System

Abstract

A system is provided.SOLUTION: A system comprising: means for acquiring food material information in a refrigerator stored in a storage device by image analysis; means for analyzing a natural language input from a user to interpret a mood of the day and what the user wants to eat; means for generating an optimal menu and recipe based on the food material information in the refrigerator and the input information of the user; and means for displaying the generated menu and recipe on a user terminal.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] As home cooking becomes more common, the burden of planning daily meals and shopping increases for housewives and househusbands. Existing applications have difficulty proposing meals that reflect the user's ambiguous expressions and daily mood, making daily meal planning cumbersome. Furthermore, purchasing ingredients when they are in short supply can be time-consuming, making them difficult for users to use. [Means for solving the problem]

[0005] To solve the above-mentioned problems, the following means are provided: A system including a means for acquiring information about ingredients in a refrigerator stored in a storage device through image analysis, a means for analyzing natural language input from a user to interpret the user's mood and food preferences for that day, a means for generating optimal menus and recipes based on the information about ingredients in the refrigerator and the user's input information, a means for displaying the generated menus and recipes on a user terminal, and a means for purchasing missing ingredients by linking with an online purchasing system.The system further includes a means for storing user profile information in a storage device and proposing menus and recipes based on that information while taking into account the user's preferences and allergy information, a means for converting the user's speech into text using voice recognition technology and analyzing it using natural language processing technology, and a means for notifying the user of the generated menus and recipe information, thereby enabling easier and more effective daily meal planning.

[0006] "Refrigerator food information" is data relating to the type, quantity, and condition of food ingredients currently stored in the refrigerator.

[0007] "Image analysis" is a technique that analyzes photographs and images, identifies the objects and characters contained within them, and creates a list of them.

[0008] "Natural language input" refers to instructions or questions in human language entered by a user through speech or text.

[0009] "Natural language processing" is a technology that allows computers to understand and analyze human language.

[0010] A "menu" is a set of dishes prepared for a particular meal.

[0011] A "recipe" is a list of steps and ingredients needed to make a particular dish.

[0012] A "user terminal" is an electronic device that can be directly operated by a user, and includes smartphones, tablets, and the like.

[0013] An "online purchasing system" is a system for purchasing goods and services over the Internet.

[0014] "User profile information" is data such as personal information, preferences, and allergy information about the user.

[0015] "Voice recognition technology" is a technology that converts voice input into text data.

[0016] "Means of notification" refers to the method by which the system communicates information generated by the system to the user, and includes screen displays and voice messages. [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] Overall system configuration

[0039] This invention is a system that proposes optimal menus and recipes based on information about ingredients in the refrigerator, taking into account the user's mood and food preferences, and also allows the user to purchase ingredients online if they are running low. This system is mainly composed of users, a server, and terminals.

[0040] Overall processing of the program

[0041] Registration and Initial Setup

[0042] 1. The user downloads the application and enters their name, address, email address, favorite ingredients, and allergy information when they first launch it.

[0043] 2. The device sends this information to the server, which creates a user profile.

[0044] Photography and food recognition

[0045] 3. The user takes a photo of the refrigerator and uploads it to the app. For example, the user opens the refrigerator door and takes a photo of the inside.

[0046] 4. The device uses image analysis technology to analyze the uploaded photo and create a list of the ingredients in the refrigerator.

[0047] Input of user's mood and desires

[0048] 5. The user inputs their mood and food cravings by voice or text. For example, they might say, "I want something light today."

[0049] 6. The device converts the voice input into text and analyzes it using natural language processing technology.

[0050] 7. The device sends the analysis results to the server, which then generates the optimal menu and recipe based on the information about ingredients in the refrigerator and the information entered by the user.

[0051] Providing menus and recipes

[0052] 8. The server sends the generated menu and recipe information to the device, which displays it to the user. For example, the device suggests a menu of "tomato and cucumber salad, lightly grilled chicken, and egg soup."

[0053] Complementing missing ingredients

[0054] 9. The user checks the proposed menu and, if there are any missing ingredients, selects the "Automatically purchase missing ingredients" option.

[0055] 10. The device sends the list of ingredients needed to the server, and the server connects to the online purchasing system to purchase the ingredients. For example, if mirin is in short supply, the server will purchase it from an online supermarket.

[0056] Notification and Delivery

[0057] 11. The server returns the order details and delivery information to the terminal, and the terminal notifies the user of the order completion and delivery information. For example, it notifies the user that "Mirin will be delivered tomorrow."

[0058] Specific examples

[0059] 1. The user downloads the app and enters the required initial information.

[0060] 2. The user uploads a photo of the inside of their refrigerator to the app, and image analysis technology is used to create a list of "chicken, spinach, carrots, and onions."

[0061] 3. The user inputs "I want to eat Japanese food today" through voice input, which is then analyzed using natural language processing technology.

[0062] 4. The device sends the analysis results to the server, which generates a recipe for "chicken teriyaki, spinach ohitashi, and carrot and onion miso soup," which is then displayed on the device.

[0063] 5. The user checks the proposed menu and, if there are any missing condiments (e.g., mirin), selects "Buy automatically."

[0064] 6. The server connects to the online purchasing system to order mirin and process the delivery.

[0065] 7. The terminal notifies the user that the mirin order has been completed and the estimated delivery date.

[0066] In this way, the system can effectively utilize the ingredients in the user's refrigerator, easily provide a menu that suits the mood of the day, and realize a series of steps to purchase the necessary ingredients online.

[0067] The processing flow will be explained below.

[0068] Step 1:

[0069] The user downloads the application and enters their name, address, email address, favorite ingredients, and allergy information when they first launch it.

[0070] Step 2:

[0071] The terminal transmits the user's input information to the server.

[0072] Step 3:

[0073] The server creates a user profile based on the received user information and stores it in a database.

[0074] Step 4:

[0075] The user takes a photo of the inside of the refrigerator and uploads it to the app. For example, the user opens the refrigerator door, takes a photo of the inside, and presses the "upload" button.

[0076] Step 5:

[0077] The device receives the uploaded photo, analyzes it using OCR and image analysis technology, and generates a list of the ingredients in the refrigerator. For example, it recognizes "tomatoes, cucumbers, eggs, and chicken" from the photo.

[0078] Step 6:

[0079] The terminal transmits the ingredient list generated as a result of the analysis to the server.

[0080] Step 7:

[0081] The server updates the ingredient information in the user profile based on the received ingredient list.

[0082] Step 8:

[0083] The user inputs their mood and what they want to eat that day by voice or text. For example, they can input "I want to eat something light today."

[0084] Step 9:

[0085] The device converts the voice input into text (in the case of voice input) and analyzes it using natural language processing technology. The analyzed results are then sent to the server.

[0086] Step 10:

[0087] The server generates the optimal menu and recipe based on the information about ingredients in the refrigerator and the information entered by the user. For example, it decides on a menu of "tomato and cucumber salad, lightly grilled chicken, and egg soup."

[0088] Step 11:

[0089] The server returns the generated menu and recipe information to the terminal, and the terminal displays the menu and recipe to the user.

[0090] Step 12:

[0091] The user checks the suggested menu and recipes, and if there are any missing ingredients, selects the "Automatically purchase missing ingredients" option.

[0092] Step 13:

[0093] The terminal sends a list of ingredients that are missing to the server.

[0094] Step 14:

[0095] The server connects to the distribution system and processes the necessary ingredients for purchase online. For example, you can order the necessary mirin from an online store.

[0096] Step 15:

[0097] The server returns the order details and delivery information to the terminal, and the terminal notifies the user of the order completion and delivery information. For example, it notifies the user that "Mirin will be delivered tomorrow."

[0098] This allows users to make effective use of ingredients in their refrigerators and enjoy a menu that suits their mood that day, easily and without hassle.

[0099] Example 1

[0100] 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."

[0101] In recent years, there has been a growing need for systems that efficiently utilize ingredients in the refrigerator and propose menus that match the user's mood and preferences. However, current systems have difficulty consistently obtaining information about ingredients in the refrigerator, making proposals based on the user's mood, and even purchasing ingredients online if they are in short supply. There is also a lack of systems that can respond individually to users' preferences and allergies. There is a need for a system that solves these problems.

[0102] 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.

[0103] In this invention, the server includes means for acquiring information about ingredients in the refrigerator stored in a storage device through image analysis, means for analyzing natural language input from the user to interpret the user's mood and food preferences for that day, means for generating optimal menus and recipes based on the information about ingredients in the refrigerator and the user's input information, means for displaying the generated menus and recipes on a user terminal, means for purchasing missing ingredients by linking with an online purchasing system, means for creating a user profile based on initial information input by the user and saving it in a storage device, means for the user to take a photo of the refrigerator and upload it to an application, and means for converting voice input into text and transmitting the analysis results to the server. This makes it possible to consistently suggest optimal menus based on the ingredients in the refrigerator and automatically purchase missing ingredients.

[0104] "Storage device" refers to hardware or software for storing and retrieving data.

[0105] "Information about ingredients in the refrigerator" refers to detailed data such as the type, quantity, and shelf life of various ingredients stored in the refrigerator.

[0106] "Image analysis" refers to the technology of analyzing image data to extract specific information or features.

[0107] "Natural language input" refers to inputting human language such as voice or text.

[0108] "User profile" refers to data that includes personal information such as a user's name, address, email address, favorite foods, and allergy information.

[0109] A "menu" refers to a combination of menus and recipes for a particular meal.

[0110] A "recipe" is a detailed set of instructions on how to prepare a particular dish.

[0111] "User terminal" refers to an electronic device operated by a user, such as a smartphone or tablet.

[0112] An "online purchasing system" refers to a system that allows you to purchase products via the Internet.

[0113] "Voice recognition technology" refers to technology that converts voice data into text.

[0114] "Natural language processing technology" refers to technology that understands and analyzes human language.

[0115] "Upload" refers to the act of sending data from a local device to a remote server or cloud.

[0116] This invention is a system that proposes optimal menus and recipes based on information about ingredients in the refrigerator, taking into account the user's mood and food preferences, and also allows the user to purchase ingredients online if they are running low. This system is mainly composed of users, a server, and terminals.

[0117] Registration and Initial Setup

[0118] When a user downloads a mobile application and launches it for the first time, they enter their name, address, email address, favorite foods, and allergy information. This information is sent from the device to a server, which then uses it to create a user profile in a database. The database is built using MySQL or MongoDB.

