayed on the screen.

[1103] Step 14:

[1104] The user starts cooking. The user follows the suggested menu, selects the necessary ingredients, and starts cooking.

[1105] Example 2

[1106] 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."

[1107] In order to efficiently manage ingredients and health at home, conventional systems generally create menus by considering only ingredient and health data. However, this method cannot propose menus that take into account the user's emotional state, making it difficult to provide menus that are truly satisfying to the user. In particular, users who are feeling stressed need menu proposals that are not only nutritionally balanced but also psychologically satisfying.

[1108] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1109] In this invention, the server includes means for storing household health data, means for detecting the types and quantities of ingredients, means for detecting the user's emotional state, means for analyzing nutritional balance based on the household health data, ingredient data, and emotional data, means for generating menus based on the analysis results, and means for displaying the generated menus. This makes it possible to propose menus that take into account not only the user's health state but also their emotional state at any given time, thereby increasing user satisfaction.

[1110] "Household health data" refers to information about each household member's individual health status, such as their height, weight, blood pressure, and blood test results.

[1111] "Type and quantity of ingredients" is information about the name and quantity of each ingredient present in the refrigerator.

[1112] "User's emotional state" is information indicating the user's psychological state obtained from the user's facial expression, tone of voice, behavior, etc.

[1113] An "emotion engine" is a system or software that analyzes emotional indicators such as a user's facial expressions and tone of voice to identify the user's emotional state.

[1114] "Means for analyzing nutritional balance" refers to the process of evaluating ingredients and nutritional balance based on collected data to find the optimal combination of nutrients.

[1115] The "means for generating a menu" is a system or software that suggests an optimal meal plan to the user based on the collected health data, ingredient data, and emotional data.

[1116] The "means for displaying the generated menu" is a display or other display device that allows the user to visually confirm the proposed menu.

[1117] "Food data" refers to information such as the type, quantity, and expiration date of food ingredients stored in the refrigerator.

[1118] A "server" is a computer system that receives, analyzes, and stores data over the Internet.

[1119] A "client terminal" is a device that can be directly operated by a user, such as a refrigerator or a tablet.

[1120] "Transmitting over the Internet" is the process of transmitting data over a network to a server.

[1121] The "cameras and sensors" are video capture devices and weight measurement devices installed inside the refrigerator.

[1122] This invention combines a system that generates menus that optimize nutritional balance by combining health data within the home and food data stored in the refrigerator, with an emotion engine that recognizes the user's emotions. Specific embodiments of this system are described below.

[1123] Data collection and transmission

[1124] First, a user adds a new ingredient to the refrigerator. For example, suppose a user puts "carrots" in the refrigerator. The device (refrigerator) detects the new ingredient using its built-in camera and weight sensor. Specifically, the camera captures video of the inside of the refrigerator and identifies the "carrots" using image recognition technology. The weight sensor measures the weight of the carrots. This generates ingredient data such as "Item name: carrots, quantity: 3, expiration date: 2023-10-15." This ingredient data is sent to a server via the Internet.

[1125] In addition, the emotion engine analyzes the user's facial expressions and tone of voice to recognize their emotional state. For example, when a user holds their face close to the refrigerator or operates it with their voice, the emotion engine recognizes that the user is "feeling stressed." This emotion data is also sent to the server.

[1126] Data analysis and menu generation

[1127] The server receives the food ingredient and emotion data sent from the device and also references the family's health data stored in a cloud database, including each family member's height, weight, blood pressure, and blood test results.

[1128] The server analyzes all the data it receives, taking into account nutritional balance and the user's emotions. For example, if the household's intake of vitamin A is insufficient and the user is feeling stressed, it will generate a menu using ingredients that are rich in vitamin A and have stress-reducing effects. Specifically, it will suggest dishes such as "Breakfast: Carrot smoothie, Lunch: Carrot and chicken salad, Dinner: Carrot stew."

[1129] Menu notification and display

[1130] The generated menu information is sent from the server to the terminal. The generated menu is displayed on the refrigerator display for the user to check. The user checks the suggested menu on the display, selects the necessary ingredients, and begins cooking.

[1131] Specific examples

[1132] For example, if a user puts tomatoes in the refrigerator and the emotion engine detects the user's stress state, the following process occurs:

[1133] 1. Adding ingredients: The user puts tomatoes in the refrigerator.

[1134] 2. Data detection and transmission: The device detects tomatoes, generates the data "Product name: Tomato, Quantity: 5, Best before date: 2023-10-20", and transmits it to the server. At the same time, the emotion engine detects the user's stress level and transmits the emotion data to the server.

[1135] 3. Data analysis and menu generation: The server analyzes the received data and generates a menu rich in vitamins and minerals that is effective in reducing stress. For example, a menu such as "Lunch: Tomato and avocado salad, Dinner: Tomato pasta" might be created.

[1136] 4. Menu notification and display: The generated menu information is sent to the terminal and displayed on the refrigerator display.

[1137] Example prompts to input to the generative AI model

[1138] When a user adds "broccoli" to the refrigerator, the emotion engine detects the user's happiness state. Combine this with the family's health data to generate appropriate menus. For example, please provide specific menu suggestions for breakfast, lunch, and dinner.

[1139] Using this prompt, the generative AI model can make specific menu suggestions.

[1140] In this way, users can manage their ingredients and be provided with optimal menus that take into account their health and emotional state, enabling them to lead an efficient, satisfying and healthy diet.

[1141] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1142] Step 1:

[1143] Add ingredients

[1144] The user adds a new ingredient to the refrigerator, for example, "carrots," which triggers data collection in the next step.

[1145] Input: New ingredient (e.g. carrot)

[1146] Output: Ingredients added to the refrigerator

[1147] Step 2:

[1148] Data detection and transmission

[1149] The device detects new ingredients using its built-in camera and weight sensor. Specifically, the camera captures video of the inside of the refrigerator and identifies "carrots" using image recognition technology. At the same time, the weight sensor measures the weight of the added ingredient. This data is compiled as "Item name: Carrots, Quantity: 3, Best before date: 2023-10-15" and sent to a server via the Internet.

[1150] Input: Video data inside the refrigerator, weight data

[1151] Output: Food data (product name: carrots, quantity: 3, expiration date: 2023-10-15)

[1152] Step 3:

[1153] Collecting and transmitting emotional data

[1154] The emotion engine analyzes the user's facial expressions and tone of voice to recognize their emotional state. For example, it collects voice data when the user approaches the refrigerator or talks to it. If the analysis determines that the user is feeling stressed, that emotional data is also sent to the server.

[1155] Input: User's facial expression data, voice data

[1156] Output: Emotion data (e.g., feeling stressed)

[1157] Step 4:

[1158] Data analysis and menu generation

[1159] The server receives the food ingredient data and emotion data sent from the device. At the same time, it also references the family's health data stored in a cloud database, including each family member's height, weight, blood pressure, and blood test results. The server then analyzes all the received data, taking into account nutritional balance and the user's emotions.

[1160] For example, if the user is experiencing stress and their household is lacking in Vitamin A, the system will generate a menu using ingredients that are rich in Vitamin A and have a stress-reducing effect. As a result, the system will suggest dishes such as "Breakfast: Carrot smoothie, Lunch: Carrot and chicken salad, Dinner: Carrot stew."

[1161] Input: Food data, emotion data, health data

[1162] Output: Generated menu (e.g. Breakfast: Carrot smoothie, Lunch: Carrot and chicken salad, Dinner: Carrot stew)

[1163] Step 5:

[1164] Menu notification and display

[1165] The generated menu information is sent from the server to the terminal, which then displays the received information on the refrigerator display so that the user can check it.