[0119] Photography and food recognition

[0120] The user takes a photo of the refrigerator and uploads it to the app. The device analyzes the uploaded photo using image analysis technology (e.g., TensorFlow or OpenCV) and creates a list of the ingredients in the refrigerator. For example, if the app recognizes that the refrigerator contains chicken, spinach, carrots, and onions, it generates a list of these ingredients.

[0121] Input of user's mood and desires

[0122] The user inputs their mood or what they want to eat that day through voice or text input. For example, they can input "I want to eat Japanese food today." The device converts the voice to text using the Google Cloud Speech-to-Text API and analyzes the text using natural language processing techniques such as BERT. The analysis results are sent from the device to the server.

[0123] Providing menus and recipes

[0124] The server uses a generative AI model to generate optimal menus and recipes based on information about ingredients in the refrigerator and user input. The generated menu and recipe information is sent from the server to the device, which displays it to the user. For example, it might suggest a menu such as "Teriyaki chicken, boiled spinach, and miso soup with carrots and onions."

[0125] Complementing missing ingredients

[0126] The user checks the proposed menu and, if there are any missing ingredients, selects the "Automatically purchase missing ingredients" option. The device sends a list of the required ingredients to the server, which then connects to the online purchasing system to purchase the necessary ingredients. For example, if mirin (sweet rice wine) is in short supply, the API of an online supermarket is used to purchase mirin.

[0127] Notification and Delivery

[0128] The server returns the order details and delivery information to the terminal, and the terminal notifies the user of the order completion and delivery information. For example, the terminal may notify the user that "Mirin will be delivered tomorrow."

[0129] Prompt Sentence Examples

[0130] "A user uploads a photo of the inside of their refrigerator, and chicken, spinach, and carrots are listed. If they enter 'I feel like eating Japanese food today,' please suggest the best menu and recipes for them."

[0131] In this way, the system can effectively utilize the ingredients in the user's refrigerator, easily provide a menu that suits the mood of the day, and realize a series of steps to purchase the necessary ingredients online.

[0132] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0133] Step 1:

[0134] A user downloads a mobile application and enters their name, address, email address, favorite ingredients, and allergy information when they first launch it. The entered data is sent from the device to a server. The server analyzes the received data and stores it as a user profile in a database such as MySQL or MongoDB. This profile allows personalized suggestions to be made to each individual user.

[0135] Step 2:

[0136] The user takes a photo of the inside of the refrigerator and uploads it to the application. The device receives this image data and analyzes it using image analysis technology (for example, TensorFlow or OpenCV). As a result of the analysis, the recognized food data is compiled into a list and sent from the device to the server. The server records the food information in the refrigerator based on this list.

[0137] Step 3:

[0138] The user inputs their mood for the day or what they want to eat by voice or text. For example, they can input "I want to eat Japanese food today." The device converts the voice data into text using the Google Cloud Speech-to-Text API, and analyzes this text data using natural language processing technology (such as BERT). The analysis results are sent to the server as data containing the user's input intent.

[0139] Step 4:

[0140] The server uses a generative AI model to generate optimal menus and recipes based on information about ingredients in the refrigerator and information input by the user. As a specific example, if the user requests "Japanese food," the server will suggest a menu such as "chicken teriyaki, spinach ohitashi, carrot and onion miso soup" using the "chicken, spinach, carrot and onion" in the refrigerator. The generated menu and recipe information is sent from the server to the device.

[0141] Step 5:

[0142] The device displays the received menu and recipes to the user. The user checks the displayed suggestions and, if there are any missing ingredients, selects the "Automatically purchase missing ingredients" option. Upon selection, the device sends a list of missing ingredients to the server.

[0143] Step 6:

[0144] The server connects to the online purchasing system and processes the necessary ingredients. For example, if there is a shortage of mirin in the refrigerator, the server uses the online supermarket's API to purchase mirin. After the purchase process is complete, the order details and delivery information are generated.

[0145] Step 7:

[0146] The server sends the order details and delivery information to the device, which then notifies the user. For example, a push notification saying "Mirin will be delivered tomorrow" can be sent to the user's smartphone, allowing them to confirm the necessary information.

[0147] (Application example 1)

[0148] 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."

[0149] Conventional refrigerator ingredient management and menu suggestion systems require users to record and manage ingredient information themselves, which is time-consuming, and the suggested menus do not always match the user's mood or preferences. Furthermore, if necessary ingredients are in short supply, users must manually purchase them, which is time-consuming and labor-intensive. Furthermore, they lack the functionality to notify users of delivery status in real time.

[0150] 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.

[0151] In this invention, the server includes means for acquiring information about ingredients in the refrigerator stored in a storage device through image analysis, means for analyzing natural language input from the user to interpret the user's mood and food preferences for that day, means for generating optimal menus and recipes based on the information about ingredients in the refrigerator and the user's input, means for displaying the generated menus and recipes on a user terminal, means for connecting with an online purchasing system to purchase ingredients that are missing, means for connecting the generated menus and recipes with a food sales system or a distribution system using natural language processing, and means for notifying the user of delivery status in real time. This reduces the burden on the user and makes it possible to automate the entire process from ingredient management to menu suggestions, purchasing procedures for missing ingredients, and notification of delivery status.

[0152] A "storage device" is an electronic device for storing data or information.

[0153] "Image analysis" is a technology that analyzes image data to recognize and distinguish the type and shape of an object.

[0154] "Natural language input" refers to a method of inputting information using a language that the user uses on a daily basis.

[0155] A "menu" refers to the combination of dishes for a particular meal.

[0156] A "recipe" is information that describes the steps and ingredients for making a particular dish.

[0157] An "online purchasing system" is a system for ordering and purchasing products via the Internet.

[0158] "Food sales system" refers to a system for selling ingredients and food.

[0159] A "distribution system" refers to the processes and mechanisms for delivering products and services from producers to consumers.

[0160] "Natural language processing" is a technology that uses computers to understand, analyze, and generate human language.

[0161] "Delivery status notification" is a function that notifies users of the progress and status of the delivery process of ordered items.

[0162] A "user terminal" is an electronic device that allows a user to access an application or system.

[0163] "Real-time notification" is a function that instantly notifies users of events or changes that are occurring.

[0164] This invention is a system that proposes optimal menus and recipes based on the user's mood and preferences based on information about ingredients in the refrigerator, and also allows the user to purchase ingredients that are in short supply online. The main processing flow of this system is shown below.

[0165] System Configuration

[0166] This system consists of a user, a server, and a terminal. The terminal can be a smartphone, smart glasses, a head-mounted display, or a robot. The server is located in a cloud environment and includes a database, an image analysis engine, a natural language processing engine, and a module for linking with the online purchasing system.

[0167] Examples of the invention

[0168] Program generation and processing overview

[0169] 1. Obtaining ingredient information:

[0170] Users take a photo of the inside of the refrigerator using the camera on their smartphone and upload it to the device. This photo is sent to the server, which uses an image analysis engine (e.g., OpenCV or TensorFlow) to recognize the ingredients in the refrigerator and store them as a list in a database.

[0171] 2. Parsing user input:

[0172] Users input their mood or what they want to eat that day into the device by voice or text. In the case of voice input, the device converts the input into text using speech recognition technology (e.g., Google Speech-to-Text API). The converted text is sent to the server and analyzed by a natural language processing engine (e.g., NLTK or SpaCy).

[0173] 3. Menu and recipe suggestions:

[0174] The server generates optimal menus and recipes based on information about ingredients in the refrigerator and user input. A generative AI model is used for generation. The generated menu and recipe information is sent to the device and displayed to the user.

[0175] 4. Automatically purchase ingredients when they are in short supply:

[0176] The user checks the proposed menu and, if any ingredients are missing, selects the "Buy automatically" option on the device. The server connects to the online purchasing system and automatically orders the necessary ingredients. Payment is made using an API (e.g., Stripe or PayPal).

[0177] 5. Delivery Status Notification:

[0178] The server monitors order details and delivery information in real time and notifies the terminal of the delivery status using services such as Firebase Cloud Messaging.

[0179] Specific examples

[0180] The user voice-inputs "I want to eat Italian today" into the device. By taking a photo of the contents of the refrigerator and uploading it to the app, "tomatoes, basil, and mozzarella cheese" are recognized. Based on this information, the server suggests "Caprese salad" and, if any necessary ingredients are missing, automatically completes the online purchase process. Delivery status is notified to the user's device in real time.

[0181] Prompt Sentence Examples

[0182] When a user types "I want to eat Italian food today," the prompt text is:

[0183] I want to eat Italian food today

[0184] In this way, the system of the present invention can perform intelligent ingredient management and menu suggestions, thereby increasing user convenience.

[0185] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0186] Step 1:

[0187] A user takes a photo of the inside of the refrigerator using a device and uploads it to the application. The input is the captured image, and the output is the image data sent to the server.

[0188] Step 2:

[0189] The server analyzes the received image data using an image analysis engine (e.g., OpenCV or TensorFlow). By analyzing the image, it creates a list of ingredients in the refrigerator and stores it in a database. The input is the image data, and the output is the analyzed list of ingredients in the refrigerator.

[0190] Step 3:

[0191] The user inputs their mood for the day and what they want to eat into the device by voice or text. The input is voice or text data. When voice input is used, the device converts the voice into text using voice recognition technology (for example, Google Speech-to-Text API). The output is text data.

[0192] Step 4:

[0193] The device sends the generated text data to the server, which then uses a natural language processing engine (e.g., NLTK or SpaCy) to analyze the user's preferences. The input is the text data, and the output is the analyzed preference information.

[0194] Step 5:

[0195] The server uses a generative AI model to generate optimal menus and recipes based on the information about ingredients in the refrigerator and the user's preferences. The input is the information about ingredients and preferences, and the output is the generated menu and recipes.

[0196] Step 6:

[0197] The server sends the generated menu and recipe information to the terminal, which displays it to the user. The input is the menu and recipe information, and the output is the display to the user.

[0198] Step 7:

[0199] The user reviews the proposed menu and, if there are any missing ingredients, selects the "Buy automatically" option on the device. The input is the user's selection, and the output is a list of missing ingredients.

[0200] Step 8:

[0201] The server connects to an online purchasing system (e.g., Amazon or Rakuten) based on the list of ingredients that are in short supply and orders the necessary ingredients. The input is the list of ingredients that are in short supply, and the output is a notification that the ordering process has been completed.

[0202] Step 9:

[0203] The server monitors order details and delivery information in real time and notifies the terminal of the delivery status. The input is order and delivery information, and the output is a notification to the user.

[0204] Specific examples

[0205] The user voice-records "I want to eat Italian food today" into the device and uploads a photo of what's in the refrigerator. The server analyzes the image and recognizes "tomatoes, basil, and mozzarella cheese," and based on this, suggests "Caprese salad." The user selects the option to automatically purchase ingredients that are running low (e.g., olive oil), and the server links with the online purchasing system to place the order. The user is notified of delivery status in real time.

[0206] 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.

[0207] Overall system configuration

[0208] This invention is a system that proposes optimal menus and recipes based on information about ingredients in the refrigerator, taking into consideration the user's mood, what they want to eat, and even their emotions, and also allows them to purchase ingredients they are running low on online.The system is mainly composed of a user, a server, a terminal, and an emotion engine.

[0209] Overall processing of the program

[0210] Registration and Initial Setup

[0211] 1. The user downloads the application and enters their name, address, email address, favorite ingredients, and allergy information when they first launch it.

[0212] 2. The device sends this information to the server, which creates a user profile.

[0213] Photography and food recognition

[0214] 3. The user takes a photo of the refrigerator and uploads it to the app. For example, the user opens the refrigerator door and takes a photo of the inside.

[0215] 4. The device uses image analysis technology to analyze the uploaded photos and generate a list of ingredients in the refrigerator.

[0216] Input of user's mood, desires, and emotions

[0217] 5. The user inputs their mood and food cravings by voice or text. For example, they might say, "I want something light today."

[0218] 6. The device converts the voice input into text (in the case of voice input) and analyzes it using natural language processing technology. The analyzed results are sent to the server.

[0219] 7. The emotion engine analyzes the user's input and obtains emotion information. For example, if the user is using a facial recognition camera, it will determine the emotion from their facial expressions.

[0220] 8. The device also sends the analyzed emotional information to the server.

[0221] Menu and recipe generation

[0222] 9. The server generates the optimal menu and recipe based on the information about ingredients in the refrigerator, the user's input, and emotional information. For example, it decides on a menu of "tomato and cucumber salad, lightly grilled chicken, and egg soup."

[0223] 10. The server sends the generated menu and recipe information to the terminal, which displays it to the user.

[0224] Complementing missing ingredients

[0225] 11. The user reviews the suggested menu and recipes and, if there are any missing ingredients, selects the "Automatically purchase missing ingredients" option.

[0226] 12. The device sends a list of ingredients that are missing to the server.

[0227] 13. The server connects to the distribution system and processes the necessary ingredients for purchase online. For example, the customer orders the necessary mirin from an online store.

[0228] Notification and Delivery

[0229] 14. The server returns the order details and delivery information to the terminal, and the terminal notifies the user of the order completion and delivery information. For example, it notifies the user that "Mirin will be delivered tomorrow."

[0230] Specific examples

[0231] 1. The user downloads the app and enters the required initial information.

[0232] 2. The user uploads a photo of the inside of their refrigerator to the app, and image analysis technology is used to create a list of "chicken, spinach, carrots, and onions."

[0233] 3. The user inputs "I want to eat Japanese food today" through voice input, which is then analyzed using natural language processing technology.

[0234] 4. The emotion engine obtains emotional information from the user's input and facial expressions and determines whether the user is in a "relaxed mood."

[0235] 5. The device sends this information to the server, which then generates a recipe for "Teriyaki chicken, boiled spinach, and miso soup with carrots and onions," which the device then displays.

[0236] 6. The user checks the proposed menu and, if there are any missing condiments (e.g., mirin), selects "Buy automatically."

[0237] 7. The server connects to the online purchasing system to order mirin and process the delivery.

[0238] 8. The terminal notifies the user that the mirin order has been completed and the estimated delivery date.

[0239] In this way, the system can effectively utilize the ingredients in the user's refrigerator, easily provide a menu that matches the user's mood or emotions that day, and enable the user to purchase the necessary ingredients online.

[0240] The processing flow will be explained below.

[0241] Step 1:

[0242] The user downloads the application and enters their name, address, email address, favorite ingredients, and allergy information when they first launch it.

[0243] Step 2:

[0244] The terminal transmits this user information to the server.

[0245] Step 3:

[0246] The server creates a user profile based on the received user information and stores it in a database.

[0247] Step 4:

[0248] The user takes a photo of the inside of the refrigerator and uploads it to the app. For example, the user opens the refrigerator door and takes a photo of the inside.

[0249] Step 5:

[0250] The device receives the uploaded photo, analyzes it using image analysis technology (such as OCR), and identifies the ingredients in the refrigerator to generate a list. For example, it recognizes "tomatoes, cucumbers, eggs, and chicken" from the photo.

[0251] Step 6:

[0252] The terminal transmits the analyzed ingredient list to the server.

[0253] Step 7:

[0254] The server updates the user profile based on the received ingredient list.

[0255] Step 8:

[0256] The user inputs their mood and what they want to eat that day by voice or text. For example, they might say, "I want to eat something light today."

[0257] Step 9:

[0258] The device converts the voice input into text (if voice input is used) and analyzes it using natural language processing technology. The analyzed information is then sent to the server.

[0259] Step 10:

[0260] The emotion engine infers the user's emotions based on the user's input, for example, by using a facial recognition camera to determine if the user wants to relax.

[0261] Step 11:

[0262] The terminal also transmits emotion information acquired by the emotion engine to the server.

[0263] Step 12:

[0264] The server generates optimal menus and recipes based on information about ingredients in the refrigerator, user input, and emotional information. For example, it suggests a menu of "tomato and cucumber salad, lightly grilled chicken, and egg soup."

[0265] Step 13:

[0266] The server sends the generated menu and recipe information to the terminal, which then displays it to the user. For example, the menu and recipes for each dish are displayed.

[0267] Step 14:

[0268] The user checks the suggested menu and recipes, and if there are any missing ingredients, selects the "Automatically purchase missing ingredients" option.

[0269] Step 15:

[0270] The terminal sends a list of ingredients that are missing to the server.

[0271] Step 16:

[0272] The server connects to the online purchasing system and processes the necessary ingredients. For example, the server orders the necessary mirin from the online store.

[0273] Step 17:

[0274] The server returns the order details and delivery information to the terminal. The terminal notifies the user of the order completion and delivery information. For example, it notifies the user that "Mirin will be delivered tomorrow."

[0275] This series of processes allows users to efficiently use the ingredients in their refrigerator, easily plan a menu that matches their mood or emotions for the day, and purchase the necessary ingredients online.

[0276] Example 2

[0277] 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."

[0278] In modern society, busy lives mean limited time for planning menus, making it difficult to propose meals that take into account the user's mood, health status, individual preferences, and allergies. Another problem is the lack of systems that can effectively utilize ingredients in the refrigerator while automatically purchasing ingredients that are in short supply. To solve this situation, a system is needed that proposes optimal menus based on the ingredients in the user's refrigerator and their mood and emotions for the day, and automatically purchases the necessary ingredients online.

[0279] 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.

[0280] In this invention, the server includes means for acquiring information about ingredients in the refrigerator stored in a storage device through image analysis, means for analyzing natural language input from the user to interpret the user's mood and food preferences for that day, means for generating optimal menus and recipes based on the information about ingredients in the refrigerator and the user's input and emotional information, means for displaying the generated menus and recipes on a user terminal, means for purchasing ingredients that are in short supply by connecting to an online purchasing system, and means for acquiring emotional information by recognizing the user's facial expression. This makes the user's life more comfortable and convenient, allowing them to easily decide on daily menus and automatically replenish necessary ingredients.

[0281] "Storage device" refers to hardware or software for storing data, and specifically includes hard disk drives and solid-state drives.

[0282] "Image analysis" is a technology that uses photographs or image data to recognize specific objects or extract specific information.

[0283] "Natural language input" is a method for allowing a system to understand and analyze the content of input in the language (voice or text) that a user uses on a daily basis.

[0284] "Menu" refers to the combination and order of dishes served at a meal.

[0285] A "recipe" is a set of instructions that outlines the steps and ingredient combinations required to prepare a particular dish.

[0286] A "user terminal" is a device that is directly operated by a user, and specifically refers to electronic devices such as smartphones and tablets.

[0287] An "online purchasing system" is a system for purchasing products via the Internet, and specifically includes online stores and e-commerce platforms.

[0288] "User profile information" is a collection of individual pieces of information related to a user, including name, address, email address, favorite ingredients, allergy information, and the like.

[0289] "Speech recognition technology" is a technology that analyzes input voice data and converts it into text data.

[0290] "Natural language processing technology" is a technology that allows computers to understand and analyze natural human language, and specifically includes semantic analysis and context understanding.

[0291] "Emotion information" is information relating to the user's mood or emotional state, and specifically is data indicating an emotional state such as "I want to relax" or "I feel stressed."

[0292] This invention is a system that proposes optimal menus and recipes based on information about ingredients in the refrigerator, taking into consideration the user's mood, what they want to eat, and even their emotions, and also allows them to purchase ingredients they are running low on online.The system is mainly composed of a server, terminals, users, and an emotion engine.

[0293] Registration and Initial Setup

[0294] A user downloads and installs the application onto a device such as a smartphone or tablet. When the application is launched for the first time, the user enters their name, address, email address, favorite foods, allergy information, etc. The device sends this information to the server, which then creates a user profile based on the received information and stores it in a database.

[0295] Photography and food recognition

[0296] The user takes a photo of the inside of the refrigerator and uploads it to the application. The device analyzes the photo using image analysis technology (e.g., Google Cloud Vision API), creates a list of the ingredients in the refrigerator, and sends it to the server.

[0297] Input of user's mood, desires, and emotions

[0298] The user tells the app how they feel that day and what they want to eat by voice or text input. For example, they might say, "I want something light today." The device uses voice recognition technology (e.g., Google Cloud Speech-to-Text) to convert the speech into text, which is then analyzed using natural language processing technology (e.g., OpenAI GPT-4). The analysis results are sent to the server.

[0299] The emotion engine analyzes emotional information based on the user's input data and data acquired from the facial recognition camera. For example, it analyzes emotional information such as "I feel like relaxing." The device then sends this emotional information to the server.

[0300] Menu and recipe generation

[0301] The server generates the optimal menu and recipes based on information about ingredients in the refrigerator, the user's mood, preferences, and emotional information. For example, it generates a menu such as "tomato and cucumber salad, lightly grilled chicken, and egg soup." The server sends the generated menu and recipe information to the terminal, which then displays the information to the user.