[1166] Input: Generated menu information

[1167] Output: Menu displayed on the screen

[1168] Step 6:

[1169] User operations

[1170] The user can check the menu displayed on the refrigerator, select the suggested ingredients, and begin cooking. They can also request other menu suggestions if needed.

[1171] Input: Menu information displayed on the screen

[1172] Output: Start cooking or request additional menu suggestions

[1173] (Application example 2)

[1174] 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."

[1175] While modern consumers desire a healthy diet, they must select ingredients and plan meals within limited time and information to achieve this goal. Daily emotional states and stress levels influence food choices, but a lack of meal plan suggestions that take these factors into account is problematic. Furthermore, even in brick-and-mortar shopping experiences, there is a lack of systems that can help consumers make optimal food choices while taking their personal health data into account. The objective of this invention is to provide a personalized shopping experience based on each individual's emotional state by proposing optimal meals that combine a user's health data and emotional data.

[1176] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes a means for storing household health data, a means for detecting the types and quantities of ingredients, a means for analyzing nutritional balance based on the household health data and ingredient data, a means for generating a menu based on the analysis results, a means for detecting the user's emotional state and including that data in the analysis, and a means for displaying the generated menu. This enables optimal menu suggestions based on the user's health and emotional state. Furthermore, even in a physical store shopping experience, personalized ingredient selection and menu suggestions based on health and emotions can be made possible by displaying the information on the user's smartphone, smart glasses, or in-store digital signage.

[1177] "Household health data" refers to health-related data such as the height, weight, blood pressure, and blood test data of each member of the household.

[1178] "Type and quantity of ingredients" is information on the name of a specific ingredient and its corresponding quantity.

[1179] "Means for analyzing nutritional balance" refers to techniques and methods for analyzing nutrient balance based on acquired health data and food ingredient data.

[1180] "Means for generating a menu based on the analysis results" refers to techniques and methods for determining the optimal combination of ingredients and cooking methods from the results of the nutritional balance analysis, and creating a specific menu.

[1181] The "means for displaying the generated menu" refers to a terminal or screen display technology for visually presenting the generated menu information to the user.

[1182] "Means for detecting the user's emotional state and including that data in the analysis" refers to technology or methods that detect the user's facial expression, tone of voice, etc., analyze their current emotional state based on that, and reflect this information in the nutritional balance analysis.

[1183] A "server" is a large-capacity data processing device that receives, stores, and analyzes data via the Internet.

[1184] A "client terminal" is an electronic device operated by a user to receive and display data from a server.

[1185] A "refrigerator" is an electrical device used to store food at low temperatures, and has cameras and sensors inside it that detect and transmit food ingredient data.

[1186] "Cameras and sensors" are imaging and sensing devices for detecting physical information such as images and weight.

[1187] This invention relates to a system for generating menus that optimize nutritional balance by combining a user's health data and emotional data. The system is composed of a terminal used in the home and a server that analyzes the data and generates the menus.

[1188] First, the device functions as a refrigerator, smartphone, smart glasses, digital signage in a physical store, etc., and collects the user's health data and food ingredient data. Specifically, it uses a camera and sensors to detect the type and quantity of ingredients in the refrigerator and sends this data to a server via the Internet. It also uses an emotion engine to analyze the user's facial expressions and tone of voice, recognizing their current emotional state and converting it into data.

[1189] The server receives the ingredient data and emotion data sent from the device. At the same time, it also references the household health data stored in a cloud database. Based on this data, it generates an optimal menu, taking into account nutritional balance and the user's emotional state.

[1190] The generated menus are displayed on the user's smartphone, smart glasses, refrigerator display, or in-store digital signage, making it easier for users to manage their ingredients and receive optimal menu suggestions based on their health and emotions.

[1191] Specific data processing includes image recognition of ingredients using Google Cloud Vision API, emotion analysis using Emotion API, and data aggregation and analysis using Firebase. Based on the analyzed data, AI generates an optimal menu and notifies and displays it to the user.

[1192] Examples of concrete examples and prompts

[1193] For example, if a user enters a supermarket and launches a smartphone app, the emotion engine may determine that the user is tired. If the store's camera system detects fresh ingredients such as tomatoes and avocados, the AI ​​can integrate this data and suggest the following menu items:

[1194] Breakfast: Tomato and avocado salad

[1195] Lunch: Vitamin C-rich tomato soup

[1196] Dinner: Comforting avocado pasta

[1197] An example of a prompt is as follows:

[1198] If the user is tired and has tomatoes and avocados on hand, the following menu will be suggested:

[1199] Breakfast: Tomato and avocado salad

[1200] Lunch: Vitamin C-rich tomato soup

[1201] Dinner: Comforting avocado pasta

[1202] In this way, users can receive optimal menu suggestions based on their emotional and health states, enabling them to lead an efficient and healthy diet.

[1203] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1204] Step 1:

[1205] A user adds ingredients to the refrigerator. The refrigerator's built-in camera and weight sensor detect the ingredients and use image recognition technology (e.g., Google Cloud Vision API) to identify the ingredient names and quantities. This information is generated as ingredient data.

[1206] Input: Adding ingredients to the refrigerator

[1207] Output: Food ingredient data (product name, quantity, expiration date)

[1208] Step 2:

[1209] The device (refrigerator) sends the generated ingredient data to the server. At the same time, the emotion engine analyzes the user's facial expressions and tone of voice to recognize their current emotional state and transmits it to the server.

[1210] Input: Food ingredients data, emotional data (facial expressions, voice tone analysis results)

[1211] Output: Ingredient data and emotion data sent to the server

[1212] Step 3:

[1213] The server receives the food ingredient data and emotion data sent from the device and references household health data (e.g., height, weight, blood pressure, blood test data) from a cloud database.

[1214] Input: food data, emotion data, health data

[1215] Output: Consolidated data for analysis

[1216] Step 4:

[1217] The server analyzes the nutritional balance based on the integrated data and generates an optimal menu that takes into account the user's health and emotional state. The AI ​​model used in this process analyzes the nutritional balance and emotional state and generates the menu.

[1218] Input: Integrated data (food data, emotion data, health data)

[1219] Output: Optimal menu plan

[1220] Step 5:

[1221] The server transmits the generated menu plan to a terminal, such as a smartphone, smart glasses, a refrigerator display, or in-store digital signage.

[1222] Input: Optimal menu plan

[1223] Output: Menu plan sent to device

[1224] Step 6:

[1225] Users can check the menu plan displayed on the device, select the suggested ingredients, and begin cooking. If shopping at a physical store, they can also use the in-store digital signage or a smartphone app to select ingredients to purchase.

[1226] Input: Menu plan displayed on the device

[1227] Output: User action (picking ingredients, cooking, or purchasing)

[1228] 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.

[1229] 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.

[1230] 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.

[1231] [Fourth embodiment]

[1232] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1233] 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.

[1234] 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).

[1235] 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.

[1236] 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.

[1237] 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).

[1238] 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.

[1239] 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.

[1240] 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.

[1241] 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.

[1242] 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.

[1243] 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.

[1244] 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."

[1245] This invention is a system that combines household health data and food ingredient data stored in the refrigerator to create a menu that optimizes nutritional balance. Specifically, it stores household health data, detects the types and quantities of ingredients, analyzes nutritional balance based on this data, and creates an optimal menu. Specific embodiments of this system are described below.

[1246] Program processing explanation

[1247] Data collection and transmission

[1248] First, a user adds a new ingredient to the refrigerator. For example, say they put carrots in the refrigerator. The device (refrigerator) automatically detects this new ingredient using its built-in camera and weight sensor. The device uses image recognition technology to identify the ingredient name "carrots" and the quantity. This generates ingredient data such as "Item name: carrots, quantity: 3, expiration date: 2023-10-15." The device then sends this data to a server via the Internet.

[1249] Data analysis and menu generation

[1250] The server receives the food ingredient data sent from the device. At the same time, it references the family's health data stored in a cloud database. This includes each family member's height, weight, blood pressure, and blood test data. The server analyzes the nutritional balance based on the received food ingredient and health data. For example, if the family's intake of vitamin A is insufficient, it will suggest a meal plan to supplement that.

[1251] The server uses an AI algorithm to analyze health and food data and generate an optimal menu, such as "Breakfast: Carrot smoothie, Lunch: Carrot and chicken salad, Dinner: Carrot stew."

[1252] Menu notification and display

[1253] The server sends the generated menu information to the terminal. The menu is displayed on the refrigerator display for the user to check. The user checks the suggested menu on the refrigerator display, takes out the necessary ingredients, and starts cooking.

[1254] Specific examples

[1255] Let's say a user puts a tomato in the refrigerator. The following steps occur:

[1256] 1. Add ingredients

[1257] The user puts tomatoes in the refrigerator.

[1258] 2. Data detection and transmission

[1259] The device detects tomatoes, generates data such as "Product name: Tomato, Quantity: 5, Best before date: 2023-10-20", and sends it to the server.

[1260] 3. Data analysis and menu generation

[1261] The server analyzes the received data and the household's health data and generates a menu such as "Lunch: Tomato salad, Dinner: Tomato pasta."

[1262] 4. Notification and display of menu

[1263] The generated menu information is sent to a terminal and displayed on the refrigerator display.

[1264] This reduces the burden on users of managing ingredients and planning healthy meals, allowing them to lead an efficient and healthy diet.

[1265] The processing flow will be explained below.

[1266] Step 1:

[1267] The user adds ingredients to the refrigerator. The user puts "carrots" in the refrigerator.

[1268] Step 2:

[1269] The device (refrigerator) recognizes the ingredients. The device's built-in camera and weight sensor detect "carrots." Image recognition and barcode scanning technology are used to identify the type and quantity of ingredients.

[1270] Step 3:

[1271] The device (refrigerator) generates food ingredient data, such as "Item name: carrots, quantity: 3, expiration date: 2023-10-15" based on the recognized ingredients.

[1272] Step 4:

[1273] The device (refrigerator) sends the food ingredient data to the server, which then sends the data to the server via the Internet.

[1274] Step 5:

[1275] The server receives the ingredient data. The server receives the data sent from the terminal.

[1276] Step 6:

[1277] The server references the family's health data and obtains the family's health data (height, weight, blood pressure, blood test data, etc.) from the cloud database.

[1278] Step 7:

[1279] The server analyzes food ingredient and health data, using an AI algorithm to analyze the data taking into account nutritional balance and the health status of family members.

[1280] Step 8:

[1281] The server generates the optimal menu, such as "Breakfast: Carrot smoothie, Lunch: Carrot and chicken salad, Dinner: Carrot stew" based on the analysis results.

[1282] Step 9:

[1283] The server sends the generated menu to the terminal. The menu information is sent to the terminal.

[1284] Step 10:

[1285] The terminal (refrigerator) displays the menu. The generated menu is displayed on the screen and the user can check it.

[1286] Step 11:

[1287] The user checks the menu. The user checks the menu displayed on the screen.

[1288] Step 12:

[1289] The user starts cooking. The user follows the suggested menu, selects the necessary ingredients, and starts cooking.

[1290] Example 1

[1291] 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."

[1292] Currently, menu suggestion systems on the market lack the ability to generate menus that fully consider the user's health status. They also lack mechanisms for efficiently managing ingredients in the home, making it difficult for users to lead a healthy diet without hassle. This invention aims to solve these problems by automatically generating and presenting optimal menus that take into consideration the management of ingredients in the home and health.

[1293] 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.

[1294] In this invention, the server includes means for detecting the types and quantities of ingredients, means for generating ingredient data using image recognition technology and weight sensors, means for transmitting the ingredient data to the server via the Internet, means for the server to analyze the health data and ingredient data within the household and generate an optimal menu using an AI algorithm, and means for displaying the generated menu on the refrigerator display. This allows users to easily obtain an optimal menu based on the health data and ingredient status.

[1295] "Household health data" refers to information that indicates the individual health status of family members, such as their height, weight, blood pressure, and blood test data.

[1296] "Type and quantity of food ingredients" is information indicating the specific names and quantities of food items stored in the household.

[1297] "Image recognition technology" is a technology for analyzing images captured by a camera and identifying objects contained within them.

[1298] A "weight sensor" is a device for measuring the weight of an object.

[1299] "Food ingredient data" is a series of information including the type, quantity, expiration date, etc. of food ingredients.

[1300] "Means for transmitting ingredient data to a server via the Internet" refers to a method or system for transmitting ingredient data obtained via the Internet to a remote server.

[1301] An "AI algorithm" is a calculation procedure or rule that uses artificial intelligence technology to analyze input data and generate optimal results.

[1302] A "menu" is a meal plan that combines multiple meal menus.

[1303] A "refrigerator display" is a screen installed on a refrigerator, and is a device for displaying information to a user.

[1304] A "server" is a central processing unit for storing data, analyzing data, and providing information to other devices.

[1305] MODE FOR CARRYING OUT THE INVENTION

[1306] This invention is a system that combines household health data and food ingredient data stored in a refrigerator to create a menu that optimizes nutritional balance. Specific embodiments of this system are described below.

[1307] Data collection and transmission

[1308] A user adds a new ingredient to the refrigerator. For example, suppose the user puts carrots in the refrigerator. The device (refrigerator) automatically detects this new ingredient using its built-in camera and weight sensor. Specifically, the camera inside the refrigerator takes a picture of the carrots, and the sensor measures their weight. The device uses image recognition technology (e.g., TensorFlow) to identify the ingredient and generates data such as "Item name: carrots, quantity: 3, expiration date: 2023-10-15." The generated data is sent to a server via the Internet.

[1309] Data analysis and menu generation

[1310] The server receives the ingredient data sent by the user and stores it in a cloud database. At the same time, it retrieves the family's health data (height, weight, blood pressure, blood test data, etc.) from the cloud database. The server analyzes nutritional balance based on the health data and ingredient data. For example, if all family members are deficient in vitamin A, it generates a menu to make up for that deficiency. The server uses an AI algorithm (e.g., OpenAI's GPT-3) to suggest the optimal menu based on the health data and ingredient data. As a specific example, it generates "Breakfast: Carrot smoothie, Lunch: Carrot and chicken salad, Dinner: Carrot stew."

[1311] Menu notification and display

[1312] The server sends the generated menu information to the terminal (refrigerator). The terminal displays the received menu information on the refrigerator's display. The user checks this display, selects ingredients based on the proposed menu, and begins cooking.

[1313] Specific examples

[1314] For example, if the user puts "tomatoes" in the refrigerator, the system behaves as follows:

[1315] 1. Add ingredients

[1316] The user puts tomatoes in the refrigerator.

[1317] 2. Data detection and transmission

[1318] The device detects tomatoes, generates data such as "Product name: Tomato, Quantity: 5, Best before date: 2023-10-20" and sends it to the server.