[0302] Complementing missing ingredients

[0303] The user checks the provided menu and recipe, and if there are any missing ingredients, selects the "Automatically purchase missing ingredients" option. The device sends a list of the missing ingredients to the server. The server then connects to the online purchasing system and carries out the procedure to purchase the missing ingredients online. For example, the user orders the required "mirin" from an online store.

[0304] Notification and Delivery

[0305] The server returns the order details and delivery information to the terminal, and the terminal notifies the user of the order completion and delivery information. For example, it notifies the user that "Mirin will be delivered the next day."

[0306] Examples of specific examples and prompts

[0307] Specific examples

[0308] The user downloads the app and enters initial information such as name, address, email address, favorite ingredients, and allergy information.

[0309] Users take a photo of the inside of their refrigerator and upload it to the app, which uses image analysis technology to analyze the photo and produce a list of "chicken, spinach, carrots, and onions."

[0310] The user inputs "I want to eat Japanese food today" by voice, and the device converts this into text using voice recognition technology, which is then analyzed using natural language processing technology.

[0311] The emotion engine determines whether you feel like relaxing based on input and facial expression data.

[0312] The device sends information about ingredients, mood, and emotions to the server, and the server generates a recipe for "teriyaki chicken, boiled spinach, and miso soup with carrots and onions," which is then displayed on the device.

[0313] The user reviews the suggested menu and selects the option to automatically purchase any mirin they are running low on.

[0314] The server connects to the online purchasing system to order mirin and process the delivery.

[0315] The terminal notifies the user of the order completion and estimated delivery date.

[0316] Prompt Sentence Examples

[0317] "Please upload a photo of the inside of your refrigerator and list the ingredients in it."

[0318] "I want to eat Japanese food today. I'd like you to suggest a relaxing menu."

[0319] "Show me recipes for chicken teriyaki, spinach ohitashi, and carrot and onion miso soup."

[0320] "Automatically purchase mirin online if I'm running low on it."

[0321] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0322] System processing steps

[0323] Step 1: Enter your user information and create your profile

[0324] The user downloads and installs the application.

[0325] Input: Name, address, email address, favorite ingredients, allergy information.

[0326] Specific operations: Launch the app and enter the required information on the initial setup screen.

[0327] The terminal transmits the input information to the server.

[0328] Output: The user profile is saved on the server.

[0329] Step 2: Take a photo of the inside of your refrigerator and upload it

[0330] A user opens the refrigerator door and takes a photo of the inside.

[0331] Input: A photo of the inside of a refrigerator.

[0332] Specific operation: Take a photo of the inside of the refrigerator using your smartphone camera and upload it to the app.

[0333] The device analyzes the uploaded photos using image analysis technology (e.g., Google Cloud Vision API).

[0334] Output: A list of ingredients in the refrigerator is generated.

[0335] Step 3: Submit and save ingredient information

[0336] The terminal transmits the generated ingredient list to the server.

[0337] Input: Ingredient list.

[0338] Specific operation: The ingredient list obtained through image analysis technology is sent to the server.

[0339] The server stores the ingredient information in a database.

[0340] Output: Ingredient information is saved in the database.

[0341] Step 4: Enter the user's mood and wishes

[0342] The user inputs their mood and what they want to eat that day using voice or text.

[0343] Input: Voice or text to indicate your mood or what you want to eat that day.

[0344] What it does: Enter your desired eating style using the app's input field or voice input function.

[0345] The device uses voice recognition technology (e.g., Google Cloud Speech-to-Text) to convert the speech into text, which is then analyzed using natural language processing technology (e.g., OpenAI GPT-4).

[0346] Output: The analyzed mood and preference information is available in text format.

[0347] Step 5: Send and save your mood and preferences

[0348] The device sends the analyzed mood and preference information to the server.

[0349] Input: Mood and preference information in text format.

[0350] Specific operation: Executes a process to send the analysis results to the server.

[0351] The server stores mood and preference information in a database.

[0352] Output: Mood and preference information is saved in a database.

[0353] Step 6: Acquiring and transmitting emotional information

[0354] The emotion engine recognizes the user's facial expressions and analyzes emotional information.

[0355] Input: User's facial expression data.

[0356] Specific operation: Using a facial expression recognition camera, the user's facial expressions are captured and their emotions are analyzed.

[0357] The emotion engine sends the emotion information to the server.

[0358] Output: Emotion information is saved in text format on the server.

[0359] Step 7: Create menus and recipes

[0360] The server combines information about ingredients in the refrigerator with the user's mood, preferences, and emotional information to generate optimal menus and recipes.

[0361] Input: Ingredient information, mood and preference information, emotional information.

[0362] Specific operation: Extracts information on ingredients, mood, preferences, and emotions from the database, and analyzes them comprehensively to generate appropriate menus and recipes.

[0363] Output: Generated menu and recipe information.

[0364] Step 8: Send and display menu and recipe information

[0365] The server sends the generated menu and recipe information to the terminal.

[0366] Input: Menu and recipe information.

[0367] Specific operation: The generated menu and recipes are sent to the user's terminal and presented to the user through the display function.

[0368] Output: Menu and recipes displayed on the terminal.

[0369] Step 9: Select the option to automatically purchase ingredients when they run out

[0370] The user checks the menu and, if there are any missing ingredients, selects the "Automatically purchase missing ingredients" option.

[0371] Input: User's choice.

[0372] What it does: Check menus and recipes, then tap to select the option you want.

[0373] The terminal sends a list of ingredients that are missing to the server.

[0374] Output: List of missing ingredients.

[0375] Step 10: Purchase missing ingredients online

[0376] The server connects to the online purchasing system and carries out the procedure to purchase the necessary ingredients.

[0377] Input: List of missing ingredients.

[0378] Specific operation: Use the API of the online purchasing system to automatically proceed with the purchase procedure and start the process to order the specified ingredients.

[0379] Output: Order completed notification.

[0380] Step 11: Order and Shipping Information Notification

[0381] The server returns the order details and delivery information to the terminal.

[0382] Input: Order details and shipping information.

[0383] Specific operation: After the order is completed, the contents and estimated delivery date are notified to the user's terminal.

[0384] The terminal notifies the user of the order completion and delivery information.

[0385] Output: The user receives a notification of order completion and shipping information.

[0386] The above are the specific processing steps of this system.

[0387] (Application example 2)

[0388] 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."

[0389] While conventional food management systems have the ability to suggest recipes to users based on information about ingredients in the refrigerator, many lack the ability to link with user emotions and store inventory information, making it impossible to suggest recipes that best suit the user's current mood or emotions. Furthermore, they lack the ability to identify the location of ingredients that are missing in the store, which can make the user's shopping experience more complicated.

[0390] The identification process by the identification 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 means for acquiring ingredient information stored in a storage device through image analysis, means for analyzing natural language input from the user to interpret the user's mood and food preferences for that day, means for analyzing the user's emotional information and optimizing menus and recipes based on the analysis, means for linking with in-store inventory information to provide information on the location of missing ingredients, means for displaying the generated menus and recipes on the user's terminal, and means for linking with an online purchasing system to purchase missing ingredients. This enables the server to suggest optimal recipes tailored to the user's mood and emotions and provide a smooth shopping experience in the store.

[0391] A "storage device" is hardware or software for storing data and information.

[0392] "Food information" refers to data such as the type, quantity, and freshness of food and ingredients in the refrigerator or store.

[0393] "Image analysis" is a technique for analyzing image data and extracting useful information from it.

[0394] "Natural language input" refers to the input of instructions or questions by a user using everyday language, either by voice or text.

[0395] "User input" means data provided to the system by a user through natural language input or other means.

[0396] "Menu" refers to the entire meal menu for that day.

[0397] A "recipe" is a detailed set of instructions and ingredients for making a particular dish.

[0398] "Emotional information" is data that indicates the user's emotional state and is obtained from facial expression analysis, voice tone, etc.

[0399] A "user terminal" is a device that is directly operated by a user, such as a smartphone, smart glasses, or a head-mounted display.

[0400] An "online purchasing system" is a platform for purchasing products via the Internet.

[0401] "Store inventory information" is data on products and ingredients currently available in the store.

[0402] "Guidance means" refers to a method or system that shows the user the location of a destination or product.

[0403] This invention is an in-store ingredient management and recommendation system that proposes optimal menus and recipes based on the user's mood and emotions, and also supports the purchase of ingredients that are in short supply. The system is mainly composed of a storage device, a user terminal, a server, an emotion engine, and an in-store inventory information system.

[0404] Hardware and software used

[0405] Hardware:

[0406] Smartphones, smart glasses, head-mounted displays (user devices)

[0407] Camera (for facial expression analysis)

[0408] software:

[0409] Image analysis libraries (e.g., Firebase ML Kit)

[0410] Natural language processing engine (e.g. Google Cloud Natural Language API)

[0411] Sentiment analysis engine (e.g. Affectiva SDK)

[0412] Cloud databases (e.g. Firebase Firestore)

[0413] Online purchasing system API (e.g. Amazon API)

[0414] Processing flow

[0415] 1. User Profile Management:

[0416] A user installs the app and enters initial information such as their name, address, email address, favorite ingredients, allergy information, etc. This user information is stored in a cloud database (Firebase Firestore).

[0417] 2. Food recognition:

[0418] The user takes a photo of the food shelves in the store using smart glasses or a smartphone. The image data is analyzed using an image analysis library (Firebase ML Kit) to obtain food information.

[0419] 3. Parsing user-entered information:

[0420] The user inputs something like "I want to eat something light today" via voice or text. This input is converted into text using speech recognition technology, and then analyzed by a natural language processing engine (Google Cloud Natural Language API).

[0421] 4. Acquiring emotional information:

[0422] The user's facial expressions are captured by the camera in the smart glasses or head-mounted display and analyzed by the emotion analysis engine (Affectiva SDK), thereby obtaining the user's emotional information.

[0423] 5. Menu and recipe generation:

[0424] The server generates optimal menus and recipes based on the ingredient information stored in the cloud database, the user's input information, and emotional information. This information is displayed on the user's device.

[0425] 6. Purchase and guidance of missing ingredients:

[0426] A list of missing ingredients is generated based on the recipe. This is checked against the store's inventory information, and if any are missing, they are ordered and purchased through the online purchasing system (Amazon API). If the missing ingredients are available in the store, the user is informed of their location.

[0427] Specific examples

[0428] 1. While walking around the store, the user scans the shelves with smart glasses and then voice-inputs, "I'd like to eat a light Japanese meal today."