[1319] 3. Data analysis and menu generation

[1320] The server analyzes the received data and the household's health data and generates a menu of "Lunch: Tomato salad, Dinner: Tomato pasta."

[1321] 4. Notification and display of menu

[1322] The generated menu information is sent to a terminal and displayed on the refrigerator display.

[1323] This reduces the burden on users of managing ingredients and planning healthy meals, allowing them to lead an efficient and healthy diet.

[1324] Prompt Sentence Examples

[1325] By inputting the following prompts into the generative AI model, optimal meal suggestions can be obtained:

[1326] A new ingredient has been added to your refrigerator. Please use the following data to create a meal plan that takes into account your family's health status.

[1327] Ingredients: Tomato

[1328] Quantity: 5 pieces

[1329] Best before date: 2023-10-20

[1330] Health data: The whole family has low vitamin A intake.

[1331] Please suggest one of the generated menus for breakfast, one for lunch, and one for dinner.

[1332] This invention combines the latest image recognition technology with AI algorithms to realize in-home food management and health-optimized meal planning, allowing users to lead a healthy diet without any hassle.

[1333] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1334] The flow of this system's program processing

[1335] Step 1: Adding and detecting ingredients

[1336] The user adds a new ingredient to the refrigerator. For example, the user adds "carrots" to the refrigerator.

[1337] Input: New ingredient (carrot)

[1338] Operation:

[1339] The device (refrigerator) takes a picture of the carrot using its built-in camera.

[1340] A weight sensor measures the weight of the carrot.

[1341] Data processing:

[1342] Image data acquired by the camera and weight data acquired by the weight sensor are stored in the device's internal memory.

[1343] Output: Image data and weight data

[1344] Step 2: Generate ingredient data

[1345] The device generates ingredient data using image recognition technology.

[1346] Input: Image data and weight data

[1347] Operation:

[1348] The device uses image recognition software (e.g., TensorFlow) to analyze the captured image and identify the name of the ingredient (carrot).

[1349] Determine the quantity of carrots based on the data from the weight sensor.

[1350] Data processing:

[1351] Formatted data including the ingredient name, quantity, and expiration date is generated (e.g., "Item name: Carrots, Quantity: 3, Expiration date: 2023-10-15").

[1352] Output: Food ingredient data (product name, quantity, expiration date)

[1353] Step 3: Submit ingredient data

[1354] The generated ingredient data is transmitted to a server via the Internet.

[1355] Input: Food ingredient data (product name, quantity, expiration date)

[1356] Operation:

[1357] The terminal transmits the ingredient data to a server via the Internet.

[1358] Output: Ingredient data sent to the server

[1359] Step 4: Acquire and analyze health data

[1360] The server collects health data from within the home and analyzes it along with food ingredient data.

[1361] Input: Food ingredient data (product name, quantity, expiration date), household health data (height, weight, blood pressure, blood test data)

[1362] Operation:

[1363] The server retrieves home health data from a cloud database.

[1364] The server combines the health data with the newly acquired food ingredient data and performs the analysis.

[1365] Data processing:

[1366] Analyze nutritional balance and evaluate the relationship between the family's nutritional status and new ingredients.

[1367] Output: Nutritional balance analysis results

[1368] Step 5: Create a menu

[1369] The server uses AI algorithms to generate the optimal menu.

[1370] Input: Nutritional balance analysis results

[1371] Operation:

[1372] The server uses a generative AI model (e.g., OpenAI's GPT-3) to generate the optimal menu based on the analysis results.

[1373] For example, to increase your intake of vitamin A, we suggest a menu such as "Breakfast: Carrot smoothie, Lunch: Carrot and chicken salad, Dinner: Carrot stew."

[1374] Data processing:

[1375] Health data and food ingredient data are integrated to generate optimized menus.

[1376] Output: Generated menu (specific menus for breakfast, lunch, and dinner)

[1377] Step 6: Submit and view your menu

[1378] The generated menu information is sent to be displayed on the refrigerator display.

[1379] Input: Generated menu

[1380] Operation:

[1381] The server then transmits the generated menu information to the terminal (refrigerator) again via the Internet.

[1382] The device displays the received menu information on the refrigerator's display.

[1383] Output: Menu displayed on the refrigerator display

[1384] Specific examples

[1385] The specific flow of processing when the user puts "tomatoes" in the refrigerator is as follows:

[1386] 1. Add ingredients

[1387] The user puts tomatoes in the refrigerator.

[1388] 2. Data detection and transmission

[1389] The device takes a picture of the tomato with its built-in camera, and a weight sensor measures the tomato's weight.

[1390] Using image recognition technology, "tomato" is identified and the following data is generated: "Product name: Tomato, Quantity: 5, Best before date: 2023-10-20."

[1391] The generated data is sent to the server.

[1392] 3. Data analysis and menu generation

[1393] The server receives the food ingredient data and retrieves the health data from the cloud database.

[1394] Analyze health data and food ingredient data to generate a menu of "Lunch: Tomato salad, Dinner: Tomato pasta."

[1395] 4. Notification and display of menu

[1396] The generated menu information is sent to be displayed on the refrigerator display.

[1397] The user checks the displayed menu, selects the necessary ingredients, and begins cooking.

[1398] keyword

[1399] Generative AI model, prompt sentence

[1400] (Application example 1)

[1401] 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."

[1402] While there have been systems that combine food data from a home refrigerator with health data to generate nutritionally balanced menus, there have been no systems that integrate food data from a commercial facility with food products purchased at home and generate optimal menus based on health data from the home. This has made it difficult for consumers to select products that are optimal for their health. The present invention aims to solve these problems and provide a system that provides personalized nutrition advice and product suggestions to consumers.

[1403] 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.

[1404] In this invention, the server includes means for storing health data in the home, means for detecting the types and quantities of ingredients, means for analyzing nutritional balance based on the health data and ingredient data in the home, means for generating menus based on the analysis results, means for displaying the generated menus, and means for combining products purchased in commercial facilities with ingredient data in the home to propose optimal menus. This enables consumers to select products that are optimal for their health condition and lead an efficient and healthy diet.

[1405] A "household health data storage means" is a storage device or database for storing health information (e.g., height, weight, blood pressure, blood test results, etc.) for each family member.

[1406] "Means for detecting the type and quantity of ingredients" refers to devices or technologies that automatically recognize the type and quantity of ingredients using cameras or sensors inside the refrigerator.

[1407] The "means for analyzing nutritional balance based on household health data and food ingredient data" refers to a processor or algorithm that analyzes the collected health data and food ingredient data and performs calculations to optimize the nutritional balance of each family member.

[1408] The "means for generating a menu based on the analysis results" is a system that suggests meal menus suitable for each family member based on the results of nutritional analysis.

[1409] The "means for displaying the generated menu" is a device equipped with a display or screen that displays the generated menu information so that the user can check it.

[1410] "A means of combining products purchased in a commercial facility with food ingredient data from the home to suggest the optimal menu" is a system that obtains data on products that consumers are planning to purchase in a commercial facility, analyzes this data together with food ingredient data from the home and household health data, and suggests the optimal menu.

[1411] This invention is a system that integrates the products a consumer is planning to purchase in a physical store with food data stored in the home refrigerator and household health data to propose a menu that takes into account optimal nutritional balance. Detailed embodiments of this system are described below.