[0429] 2. The system recognizes "salmon, tofu, and green onions" from an image of a product shelf, and, combined with the user information, suggests "grilled salmon with salt and tofu miso soup."

[0430] 3. The app will notify you when you are low on soy sauce and use your location information in the store to guide you to the soy sauce section.

[0431] Prompt Sentence Examples

[0432] You're using an "intelligent shopping assistant" app. It analyzes photos of shelves you take in a store and suggests recipes based on ingredients that fit your current mood. You enter, "I want something light today."

[0433] This will enable the system to suggest optimal recipes that match the user's mood and emotions, as well as provide a smooth shopping experience in stores.

[0434] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0435] Step 1:

[0436] Users install the application and enter their name, address, email address, favorite foods, and allergy information. This initial information is stored in a cloud database. The user's personal information and preferences are used as input data to create a user profile. The server uses this data to build an individual user profile.

[0437] Step 2:

[0438] The user takes a photo of the food shelves in the store using smart glasses or a smartphone and uploads it to the app. The image data is analyzed using an image analysis library (e.g., Firebase ML Kit) to extract information about the ingredients. The input is the captured image data, and the output is a list of analyzed ingredients. The device displays this list and also sends it to the server.

[0439] Step 3:

[0440] The user inputs a voice or text message such as "I want something light today." In the case of voice input, the message is converted into text using speech recognition technology and analyzed by a natural language processing engine (e.g., Google Cloud Natural Language API). The input data is voice or text, and the output is text data that reflects the analyzed user's mood and preferences. The server uses the results of this analysis to generate menu suggestions.

[0441] Step 4:

[0442] The user's facial expressions are captured by a camera in smart glasses or a head-mounted display and analyzed by an emotion analysis engine (e.g., Affectiva SDK). The captured image or video data is used as input data, and the output is the user's emotional information. This emotional information is sent to a server and analyzed together with the user's mood and wishes.

[0443] Step 5:

[0444] The server generates optimal menus and recipes based on ingredient information stored in the cloud database, user input information, and emotional information. The input data is the ingredient list, the user's preferences and mood information, and emotional information, and the output is optimized menus and recipes. The generated information is sent to the user's device and displayed to the user.

[0445] Step 6:

[0446] The terminal displays the generated menu and recipes to the user, and also presents a list of missing ingredients. The input data are the generated menu and recipes, and the output is the list of missing ingredients.

[0447] Step 7:

[0448] The server checks the store's inventory information and, if there are shortages, orders and purchases ingredients through an online purchasing system (e.g., Amazon API). If the missing ingredients are available in the store, the server guides the user to their location. The input data is inventory information and a list of ingredients that are in short supply, and the output is purchase completion information and in-store guidance information. The terminal notifies the user of this.

[0449] In this way, a system is formed that inputs, analyzes, and outputs data at each step to provide the user with the optimal experience.

[0450] 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.

[0451] 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.

[0452] 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.

[0453] [Second embodiment]

[0454] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0455] 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.

[0456] 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).

[0457] 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.

[0458] 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.

[0459] 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).

[0460] 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.

[0461] 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.

[0462] 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.

[0463] 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.

[0464] 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.

[0465] 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."

[0466] Overall system configuration

[0467] This invention is a system that proposes optimal menus and recipes based on information about ingredients in the refrigerator, taking into account the user's mood and food preferences, and also allows the user to purchase ingredients online if they are running low. This system is mainly composed of users, a server, and terminals.

[0468] Overall processing of the program

[0469] Registration and Initial Setup

[0470] 1. The user downloads the application and enters their name, address, email address, favorite ingredients, and allergy information when they first launch it.

[0471] 2. The device sends this information to the server, which creates a user profile.

[0472] Photography and food recognition

[0473] 3. The user takes a photo of the refrigerator and uploads it to the app. For example, the user opens the refrigerator door and takes a photo of the inside.

[0474] 4. The device uses image analysis technology to analyze the uploaded photo and create a list of the ingredients in the refrigerator.

[0475] Input of user's mood and desires

[0476] 5. The user inputs their mood and food cravings by voice or text. For example, they might say, "I want something light today."

[0477] 6. The device converts the voice input into text and analyzes it using natural language processing technology.

[0478] 7. The device sends the analysis results to the server, which then generates the optimal menu and recipe based on the information about ingredients in the refrigerator and the information entered by the user.

[0479] Providing menus and recipes

[0480] 8. The server sends the generated menu and recipe information to the device, which displays it to the user. For example, the device suggests a menu of "tomato and cucumber salad, lightly grilled chicken, and egg soup."

[0481] Complementing missing ingredients

[0482] 9. The user checks the proposed menu and, if there are any missing ingredients, selects the "Automatically purchase missing ingredients" option.

[0483] 10. The device sends the list of ingredients needed to the server, and the server connects to the online purchasing system to purchase the ingredients. For example, if mirin is in short supply, the server will purchase it from an online supermarket.

[0484] Notification and Delivery

[0485] 11. The server returns the order details and delivery information to the terminal, and the terminal notifies the user of the order completion and delivery information. For example, it notifies the user that "Mirin will be delivered tomorrow."

[0486] Specific examples

[0487] 1. The user downloads the app and enters the required initial information.

[0488] 2. The user uploads a photo of the inside of their refrigerator to the app, and image analysis technology is used to create a list of "chicken, spinach, carrots, and onions."

[0489] 3. The user inputs "I want to eat Japanese food today" through voice input, which is then analyzed using natural language processing technology.

[0490] 4. The device sends the analysis results to the server, which generates a recipe for "chicken teriyaki, spinach ohitashi, and carrot and onion miso soup," which is then displayed on the device.

[0491] 5. The user checks the proposed menu and, if there are any missing condiments (e.g., mirin), selects "Buy automatically."

[0492] 6. The server connects to the online purchasing system to order mirin and process the delivery.

[0493] 7. The terminal notifies the user that the mirin order has been completed and the estimated delivery date.

[0494] In this way, the system can effectively utilize the ingredients in the user's refrigerator, easily provide a menu that suits the mood of the day, and realize a series of steps to purchase the necessary ingredients online.

[0495] The processing flow will be explained below.

[0496] Step 1:

[0497] The user downloads the application and enters their name, address, email address, favorite ingredients, and allergy information when they first launch it.

[0498] Step 2:

[0499] The terminal transmits the user's input information to the server.

[0500] Step 3:

[0501] The server creates a user profile based on the received user information and stores it in a database.

[0502] Step 4:

[0503] The user takes a photo of the inside of the refrigerator and uploads it to the app. For example, the user opens the refrigerator door, takes a photo of the inside, and presses the "upload" button.

[0504] Step 5:

[0505] The device receives the uploaded photo, analyzes it using OCR and image analysis technology, and generates a list of the ingredients in the refrigerator. For example, it recognizes "tomatoes, cucumbers, eggs, and chicken" from the photo.

[0506] Step 6:

[0507] The terminal transmits the ingredient list generated as a result of the analysis to the server.

[0508] Step 7:

[0509] The server updates the ingredient information in the user profile based on the received ingredient list.

[0510] Step 8:

[0511] The user inputs their mood and what they want to eat that day by voice or text. For example, they can input "I want to eat something light today."

[0512] Step 9:

[0513] The device converts the voice input into text (in the case of voice input) and analyzes it using natural language processing technology. The analyzed results are then sent to the server.

[0514] Step 10:

[0515] The server generates the optimal menu and recipe based on the information about ingredients in the refrigerator and the information entered by the user. For example, it decides on a menu of "tomato and cucumber salad, lightly grilled chicken, and egg soup."

[0516] Step 11:

[0517] The server returns the generated menu and recipe information to the terminal, and the terminal displays the menu and recipe to the user.

[0518] Step 12:

[0519] The user checks the suggested menu and recipes, and if there are any missing ingredients, selects the "Automatically purchase missing ingredients" option.

[0520] Step 13:

[0521] The terminal sends a list of ingredients that are missing to the server.

[0522] Step 14:

[0523] The server connects to the distribution system and processes the necessary ingredients for purchase online. For example, you can order the necessary mirin from an online store.

[0524] Step 15:

[0525] The server returns the order details and delivery information to the terminal, and the terminal notifies the user of the order completion and delivery information. For example, it notifies the user that "Mirin will be delivered tomorrow."

[0526] This allows users to make effective use of ingredients in their refrigerators and enjoy a menu that suits their mood that day, easily and without hassle.

[0527] Example 1

[0528] 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."

[0529] In recent years, there has been a growing need for systems that efficiently utilize ingredients in the refrigerator and propose menus that match the user's mood and preferences. However, current systems have difficulty consistently obtaining information about ingredients in the refrigerator, making proposals based on the user's mood, and even purchasing ingredients online if they are in short supply. There is also a lack of systems that can respond individually to users' preferences and allergies. There is a need for a system that solves these problems.

[0530] 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.

[0531] In this invention, the server includes means for acquiring information about ingredients in the refrigerator stored in a storage device through image analysis, means for analyzing natural language input from the user to interpret the user's mood and food preferences for that day, means for generating optimal menus and recipes based on the information about ingredients in the refrigerator and the user's input information, means for displaying the generated menus and recipes on a user terminal, means for purchasing missing ingredients by linking with an online purchasing system, means for creating a user profile based on initial information input by the user and saving it in a storage device, means for the user to take a photo of the refrigerator and upload it to an application, and means for converting voice input into text and transmitting the analysis results to the server. This makes it possible to consistently suggest optimal menus based on the ingredients in the refrigerator and automatically purchase missing ingredients.

[0532] "Storage device" refers to hardware or software for storing and retrieving data.

[0533] "Information about ingredients in the refrigerator" refers to detailed data such as the type, quantity, and shelf life of various ingredients stored in the refrigerator.

[0534] "Image analysis" refers to the technology of analyzing image data to extract specific information or features.

[0535] "Natural language input" refers to inputting human language such as voice or text.

[0536] "User profile" refers to data that includes personal information such as a user's name, address, email address, favorite foods, and allergy information.

[0537] A "menu" refers to a combination of menus and recipes for a particular meal.

[0538] A "recipe" is a detailed set of instructions on how to prepare a particular dish.

[0539] "User terminal" refers to an electronic device operated by a user, such as a smartphone or tablet.

[0540] An "online purchasing system" refers to a system that allows you to purchase products via the Internet.

[0541] "Voice recognition technology" refers to technology that converts voice data into text.

[0542] "Natural language processing technology" refers to technology that understands and analyzes human language.

[0543] "Upload" refers to the act of sending data from a local device to a remote server or cloud.

[0544] This invention is a system that proposes optimal menus and recipes based on information about ingredients in the refrigerator, taking into account the user's mood and food preferences, and also allows the user to purchase ingredients online if they are running low. This system is mainly composed of users, a server, and terminals.

[0545] Registration and Initial Setup

[0546] When a user downloads a mobile application and launches it for the first time, they enter their name, address, email address, favorite foods, and allergy information. This information is sent from the device to a server, which then uses it to create a user profile in a database. The database is built using MySQL or MongoDB.

[0547] Photography and food recognition

[0548] The user takes a photo of the refrigerator and uploads it to the app. The device analyzes the uploaded photo using image analysis technology (e.g., TensorFlow or OpenCV) and creates a list of the ingredients in the refrigerator. For example, if the app recognizes that the refrigerator contains chicken, spinach, carrots, and onions, it generates a list of these ingredients.

[0549] Input of user's mood and desires

[0550] The user inputs their mood or what they want to eat that day through voice or text input. For example, they can input "I want to eat Japanese food today." The device converts the voice to text using the Google Cloud Speech-to-Text API and analyzes the text using natural language processing techniques such as BERT. The analysis results are sent from the device to the server.

[0551] Providing menus and recipes

[0552] The server uses a generative AI model to generate optimal menus and recipes based on information about ingredients in the refrigerator and user input. The generated menu and recipe information is sent from the server to the device, which displays it to the user. For example, it might suggest a menu such as "Teriyaki chicken, boiled spinach, and miso soup with carrots and onions."

[0553] Complementing missing ingredients

[0554] The user checks the proposed menu and, if there are any missing ingredients, selects the "Automatically purchase missing ingredients" option. The device sends a list of the required ingredients to the server, which then connects to the online purchasing system to purchase the necessary ingredients. For example, if mirin (sweet rice wine) is in short supply, the API of an online supermarket is used to purchase mirin.

[0555] Notification and Delivery

[0556] The server returns the order details and delivery information to the terminal, and the terminal notifies the user of the order completion and delivery information. For example, the terminal may notify the user that "Mirin will be delivered tomorrow."

[0557] Prompt Sentence Examples

[0558] "A user uploads a photo of the inside of their refrigerator, and chicken, spinach, and carrots are listed. If they enter 'I feel like eating Japanese food today,' please suggest the best menu and recipes for them."

[0559] In this way, the system can effectively utilize the ingredients in the user's refrigerator, easily provide a menu that suits the mood of the day, and realize a series of steps to purchase the necessary ingredients online.

[0560] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0561] Step 1:

[0562] A user downloads a mobile application and enters their name, address, email address, favorite ingredients, and allergy information when they first launch it. The entered data is sent from the device to a server. The server analyzes the received data and stores it as a user profile in a database such as MySQL or MongoDB. This profile allows personalized suggestions to be made to each individual user.

[0563] Step 2:

[0564] The user takes a photo of the inside of the refrigerator and uploads it to the application. The device receives this image data and analyzes it using image analysis technology (for example, TensorFlow or OpenCV). As a result of the analysis, the recognized food data is compiled into a list and sent from the device to the server. The server records the food information in the refrigerator based on this list.

[0565] Step 3:

[0566] The user inputs their mood for the day or what they want to eat by voice or text. For example, they can input "I want to eat Japanese food today." The device converts the voice data into text using the Google Cloud Speech-to-Text API, and analyzes this text data using natural language processing technology (such as BERT). The analysis results are sent to the server as data containing the user's input intent.

[0567] Step 4:

[0568] The server uses a generative AI model to generate optimal menus and recipes based on information about ingredients in the refrigerator and information input by the user. As a specific example, if the user requests "Japanese food," the server will suggest a menu such as "chicken teriyaki, spinach ohitashi, carrot and onion miso soup" using the "chicken, spinach, carrot and onion" in the refrigerator. The generated menu and recipe information is sent from the server to the device.

[0569] Step 5:

[0570] The device displays the received menu and recipes to the user. The user checks the displayed suggestions and, if there are any missing ingredients, selects the "Automatically purchase missing ingredients" option. Upon selection, the device sends a list of missing ingredients to the server.

[0571] Step 6:

[0572] The server connects to the online purchasing system and processes the necessary ingredients. For example, if there is a shortage of mirin in the refrigerator, the server uses the online supermarket's API to purchase mirin. After the purchase process is complete, the order details and delivery information are generated.

[0573] Step 7:

[0574] The server sends the order details and delivery information to the device, which then notifies the user. For example, a push notification saying "Mirin will be delivered tomorrow" can be sent to the user's smartphone, allowing them to confirm the necessary information.

[0575] (Application example 1)

[0576] 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."

[0577] Conventional refrigerator ingredient management and menu suggestion systems require users to record and manage ingredient information themselves, which is time-consuming, and the suggested menus do not always match the user's mood or preferences. Furthermore, if necessary ingredients are in short supply, users must manually purchase them, which is time-consuming and labor-intensive. Furthermore, they lack the functionality to notify users of delivery status in real time.

[0578] 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.

[0579] In this invention, the server includes means for acquiring information about ingredients in the refrigerator stored in a storage device through image analysis, means for analyzing natural language input from the user to interpret the user's mood and food preferences for that day, means for generating optimal menus and recipes based on the information about ingredients in the refrigerator and the user's input, means for displaying the generated menus and recipes on a user terminal, means for connecting with an online purchasing system to purchase ingredients that are missing, means for connecting the generated menus and recipes with a food sales system or a distribution system using natural language processing, and means for notifying the user of delivery status in real time. This reduces the burden on the user and makes it possible to automate the entire process from ingredient management to menu suggestions, purchasing procedures for missing ingredients, and notification of delivery status.

[0580] A "storage device" is an electronic device for storing data or information.

[0581] "Image analysis" is a technology that analyzes image data to recognize and distinguish the type and shape of an object.

[0582] "Natural language input" refers to a method of inputting information using a language that the user uses on a daily basis.

[0583] A "menu" refers to the combination of dishes for a particular meal.

[0584] A "recipe" is information that describes the steps and ingredients for making a particular dish.

[0585] An "online purchasing system" is a system for ordering and purchasing products via the Internet.

[0586] "Food sales system" refers to a system for selling ingredients and food.

[0587] A "distribution system" refers to the processes and mechanisms for delivering products and services from producers to consumers.

[0588] "Natural language processing" is a technology that uses computers to understand, analyze, and generate human language.

[0589] "Delivery status notification" is a function that notifies users of the progress and status of the delivery process of ordered items.

[0590] A "user terminal" is an electronic device that allows a user to access an application or system.

[0591] "Real-time notification" is a function that instantly notifies users of events or changes that are occurring.

[0592] This invention is a system that proposes optimal menus and recipes based on the user's mood and preferences based on information about ingredients in the refrigerator, and also allows the user to purchase ingredients that are in short supply online. The main processing flow of this system is shown below.

[0593] System Configuration

[0594] This system consists of a user, a server, and a terminal. The terminal can be a smartphone, smart glasses, a head-mounted display, or a robot. The server is located in a cloud environment and includes a database, an image analysis engine, a natural language processing engine, and a module for linking with the online purchasing system.

[0595] Examples of the invention

[0596] Program generation and processing overview

[0597] 1. Obtaining ingredient information:

[0598] Users take a photo of the inside of the refrigerator using the camera on their smartphone and upload it to the device. This photo is sent to the server, which uses an image analysis engine (e.g., OpenCV or TensorFlow) to recognize the ingredients in the refrigerator and store them as a list in a database.

[0599] 2. Parsing user input:

[0600] Users input their mood or what they want to eat that day into the device by voice or text. In the case of voice input, the device converts the input into text using speech recognition technology (e.g., Google Speech-to-Text API). The converted text is sent to the server and analyzed by a natural language processing engine (e.g., NLTK or SpaCy).

[0601] 3. Menu and recipe suggestions:

[0602] The server generates optimal menus and recipes based on information about ingredients in the refrigerator and user input. A generative AI model is used for generation. The generated menu and recipe information is sent to the device and displayed to the user.

[0603] 4. Automatically purchase ingredients when they are in short supply:

[0604] The user checks the proposed menu and, if any ingredients are missing, selects the "Buy automatically" option on the device. The server connects to the online purchasing system and automatically orders the necessary ingredients. Payment is made using an API (e.g., Stripe or PayPal).

[0605] 5. Delivery Status Notification:

[0606] The server monitors order details and delivery information in real time and notifies the terminal of the delivery status using services such as Firebase Cloud Messaging.

[0607] Specific examples

[0608] The user voice-inputs "I want to eat Italian today" into the device. By taking a photo of the contents of the refrigerator and uploading it to the app, "tomatoes, basil, and mozzarella cheese" are recognized. Based on this information, the server suggests "Caprese salad" and, if any necessary ingredients are missing, automatically completes the online purchase process. Delivery status is notified to the user's device in real time.

[0609] Prompt Sentence Examples

[0610] When a user types "I want to eat Italian food today," the prompt text is:

[0611] I want to eat Italian food today

[0612] In this way, the system of the present invention can perform intelligent ingredient management and menu suggestions, thereby increasing user convenience.

[0613] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0614] Step 1:

[0615] A user takes a photo of the inside of the refrigerator using a device and uploads it to the application. The input is the captured image, and the output is the image data sent to the server.

[0616] Step 2:

[0617] The server analyzes the received image data using an image analysis engine (e.g., OpenCV or TensorFlow). By analyzing the image, it creates a list of ingredients in the refrigerator and stores it in a database. The input is the image data, and the output is the analyzed list of ingredients in the refrigerator.

[0618] Step 3:

[0619] The user inputs their mood for the day and what they want to eat into the device by voice or text. The input is voice or text data. When voice input is used, the device converts the voice into text using voice recognition technology (for example, Google Speech-to-Text API). The output is text data.

[0620] Step 4:

[0621] The device sends the generated text data to the server, which then uses a natural language processing engine (e.g., NLTK or SpaCy) to analyze the user's preferences. The input is the text data, and the output is the analyzed preference information.

[0622] Step 5:

[0623] The server uses a generative AI model to generate optimal menus and recipes based on the information about ingredients in the refrigerator and the user's preferences. The input is the information about ingredients and preferences, and the output is the generated menu and recipes.