[1412] System Configuration

[1413] The system consists of the following main components:

[1414] Client terminal: A device such as a smartphone or robot

[1415] Server: A central system that integrates data and performs analysis

[1416] Storage device: A database that stores health data within the home

[1417] Hardware and software used

[1418] Hardware: smartphones, robot cameras, sensors

[1419] Software: OpenCV (image processing library), Requests (HTTP request library)

[1420] Data collection and transmission

[1421] When a user scans an item in a commercial facility, the device collects information about the item and acquires the appropriate data using the device's built-in camera and sensors. The device then transmits the collected data to a server via the Internet. Specifically, image recognition technology is used to identify the product name and quantity. OpenCV is used for this process.

[1422] Data analysis and menu generation

[1423] The server receives product data sent from the device, food data in the home refrigerator, and household health data. Based on the received data, the server uses an AI algorithm to analyze nutritional balance and generate optimal menus. This analysis uses a generative AI model and analyzes the results.

[1424] Menu notification and display

[1425] The server sends the generated menu information to the device, which then presents this information to the user, who can then check the optimal menu. For example, the menu is displayed on a smartphone or robot display.

[1426] Specific examples

[1427] 1. Data collection and transmission:

[1428] A user attempts to purchase "tomatoes" at a commercial facility.

[1429] The tomato is scanned with the smartphone camera, data such as "Item name: Tomato, Quantity: 5" is generated and sent to the server.

[1430] 2. Data analysis and menu generation:

[1431] The server analyzes the received tomato data, household health data, and data on other ingredients in the refrigerator, and generates a menu such as "Lunch: Tomato salad, Dinner: Tomato pasta."

[1432] 3. Menu Notification and Display:

[1433] The generated menu information is sent to a smartphone and displayed for the user to review.

[1434] Prompt Sentence Examples

[1435] "Could you tell me how much food you have in the fridge?"

[1436] "How can I get optimal vitamin A?"

[1437] The system allows users to take their household health data into account when choosing what to buy, enabling them to lead an efficient and healthy diet.

[1438] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1439] Step 1:

[1440] Data collection

[1441] A user scans a product they are about to purchase at a commercial facility. The device (smartphone or robot) uses a camera to take a picture of the product (e.g., tomatoes) and acquires image data. This image data becomes the input. Image recognition technology (e.g., OpenCV) is used to identify the product name (tomatoes) and quantity (5 pieces). As a result, data such as "Product name: tomatoes, quantity: 5 pieces" is output.

[1442] Step 2:

[1443] Data transmission

[1444] The terminal sends the product data acquired in step 1 to the server via the Internet. In this step, an HTTP request library (e.g., Requests) is used to send the product data to the server. The input data is the product name and quantity, which are then sent to the server.

[1445] Step 3:

[1446] Integrating household health data and food ingredient data

[1447] The server retrieves food item data and health data from the cloud database. The inputs are the health data of each household member (e.g., height, weight, blood pressure) and food item data in the refrigerator (e.g., ingredients in stock and their quantities). The server integrates this data and generates a dataset for analysis.

[1448] Step 4:

[1449] Nutritional balance analysis

[1450] The server analyzes nutritional balance based on the product data and household data received in step 2. In this step, a generative AI model is used to analyze the collected data as an input dataset. For example, if the user's vitamin A intake is insufficient, it determines whether it can suggest ingredients and menus to compensate for that. As a result of the analysis, a specific action plan for adjusting nutritional balance is output.

[1451] Step 5:

[1452] Menu generation

[1453] The server generates an optimal menu based on the results of the nutritional balance analysis. The input is the analysis result data, and based on this, it proposes the optimal meal menu for each member of the household. The generated menu (e.g., "Lunch: Tomato salad, Dinner: Tomato pasta") is output.

[1454] Step 6:

[1455] Menu notification

[1456] The server sends the generated menu information to the terminal. In this step, the generated menu data is sent to the terminal using the HTTP protocol. The input is the menu data, and once it is sent to the terminal, the menu is displayed on the terminal's display. This allows the user to check the optimal menu.

[1457] Step 7:

[1458] User Verification

[1459] The user checks the menu displayed on the terminal screen, takes out the necessary ingredients, and begins cooking. The menu information displayed on the terminal is input, and the user checks the ingredients based on this and cooks. Specific actions include taking the necessary ingredients from the refrigerator and cooking as displayed on the screen. Examples of prompt sentences include, "Please tell me how much food is in the refrigerator," and "How can I get the optimal amount of vitamin A?"

[1460] 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.

[1461] This invention combines a system that generates menus that optimize nutritional balance by combining health data within the home and food data stored in the refrigerator, with an emotion engine that recognizes the user's emotions. Specific embodiments of this system are described below.

[1462] Program processing explanation

[1463] Data collection and transmission

[1464] First, the user adds a new ingredient to the refrigerator. For example, let's say the user puts "carrots" in the refrigerator. The device (refrigerator) detects the new ingredient using its built-in camera and weight sensor. The device uses image recognition technology to identify the ingredient name "carrots" and its quantity. This generates ingredient data: "Item name: carrots, quantity: 3, expiration date: 2023-10-15." The device then sends this ingredient data to a server via the Internet.

[1465] The emotion engine also detects the user's facial expressions, tone of voice, and other emotional indicators to recognize their current emotional state. For example, if the user is recognized as "feeling stressed," that emotion data is also sent to the server.

[1466] Data analysis and menu generation

[1467] The server receives the food ingredient data and emotion data sent from the device. At the same time, it references the family's health data stored in a cloud database, including each family member's height, weight, blood pressure, and blood test data. The server analyzes all the received data, taking into account nutritional balance and the user's emotions.

[1468] For example, if the household is lacking in Vitamin A intake and the user is feeling stressed, the server will generate a menu that takes this into account. Based on data from the emotion engine, it will prioritize ingredients and cooking methods that are suitable for reducing stress.

[1469] As a concrete example, the server generates a menu such as "Breakfast: Carrot smoothie (rich in vitamin A, stress-reducing), Lunch: Carrot and chicken salad, Dinner: Carrot stew."

[1470] Menu notification and display

[1471] The server sends the generated menu information to the terminal. The generated menu is displayed on the refrigerator display for the user to check. The user checks the suggested menu on the refrigerator display, takes out the necessary ingredients, and starts cooking.

[1472] Specific examples

[1473] If the user puts tomatoes in the refrigerator and the emotion engine detects the user's stress state, the following process occurs:

[1474] 1. Adding ingredients: The user puts tomatoes in the refrigerator.

[1475] 2. Data detection and transmission: The device detects tomatoes, generates the data "Product name: Tomato, Quantity: 5, Best before date: 2023-10-20", and transmits it to the server. At the same time, the emotion engine detects the user's stress level and transmits the emotion data to the server.

[1476] 3. Data analysis and menu generation: The server analyzes the received data and generates a menu rich in vitamins and minerals that is effective in reducing stress. For example, a specific menu such as "Lunch: Tomato and avocado salad, Dinner: Tomato pasta" is created.

[1477] 4. Menu notification and display: The generated menu information is sent to the terminal and displayed on the refrigerator display.

[1478] In this way, users can be provided with optimal menus based on their food management, health status, and emotional state, enabling them to lead an efficient and healthy diet.

[1479] The processing flow will be explained below.

[1480] Step 1:

[1481] The user adds ingredients to the refrigerator. The user puts "carrots" in the refrigerator.

[1482] Step 2:

[1483] The device (refrigerator) recognizes the ingredients. The device's built-in camera and weight sensor detect "carrots." Image recognition and barcode scanning technology are used to identify the type and quantity of ingredients.

[1484] Step 3:

[1485] The device (refrigerator) generates the ingredient data. Based on the recognized ingredients, the data generated is "Item name: carrots, quantity: 3, expiration date: 2023-10-15".

[1486] Step 4:

[1487] The terminal (refrigerator) sends food ingredient data to the server, which then sends this data to the server via the Internet.

[1488] Step 5:

[1489] The emotion engine of the device (refrigerator) recognizes the user's emotions. It uses facial recognition and voice analysis technology using a camera and microphone to identify the user's emotional state (for example, to recognize that the user is feeling stressed).

[1490] Step 6:

[1491] The device (refrigerator) sends the recognized emotion data to a server. The emotional state data is then sent to the server via the internet.

[1492] Step 7:

[1493] The server receives the ingredient data and emotion data. The server receives the ingredient data and emotion data transmitted from the terminal.

[1494] Step 8:

[1495] The server references the family's health data and retrieves the family's health data (height, weight, blood pressure, blood test data, etc.) stored in the cloud database.

[1496] Step 9:

[1497] The server analyzes food ingredient data, emotional data, and health data, and uses AI algorithms to analyze the data taking into account nutritional balance, the health of family members, and the user's emotional state.

[1498] Step 10:

[1499] The server generates an optimal menu based on the analysis results, such as "Breakfast: Carrot smoothie (rich in vitamin A, effective in reducing stress)," "Lunch: Carrot and chicken salad," and "Dinner: Carrot stew."

[1500] Step 11:

[1501] The server sends the generated menu to the terminal, and the menu information is sent to the terminal via the Internet.

[1502] Step 12:

[1503] The terminal (refrigerator) displays the menu. The generated menu is displayed on the screen and the user can check it.

[1504] Step 13:

[1505] The user checks the menu. The user checks the menu displayed on the screen.

[1506] Step 14:

[1507] The user starts cooking. The user follows the suggested menu, selects the necessary ingredients, and starts cooking.

[1508] Example 2

[1509] 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."

[1510] In order to efficiently manage ingredients and health at home, conventional systems generally create menus by considering only ingredient and health data. However, this method cannot propose menus that take into account the user's emotional state, making it difficult to provide menus that are truly satisfying to the user. In particular, users who are feeling stressed need menu proposals that are not only nutritionally balanced but also psychologically satisfying.

[1511] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1512] In this invention, the server includes means for storing household health data, means for detecting the types and quantities of ingredients, means for detecting the user's emotional state, means for analyzing nutritional balance based on the household health data, ingredient data, and emotional data, means for generating menus based on the analysis results, and means for displaying the generated menus. This makes it possible to propose menus that take into account not only the user's health state but also their emotional state at any given time, thereby increasing user satisfaction.

[1513] "Household health data" refers to information about each household member's individual health status, such as their height, weight, blood pressure, and blood test results.

[1514] "Type and quantity of ingredients" is information about the name and quantity of each ingredient present in the refrigerator.

[1515] "User's emotional state" is information indicating the user's psychological state obtained from the user's facial expression, tone of voice, behavior, etc.

[1516] An "emotion engine" is a system or software that analyzes emotional indicators such as a user's facial expressions and tone of voice to identify the user's emotional state.

[1517] "Means for analyzing nutritional balance" refers to the process of evaluating ingredients and nutritional balance based on collected data to find the optimal combination of nutrients.

[1518] The "means for generating a menu" is a system or software that suggests an optimal meal plan to the user based on the collected health data, ingredient data, and emotional data.

[1519] The "means for displaying the generated menu" is a display or other display device that allows the user to visually confirm the proposed menu.

[1520] "Food data" refers to information such as the type, quantity, and expiration date of food ingredients stored in the refrigerator.

[1521] A "server" is a computer system that receives, analyzes, and stores data over the Internet.

[1522] A "client terminal" is a device that can be directly operated by a user, such as a refrigerator or a tablet.

[1523] "Transmitting over the Internet" is the process of transmitting data over a network to a server.

[1524] The "cameras and sensors" are video capture devices and weight measurement devices installed inside the refrigerator.

[1525] This invention combines a system that generates menus that optimize nutritional balance by combining health data within the home and food data stored in the refrigerator, with an emotion engine that recognizes the user's emotions. Specific embodiments of this system are described below.

[1526] Data collection and transmission

[1527] First, a user adds a new ingredient to the refrigerator. For example, suppose a user puts "carrots" in the refrigerator. The device (refrigerator) detects the new ingredient using its built-in camera and weight sensor. Specifically, the camera captures video of the inside of the refrigerator and identifies the "carrots" using image recognition technology. The weight sensor measures the weight of the carrots. This generates ingredient data such as "Item name: carrots, quantity: 3, expiration date: 2023-10-15." This ingredient data is sent to a server via the Internet.

[1528] In addition, the emotion engine analyzes the user's facial expressions and tone of voice to recognize their emotional state. For example, when a user holds their face close to the refrigerator or operates it with their voice, the emotion engine recognizes that the user is "feeling stressed." This emotion data is also sent to the server.

[1529] Data analysis and menu generation

[1530] The server receives the food ingredient and emotion data sent from the device and also references the family's health data stored in a cloud database, including each family member's height, weight, blood pressure, and blood test results.

[1531] The server analyzes all the data it receives, taking into account nutritional balance and the user's emotions. For example, if the household's intake of vitamin A is insufficient and the user is feeling stressed, it will generate a menu using ingredients that are rich in vitamin A and have stress-reducing effects. Specifically, it will suggest dishes such as "Breakfast: Carrot smoothie, Lunch: Carrot and chicken salad, Dinner: Carrot stew."

[1532] Menu notification and display

[1533] The generated menu information is sent from the server to the terminal. The generated menu is displayed on the refrigerator display for the user to check. The user checks the suggested menu on the display, selects the necessary ingredients, and begins cooking.

[1534] Specific examples

[1535] For example, if a user puts tomatoes in the refrigerator and the emotion engine detects the user's stress state, the following process occurs:

[1536] 1. Adding ingredients: The user puts tomatoes in the refrigerator.

[1537] 2. Data detection and transmission: The device detects tomatoes, generates the data "Product name: Tomato, Quantity: 5, Best before date: 2023-10-20", and transmits it to the server. At the same time, the emotion engine detects the user's stress level and transmits the emotion data to the server.

[1538] 3. Data analysis and menu generation: The server analyzes the received data and generates a menu rich in vitamins and minerals that is effective in reducing stress. For example, a menu such as "Lunch: Tomato and avocado salad, Dinner: Tomato pasta" might be created.

[1539] 4. Menu notification and display: The generated menu information is sent to the terminal and displayed on the refrigerator display.

[1540] Example prompts to input to the generative AI model

[1541] When a user adds "broccoli" to the refrigerator, the emotion engine detects the user's happiness state. Combine this with the family's health data to generate appropriate menus. For example, please provide specific menu suggestions for breakfast, lunch, and dinner.

[1542] Using this prompt, the generative AI model can make specific menu suggestions.

[1543] In this way, users can manage their ingredients and be provided with optimal menus that take into account their health and emotional state, enabling them to lead an efficient, satisfying and healthy diet.

[1544] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1545] Step 1:

[1546] Add ingredients

[1547] The user adds a new ingredient to the refrigerator, for example, "carrots," which triggers data collection in the next step.

[1548] Input: New ingredient (e.g. carrot)

[1549] Output: Ingredients added to the refrigerator

[1550] Step 2:

[1551] Data detection and transmission

[1552] The device detects new ingredients using its built-in camera and weight sensor. Specifically, the camera captures video of the inside of the refrigerator and identifies "carrots" using image recognition technology. At the same time, the weight sensor measures the weight of the added ingredient. This data is compiled as "Item name: Carrots, Quantity: 3, Best before date: 2023-10-15" and sent to a server via the Internet.