[0624] Step 6:

[0625] The server sends the generated menu and recipe information to the terminal, which displays it to the user. The input is the menu and recipe information, and the output is the display to the user.

[0626] Step 7:

[0627] The user reviews the proposed menu and, if there are any missing ingredients, selects the "Buy automatically" option on the device. The input is the user's selection, and the output is a list of missing ingredients.

[0628] Step 8:

[0629] The server connects to an online purchasing system (e.g., Amazon or Rakuten) based on the list of ingredients that are in short supply and orders the necessary ingredients. The input is the list of ingredients that are in short supply, and the output is a notification that the ordering process has been completed.

[0630] Step 9:

[0631] The server monitors order details and delivery information in real time and notifies the terminal of the delivery status. The input is order and delivery information, and the output is a notification to the user.

[0632] Specific examples

[0633] The user voice-records "I want to eat Italian food today" into the device and uploads a photo of what's in the refrigerator. The server analyzes the image and recognizes "tomatoes, basil, and mozzarella cheese," and based on this, suggests "Caprese salad." The user selects the option to automatically purchase ingredients that are running low (e.g., olive oil), and the server links with the online purchasing system to place the order. The user is notified of delivery status in real time.

[0634] 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.

[0635] Overall system configuration

[0636] This invention is a system that proposes optimal menus and recipes based on information about ingredients in the refrigerator, taking into consideration the user's mood, what they want to eat, and even their emotions, and also allows them to purchase ingredients they are running low on online.The system is mainly composed of a user, a server, a terminal, and an emotion engine.

[0637] Overall processing of the program

[0638] Registration and Initial Setup

[0639] 1. The user downloads the application and enters their name, address, email address, favorite ingredients, and allergy information when they first launch it.

[0640] 2. The device sends this information to the server, which creates a user profile.

[0641] Photography and food recognition

[0642] 3. The user takes a photo of the refrigerator and uploads it to the app. For example, the user opens the refrigerator door and takes a photo of the inside.

[0643] 4. The device uses image analysis technology to analyze the uploaded photos and generate a list of ingredients in the refrigerator.

[0644] Input of user's mood, desires, and emotions

[0645] 5. The user inputs their mood and food cravings by voice or text. For example, they might say, "I want something light today."

[0646] 6. The device converts the voice input into text (in the case of voice input) and analyzes it using natural language processing technology. The analyzed results are sent to the server.

[0647] 7. The emotion engine analyzes the user's input and obtains emotion information. For example, if the user is using a facial recognition camera, it will determine the emotion from their facial expressions.

[0648] 8. The device also sends the analyzed emotional information to the server.

[0649] Menu and recipe generation

[0650] 9. The server generates the optimal menu and recipe based on the information about ingredients in the refrigerator, the user's input, and emotional information. For example, it decides on a menu of "tomato and cucumber salad, lightly grilled chicken, and egg soup."

[0651] 10. The server sends the generated menu and recipe information to the terminal, which displays it to the user.

[0652] Complementing missing ingredients

[0653] 11. The user reviews the suggested menu and recipes and, if there are any missing ingredients, selects the "Automatically purchase missing ingredients" option.

[0654] 12. The device sends a list of ingredients that are missing to the server.

[0655] 13. The server connects to the distribution system and processes the necessary ingredients for purchase online. For example, the customer orders the necessary mirin from an online store.

[0656] Notification and Delivery

[0657] 14. The server returns the order details and delivery information to the terminal, and the terminal notifies the user of the order completion and delivery information. For example, it notifies the user that "Mirin will be delivered tomorrow."

[0658] Specific examples

[0659] 1. The user downloads the app and enters the required initial information.

[0660] 2. The user uploads a photo of the inside of their refrigerator to the app, and image analysis technology is used to create a list of "chicken, spinach, carrots, and onions."

[0661] 3. The user inputs "I want to eat Japanese food today" through voice input, which is then analyzed using natural language processing technology.

[0662] 4. The emotion engine obtains emotional information from the user's input and facial expressions and determines whether the user is in a "relaxed mood."

[0663] 5. The device sends this information to the server, which then generates a recipe for "Teriyaki chicken, boiled spinach, and miso soup with carrots and onions," which the device then displays.

[0664] 6. The user checks the proposed menu and, if there are any missing condiments (e.g., mirin), selects "Buy automatically."

[0665] 7. The server connects to the online purchasing system to order mirin and process the delivery.

[0666] 8. The terminal notifies the user that the mirin order has been completed and the estimated delivery date.

[0667] In this way, the system can effectively utilize the ingredients in the user's refrigerator, easily provide a menu that matches the user's mood or emotions that day, and enable the user to purchase the necessary ingredients online.

[0668] The processing flow will be explained below.

[0669] Step 1:

[0670] The user downloads the application and enters their name, address, email address, favorite ingredients, and allergy information when they first launch it.

[0671] Step 2:

[0672] The terminal transmits this user information to the server.

[0673] Step 3:

[0674] The server creates a user profile based on the received user information and stores it in a database.

[0675] Step 4:

[0676] The user takes a photo of the inside of the refrigerator and uploads it to the app. For example, the user opens the refrigerator door and takes a photo of the inside.

[0677] Step 5:

[0678] The device receives the uploaded photo, analyzes it using image analysis technology (such as OCR), and identifies the ingredients in the refrigerator to generate a list. For example, it recognizes "tomatoes, cucumbers, eggs, and chicken" from the photo.

[0679] Step 6:

[0680] The terminal transmits the analyzed ingredient list to the server.

[0681] Step 7:

[0682] The server updates the user profile based on the received ingredient list.

[0683] Step 8:

[0684] The user inputs their mood and what they want to eat that day by voice or text. For example, they might say, "I want to eat something light today."

[0685] Step 9:

[0686] The device converts the voice input into text (if voice input is used) and analyzes it using natural language processing technology. The analyzed information is then sent to the server.

[0687] Step 10:

[0688] The emotion engine infers the user's emotions based on the user's input, for example, by using a facial recognition camera to determine if the user wants to relax.

[0689] Step 11:

[0690] The terminal also transmits emotion information acquired by the emotion engine to the server.

[0691] Step 12:

[0692] The server generates optimal menus and recipes based on information about ingredients in the refrigerator, user input, and emotional information. For example, it suggests a menu of "tomato and cucumber salad, lightly grilled chicken, and egg soup."

[0693] Step 13:

[0694] The server sends the generated menu and recipe information to the terminal, which then displays it to the user. For example, the menu and recipes for each dish are displayed.

[0695] Step 14:

[0696] The user checks the suggested menu and recipes, and if there are any missing ingredients, selects the "Automatically purchase missing ingredients" option.

[0697] Step 15:

[0698] The terminal sends a list of ingredients that are missing to the server.

[0699] Step 16:

[0700] The server connects to the online purchasing system and processes the necessary ingredients. For example, the server orders the necessary mirin from the online store.

[0701] Step 17:

[0702] The server returns the order details and delivery information to the terminal. The terminal notifies the user of the order completion and delivery information. For example, it notifies the user that "Mirin will be delivered tomorrow."

[0703] This series of processes allows users to efficiently use the ingredients in their refrigerator, easily plan a menu that matches their mood or emotions for the day, and purchase the necessary ingredients online.

[0704] Example 2

[0705] 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."

[0706] In modern society, busy lives mean limited time for planning menus, making it difficult to propose meals that take into account the user's mood, health status, individual preferences, and allergies. Another problem is the lack of systems that can effectively utilize ingredients in the refrigerator while automatically purchasing ingredients that are in short supply. To solve this situation, a system is needed that proposes optimal menus based on the ingredients in the user's refrigerator and their mood and emotions for the day, and automatically purchases the necessary ingredients online.

[0707] 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.

[0708] In this invention, the server includes means for acquiring information about ingredients in the refrigerator stored in a storage device through image analysis, means for analyzing natural language input from the user to interpret the user's mood and food preferences for that day, means for generating optimal menus and recipes based on the information about ingredients in the refrigerator and the user's input and emotional information, means for displaying the generated menus and recipes on a user terminal, means for purchasing ingredients that are in short supply by connecting to an online purchasing system, and means for acquiring emotional information by recognizing the user's facial expression. This makes the user's life more comfortable and convenient, allowing them to easily decide on daily menus and automatically replenish necessary ingredients.

[0709] "Storage device" refers to hardware or software for storing data, and specifically includes hard disk drives and solid-state drives.

[0710] "Image analysis" is a technology that uses photographs or image data to recognize specific objects or extract specific information.

[0711] "Natural language input" is a method for allowing a system to understand and analyze the content of input in the language (voice or text) that a user uses on a daily basis.

[0712] "Menu" refers to the combination and order of dishes served at a meal.

[0713] A "recipe" is a set of instructions that outlines the steps and ingredient combinations required to prepare a particular dish.

[0714] A "user terminal" is a device that is directly operated by a user, and specifically refers to electronic devices such as smartphones and tablets.

[0715] An "online purchasing system" is a system for purchasing products via the Internet, and specifically includes online stores and e-commerce platforms.

[0716] "User profile information" is a collection of individual pieces of information related to a user, including name, address, email address, favorite ingredients, allergy information, and the like.

[0717] "Speech recognition technology" is a technology that analyzes input voice data and converts it into text data.

[0718] "Natural language processing technology" is a technology that allows computers to understand and analyze natural human language, and specifically includes semantic analysis and context understanding.

[0719] "Emotion information" is information relating to the user's mood or emotional state, and specifically is data indicating an emotional state such as "I want to relax" or "I feel stressed."

[0720] This invention is a system that proposes optimal menus and recipes based on information about ingredients in the refrigerator, taking into consideration the user's mood, what they want to eat, and even their emotions, and also allows them to purchase ingredients they are running low on online.The system is mainly composed of a server, terminals, users, and an emotion engine.

[0721] Registration and Initial Setup

[0722] A user downloads and installs the application onto a device such as a smartphone or tablet. When the application is launched for the first time, the user enters their name, address, email address, favorite foods, allergy information, etc. The device sends this information to the server, which then creates a user profile based on the received information and stores it in a database.

[0723] Photography and food recognition

[0724] The user takes a photo of the inside of the refrigerator and uploads it to the application. The device analyzes the photo using image analysis technology (e.g., Google Cloud Vision API), creates a list of the ingredients in the refrigerator, and sends it to the server.