[1553] Input: Video data inside the refrigerator, weight data

[1554] Output: Food data (product name: carrots, quantity: 3, expiration date: 2023-10-15)

[1555] Step 3:

[1556] Collecting and transmitting emotional data

[1557] The emotion engine analyzes the user's facial expressions and tone of voice to recognize their emotional state. For example, it collects voice data when the user approaches the refrigerator or talks to it. If the analysis determines that the user is feeling stressed, that emotional data is also sent to the server.

[1558] Input: User's facial expression data, voice data

[1559] Output: Emotion data (e.g., feeling stressed)

[1560] Step 4:

[1561] Data analysis and menu generation

[1562] The server receives the food ingredient data and emotion data sent from the device. At the same time, it also references the family's health data stored in a cloud database, including each family member's height, weight, blood pressure, and blood test results. The server then analyzes all the received data, taking into account nutritional balance and the user's emotions.

[1563] For example, if the user is experiencing stress and their household is lacking in Vitamin A, the system will generate a menu using ingredients that are rich in Vitamin A and have a stress-reducing effect. As a result, the system will suggest dishes such as "Breakfast: Carrot smoothie, Lunch: Carrot and chicken salad, Dinner: Carrot stew."

[1564] Input: Food data, emotion data, health data

[1565] Output: Generated menu (e.g. Breakfast: Carrot smoothie, Lunch: Carrot and chicken salad, Dinner: Carrot stew)

[1566] Step 5:

[1567] Menu notification and display

[1568] The generated menu information is sent from the server to the terminal, which then displays the received information on the refrigerator display so that the user can check it.

[1569] Input: Generated menu information

[1570] Output: Menu displayed on the screen

[1571] Step 6:

[1572] User operations

[1573] The user can check the menu displayed on the refrigerator, select the suggested ingredients, and begin cooking. They can also request other menu suggestions if needed.

[1574] Input: Menu information displayed on the screen

[1575] Output: Start cooking or request additional menu suggestions

[1576] (Application example 2)

[1577] 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."

[1578] While modern consumers desire a healthy diet, they must select ingredients and plan meals within limited time and information to achieve this goal. Daily emotional states and stress levels influence food choices, but a lack of meal plan suggestions that take these factors into account is problematic. Furthermore, even in brick-and-mortar shopping experiences, there is a lack of systems that can help consumers make optimal food choices while taking their personal health data into account. The objective of this invention is to provide a personalized shopping experience based on each individual's emotional state by proposing optimal meals that combine a user's health data and emotional data.

[1579] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes a means for storing household health data, a means for detecting the types and quantities of ingredients, a means for analyzing nutritional balance based on the household health data and ingredient data, a means for generating a menu based on the analysis results, a means for detecting the user's emotional state and including that data in the analysis, and a means for displaying the generated menu. This enables optimal menu suggestions based on the user's health and emotional state. Furthermore, even in a physical store shopping experience, personalized ingredient selection and menu suggestions based on health and emotions can be made possible by displaying the information on the user's smartphone, smart glasses, or in-store digital signage.

[1580] "Household health data" refers to health-related data such as the height, weight, blood pressure, and blood test data of each member of the household.

[1581] "Type and quantity of ingredients" is information on the name of a specific ingredient and its corresponding quantity.

[1582] "Means for analyzing nutritional balance" refers to techniques and methods for analyzing nutrient balance based on acquired health data and food ingredient data.

[1583] "Means for generating a menu based on the analysis results" refers to techniques and methods for determining the optimal combination of ingredients and cooking methods from the results of the nutritional balance analysis, and creating a specific menu.

[1584] The "means for displaying the generated menu" refers to a terminal or screen display technology for visually presenting the generated menu information to the user.

[1585] "Means for detecting the user's emotional state and including that data in the analysis" refers to technology or methods that detect the user's facial expression, tone of voice, etc., analyze their current emotional state based on that, and reflect this information in the nutritional balance analysis.

[1586] A "server" is a large-capacity data processing device that receives, stores, and analyzes data via the Internet.

[1587] A "client terminal" is an electronic device operated by a user to receive and display data from a server.

[1588] A "refrigerator" is an electrical device used to store food at low temperatures, and has cameras and sensors inside it that detect and transmit food ingredient data.

[1589] "Cameras and sensors" are imaging and sensing devices for detecting physical information such as images and weight.

[1590] This invention relates to a system for generating menus that optimize nutritional balance by combining a user's health data and emotional data. The system is composed of a terminal used in the home and a server that analyzes the data and generates the menus.

[1591] First, the device functions as a refrigerator, smartphone, smart glasses, digital signage in a physical store, etc., and collects the user's health data and food ingredient data. Specifically, it uses a camera and sensors to detect the type and quantity of ingredients in the refrigerator and sends this data to a server via the Internet. It also uses an emotion engine to analyze the user's facial expressions and tone of voice, recognizing their current emotional state and converting it into data.

[1592] The server receives the ingredient data and emotion data sent from the device. At the same time, it also references the household health data stored in a cloud database. Based on this data, it generates an optimal menu, taking into account nutritional balance and the user's emotional state.

[1593] The generated menus are displayed on the user's smartphone, smart glasses, refrigerator display, or in-store digital signage, making it easier for users to manage their ingredients and receive optimal menu suggestions based on their health and emotions.

[1594] Specific data processing includes image recognition of ingredients using Google Cloud Vision API, emotion analysis using Emotion API, and data aggregation and analysis using Firebase. Based on the analyzed data, AI generates an optimal menu and notifies and displays it to the user.

[1595] Examples of concrete examples and prompts

[1596] For example, if a user enters a supermarket and launches a smartphone app, the emotion engine may determine that the user is tired. If the store's camera system detects fresh ingredients such as tomatoes and avocados, the AI ​​can integrate this data and suggest the following menu items:

[1597] Breakfast: Tomato and avocado salad

[1598] Lunch: Vitamin C-rich tomato soup

[1599] Dinner: Comforting avocado pasta

[1600] An example of a prompt is as follows:

[1601] If the user is tired and has tomatoes and avocados on hand, the following menu will be suggested:

[1602] Breakfast: Tomato and avocado salad

[1603] Lunch: Vitamin C-rich tomato soup

[1604] Dinner: Comforting avocado pasta

[1605] In this way, users can receive optimal menu suggestions based on their emotional and health states, enabling them to lead an efficient and healthy diet.

[1606] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1607] Step 1:

[1608] A user adds ingredients to the refrigerator. The refrigerator's built-in camera and weight sensor detect the ingredients and use image recognition technology (e.g., Google Cloud Vision API) to identify the ingredient names and quantities. This information is generated as ingredient data.

[1609] Input: Adding ingredients to the refrigerator

[1610] Output: Food ingredient data (product name, quantity, expiration date)

[1611] Step 2:

[1612] The device (refrigerator) sends the generated ingredient data to the server. At the same time, the emotion engine analyzes the user's facial expressions and tone of voice to recognize their current emotional state and transmits it to the server.

[1613] Input: Food ingredients data, emotional data (facial expressions, voice tone analysis results)

[1614] Output: Ingredient data and emotion data sent to the server

[1615] Step 3:

[1616] The server receives the food ingredient data and emotion data sent from the device and references household health data (e.g., height, weight, blood pressure, blood test data) from a cloud database.

[1617] Input: food data, emotion data, health data

[1618] Output: Consolidated data for analysis

[1619] Step 4:

[1620] The server analyzes the nutritional balance based on the integrated data and generates an optimal menu that takes into account the user's health and emotional state. The AI ​​model used in this process analyzes the nutritional balance and emotional state and generates the menu.

[1621] Input: Integrated data (food data, emotion data, health data)

[1622] Output: Optimal menu plan

[1623] Step 5:

[1624] The server transmits the generated menu plan to a terminal, such as a smartphone, smart glasses, a refrigerator display, or in-store digital signage.

[1625] Input: Optimal menu plan

[1626] Output: Menu plan sent to device

[1627] Step 6:

[1628] Users can check the menu plan displayed on the device, select the suggested ingredients, and begin cooking. If shopping at a physical store, they can also use the in-store digital signage or a smartphone app to select ingredients to purchase.

[1629] Input: Menu plan displayed on the device

[1630] Output: User action (picking ingredients, cooking, or purchasing)

[1631] 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.

[1632] 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.

[1633] 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.

[1634] 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.

[1635] 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.

[1636] 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.

[1637] 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).

[1638] 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.

[1639] 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."

[1640] 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.

[1641] 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).

[1642] 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.

[1643] 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.

[1644] 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.

[1645] 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.

[1646] 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.

[1647] 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.

[1648] 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.

[1649] 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.

[1650] 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.

[1651] 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.

[1652] The following is further disclosed regarding the above embodiment.

[1653] (Claim 1)

[1654] means for storing health data within the home;

[1655] means for detecting the type and quantity of ingredients;

[1656] A means for analyzing nutritional balance based on household health data and food ingredient data;

[1657] A means for generating a menu based on the analysis results;

[1658] A means for displaying the generated menu;

[1659] A system including:

[1660] (Claim 2)

[1661] a means for transmitting food ingredient data to a server via the Internet;

[1662] A means for analyzing nutritional balance based on health data and food ingredient data in the home and generating a menu in the server;

[1663] means for transmitting the generated menu to a client terminal and displaying it on the client terminal;

[1664] 10. The system of claim 1, comprising:

[1665] (Claim 3)

[1666] The client terminal is a refrigerator,

[1667] It is equipped with a camera and sensors to detect ingredients in the refrigerator.

[1668] means for transmitting food ingredient data detected by the camera and the sensor to a server;

[1669] 10. The system of claim 1, comprising:

[1670] "Example 1"

[1671] (Claim 1)

[1672] means for storing health data within the home;

[1673] means for detecting the type and quantity of ingredients;

[1674] A means for analyzing nutritional balance based on household health data and food ingredient data;

[1675] A means for generating food ingredient data using image recognition technology and a weight sensor;

[1676] means for transmitting ingredient data to a server via the Internet;

[1677] A means to analyze household health data and food ingredient data on a server and generate optimal menus using AI algorithms;

[1678] a means for displaying the generated menu on a display of the refrigerator;

[1679] A system including:

[1680] (Claim 2)

[1681] A server analyzes nutritional balance based on household health data and food ingredient data, and generates menus using an AI algorithm.

[1682] means for transmitting the generated menu to a client terminal and displaying it on the client terminal;

[1683] 10. The system of claim 1, comprising:

[1684] (Claim 3)

[1685] The client terminal is a refrigerator,

[1686] It is equipped with a camera and sensors to detect ingredients in the refrigerator.

[1687] means for transmitting food ingredient data detected by the camera and the sensor to a server;

[1688] 10. The system of claim 1, comprising:

[1689] "Application Example 1"

[1690] (Claim 1)

[1691] means for storing health data within the home;

[1692] means for detecting the type and quantity of ingredients;

[1693] A means for analyzing nutritional balance based on household health data and food ingredient data;

[1694] A means for generating a menu based on the analysis results;

[1695] A means for displaying the generated menu;

[1696] A method to combine products purchased in commercial facilities with food data from home to suggest optimal menus,

[1697] A system including:

[1698] (Claim 2)

[1699] a means for transmitting food ingredient data to a server via the Internet;

[1700] A means for analyzing nutritional balance based on health data and food ingredient data in the home and generating a menu in the server;

[1701] means for transmitting the generated menu to a client terminal and displaying it on the client terminal;

[1702] means for transmitting information about the product to be purchased to a server and displaying the results of analysis by the server on the client terminal;

[1703] 10. The system of claim 1, comprising:

[1704] (Claim 3)

[1705] The client terminal is a refrigerator,

[1706] It is equipped with a camera and sensors to detect ingredients in the refrigerator.

[1707] means for transmitting food ingredient data detected by the camera and the sensor to a server;

[1708] means for scanning merchandise purchased within the commercial establishment and transmitting the data to a server;

[1709] 10. The system of claim 1, comprising:

[1710] "Example 2: Combining Emotion Engines"

[1711] (Claim 1)

[1712] means for storing health data within the home;

[1713] means for detecting the type and quantity of ingredients;

[1714] means for detecting an emotional state of a user;

[1715] A means for analyzing nutritional balance based on health data, food data, and emotional data within the home;

[1716] A means for generating a menu based on the analysis results;

[1717] A means for displaying the generated menu;

[1718] A system including:

[1719] (Claim 2)

[1720] means for transmitting food ingredient data and emotion data to a server via the Internet;

[1721] A means for analyzing nutritional balance based on health data, food ingredient data and emotion data in the home and generating a menu in the server;

[1722] means for transmitting the generated menu to a client terminal and displaying it on the client terminal;

[1723] 10. The system of claim 1, comprising:

[1724] (Claim 3)

[1725] The client terminal is a refrigerator,

[1726] It is equipped with a camera and sensors to detect ingredients in the refrigerator.

[1727] means for transmitting food ingredient data detected by the camera and the sensor to a server;

[1728] 10. The system of claim 1, comprising:

[1729] "Application example 2 when combining emotion engines"

[1730] (Claim 1)

[1731] means for storing health data within the home;

[1732] means for detecting the type and quantity of ingredients;

[1733] A means for analyzing nutritional balance based on household health data and food ingredient data;

[1734] A means for generating a menu based on the analysis results;

[1735] means for detecting the user's emotional state and including that data in the analysis;

[1736] A means for displaying the generated menu;

[1737] A system including:

[1738] (Claim 2)

[1739] a means for transmitting food ingredient data to a server via the Internet;

[1740] A means for analyzing nutritional balance based on health data, emotion data and food ingredient data in the home and generating a menu in the server;

[1741] means for transmitting the generated menu to a client terminal and displaying it on the client terminal;

[1742] 10. The system of claim 1, comprising:

[1743] (Claim 3)

[1744] The client terminal is a refrigerator,

[1745] It is equipped with a camera and sensors to detect ingredients in the refrigerator.

[1746] means for transmitting food ingredient data detected by the camera and the sensor to a server;

[1747] 10. The system of claim 1, comprising: [Explanation of symbols]

[1748] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. means for storing health data within the home; means for detecting the type and quantity of ingredients; A means for analyzing nutritional balance based on household health data and food ingredient data; A means for generating a menu based on the analysis results; A means for displaying the generated menu; A system including:

2. a means for transmitting food ingredient data to a server via the Internet; A means for analyzing nutritional balance based on health data and food ingredient data in the home and generating a menu in the server; means for transmitting the generated menu to a client terminal and displaying it on the client terminal; The system of claim 1 , comprising:

3. The client terminal is a refrigerator, It is equipped with a camera and sensors to detect ingredients in the refrigerator. means for transmitting food ingredient data detected by the camera and the sensor to a server; The system of claim 1 , comprising:

Images