[0725] Input of user's mood, desires, and emotions

[0726] The user tells the app how they feel that day and what they want to eat by voice or text input. For example, they might say, "I want something light today." The device uses voice recognition technology (e.g., Google Cloud Speech-to-Text) to convert the speech into text, which is then analyzed using natural language processing technology (e.g., OpenAI GPT-4). The analysis results are sent to the server.

[0727] The emotion engine analyzes emotional information based on the user's input data and data acquired from the facial recognition camera. For example, it analyzes emotional information such as "I feel like relaxing." The device then sends this emotional information to the server.

[0728] Menu and recipe generation

[0729] The server generates the optimal menu and recipes based on information about ingredients in the refrigerator, the user's mood, preferences, and emotional information. For example, it generates a menu such as "tomato and cucumber salad, lightly grilled chicken, and egg soup." The server sends the generated menu and recipe information to the terminal, which then displays the information to the user.

[0730] Complementing missing ingredients

[0731] The user checks the provided menu and recipe, and if there are any missing ingredients, selects the "Automatically purchase missing ingredients" option. The device sends a list of the missing ingredients to the server. The server then connects to the online purchasing system and carries out the procedure to purchase the missing ingredients online. For example, the user orders the required "mirin" from an online store.

[0732] Notification and Delivery

[0733] The server returns the order details and delivery information to the terminal, and the terminal notifies the user of the order completion and delivery information. For example, it notifies the user that "Mirin will be delivered the next day."

[0734] Examples of specific examples and prompts

[0735] Specific examples

[0736] The user downloads the app and enters initial information such as name, address, email address, favorite ingredients, and allergy information.

[0737] Users take a photo of the inside of their refrigerator and upload it to the app, which uses image analysis technology to analyze the photo and produce a list of "chicken, spinach, carrots, and onions."

[0738] The user inputs "I want to eat Japanese food today" by voice, and the device converts this into text using voice recognition technology, which is then analyzed using natural language processing technology.

[0739] The emotion engine determines whether you feel like relaxing based on input and facial expression data.

[0740] The device sends information about ingredients, mood, and emotions to the server, and the server generates a recipe for "teriyaki chicken, boiled spinach, and miso soup with carrots and onions," which is then displayed on the device.

[0741] The user reviews the suggested menu and selects the option to automatically purchase any mirin they are running low on.

[0742] The server connects to the online purchasing system to order mirin and process the delivery.

[0743] The terminal notifies the user of the order completion and estimated delivery date.

[0744] Prompt Sentence Examples

[0745] "Please upload a photo of the inside of your refrigerator and list the ingredients in it."

[0746] "I want to eat Japanese food today. I'd like you to suggest a relaxing menu."

[0747] "Show me recipes for chicken teriyaki, spinach ohitashi, and carrot and onion miso soup."

[0748] "Automatically purchase mirin online if I'm running low on it."

[0749] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0750] System processing steps

[0751] Step 1: Enter your user information and create your profile

[0752] The user downloads and installs the application.

[0753] Input: Name, address, email address, favorite ingredients, allergy information.

[0754] Specific operations: Launch the app and enter the required information on the initial setup screen.

[0755] The terminal transmits the input information to the server.

[0756] Output: The user profile is saved on the server.

[0757] Step 2: Take a photo of the inside of your refrigerator and upload it

[0758] A user opens the refrigerator door and takes a photo of the inside.

[0759] Input: A photo of the inside of a refrigerator.

[0760] Specific operation: Take a photo of the inside of the refrigerator using your smartphone camera and upload it to the app.

[0761] The device analyzes the uploaded photos using image analysis technology (e.g., Google Cloud Vision API).

[0762] Output: A list of ingredients in the refrigerator is generated.

[0763] Step 3: Submit and save ingredient information

[0764] The terminal transmits the generated ingredient list to the server.

[0765] Input: Ingredient list.

[0766] Specific operation: The ingredient list obtained through image analysis technology is sent to the server.

[0767] The server stores the ingredient information in a database.

[0768] Output: Ingredient information is saved in the database.

[0769] Step 4: Enter the user's mood and wishes

[0770] The user inputs their mood and what they want to eat that day using voice or text.

[0771] Input: Voice or text to indicate your mood or what you want to eat that day.

[0772] What it does: Enter your desired eating style using the app's input field or voice input function.

[0773] The device uses voice recognition technology (e.g., Google Cloud Speech-to-Text) to convert the speech into text, which is then analyzed using natural language processing technology (e.g., OpenAI GPT-4).

[0774] Output: The analyzed mood and preference information is available in text format.

[0775] Step 5: Send and save your mood and preferences

[0776] The device sends the analyzed mood and preference information to the server.

[0777] Input: Mood and preference information in text format.

[0778] Specific operation: Executes a process to send the analysis results to the server.

[0779] The server stores mood and preference information in a database.

[0780] Output: Mood and preference information is saved in a database.

[0781] Step 6: Acquiring and transmitting emotional information

[0782] The emotion engine recognizes the user's facial expressions and analyzes emotional information.

[0783] Input: User's facial expression data.

[0784] Specific operation: Using a facial expression recognition camera, the user's facial expressions are captured and their emotions are analyzed.

[0785] The emotion engine sends the emotion information to the server.

[0786] Output: Emotion information is saved in text format on the server.

[0787] Step 7: Create menus and recipes

[0788] The server combines information about ingredients in the refrigerator with the user's mood, preferences, and emotional information to generate optimal menus and recipes.

[0789] Input: Ingredient information, mood and preference information, emotional information.

[0790] Specific operation: Extracts information on ingredients, mood, preferences, and emotions from the database, and analyzes them comprehensively to generate appropriate menus and recipes.

[0791] Output: Generated menu and recipe information.

[0792] Step 8: Send and display menu and recipe information

[0793] The server sends the generated menu and recipe information to the terminal.

[0794] Input: Menu and recipe information.

[0795] Specific operation: The generated menu and recipes are sent to the user's terminal and presented to the user through the display function.

[0796] Output: Menu and recipes displayed on the terminal.

[0797] Step 9: Select the option to automatically purchase ingredients when they run out

[0798] The user checks the menu and, if there are any missing ingredients, selects the "Automatically purchase missing ingredients" option.

[0799] Input: User's choice.

[0800] What it does: Check menus and recipes, then tap to select the option you want.

[0801] The terminal sends a list of ingredients that are missing to the server.

[0802] Output: List of missing ingredients.

[0803] Step 10: Purchase missing ingredients online

[0804] The server connects to the online purchasing system and carries out the procedure to purchase the necessary ingredients.

[0805] Input: List of missing ingredients.

[0806] Specific operation: Use the API of the online purchasing system to automatically proceed with the purchase procedure and start the process to order the specified ingredients.

[0807] Output: Order completed notification.

[0808] Step 11: Order and Shipping Information Notification

[0809] The server returns the order details and delivery information to the terminal.

[0810] Input: Order details and shipping information.

[0811] Specific operation: After the order is completed, the contents and estimated delivery date are notified to the user's terminal.

[0812] The terminal notifies the user of the order completion and delivery information.

[0813] Output: The user receives a notification of order completion and shipping information.

[0814] The above are the specific processing steps of this system.

[0815] (Application example 2)

[0816] 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."

[0817] While conventional food management systems have the ability to suggest recipes to users based on information about ingredients in the refrigerator, many lack the ability to link with user emotions and store inventory information, making it impossible to suggest recipes that best suit the user's current mood or emotions. Furthermore, they lack the ability to identify the location of ingredients that are missing in the store, which can make the user's shopping experience more complicated.

[0818] The identification process by the identification 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 means for acquiring ingredient information stored in a storage device through image analysis, means for analyzing natural language input from the user to interpret the user's mood and food preferences for that day, means for analyzing the user's emotional information and optimizing menus and recipes based on the analysis, means for linking with in-store inventory information to provide information on the location of missing ingredients, means for displaying the generated menus and recipes on the user's terminal, and means for linking with an online purchasing system to purchase missing ingredients. This enables the server to suggest optimal recipes tailored to the user's mood and emotions and provide a smooth shopping experience in the store.

[0819] A "storage device" is hardware or software for storing data and information.

[0820] "Food information" refers to data such as the type, quantity, and freshness of food and ingredients in the refrigerator or store.

[0821] "Image analysis" is a technique for analyzing image data and extracting useful information from it.

[0822] "Natural language input" refers to the input of instructions or questions by a user using everyday language, either by voice or text.

[0823] "User input" means data provided to the system by a user through natural language input or other means.

[0824] "Menu" refers to the entire meal menu for that day.

[0825] A "recipe" is a detailed set of instructions and ingredients for making a particular dish.

[0826] "Emotional information" is data that indicates the user's emotional state and is obtained from facial expression analysis, voice tone, etc.

[0827] A "user terminal" is a device that is directly operated by a user, such as a smartphone, smart glasses, or a head-mounted display.

[0828] An "online purchasing system" is a platform for purchasing products via the Internet.

[0829] "Store inventory information" is data on products and ingredients currently available in the store.

[0830] "Guidance means" refers to a method or system that shows the user the location of a destination or product.

[0831] This invention is an in-store ingredient management and recommendation system that proposes optimal menus and recipes based on the user's mood and emotions, and also supports the purchase of ingredients that are in short supply. The system is mainly composed of a storage device, a user terminal, a server, an emotion engine, and an in-store inventory information system.

[0832] Hardware and software used

[0833] Hardware:

[0834] Smartphones, smart glasses, head-mounted displays (user devices)

[0835] Camera (for facial expression analysis)

[0836] software:

[0837] Image analysis libraries (e.g., Firebase ML Kit)

[0838] Natural language processing engine (e.g. Google Cloud Natural Language API)

[0839] Sentiment analysis engine (e.g. Affectiva SDK)

[0840] Cloud databases (e.g. Firebase Firestore)

[0841] Online purchasing system API (e.g. Amazon API)

[0842] Processing flow

[0843] 1. User Profile Management:

[0844] A user installs the app and enters initial information such as their name, address, email address, favorite ingredients, allergy information, etc. This user information is stored in a cloud database (Firebase Firestore).

[0845] 2. Food recognition:

[0846] The user takes a photo of the food shelves in the store using smart glasses or a smartphone. The image data is analyzed using an image analysis library (Firebase ML Kit) to obtain food information.

[0847] 3. Parsing user-entered information:

[0848] The user inputs something like "I want to eat something light today" via voice or text. This input is converted into text using speech recognition technology, and then analyzed by a natural language processing engine (Google Cloud Natural Language API).

[0849] 4. Acquiring emotional information:

[0850] The user's facial expressions are captured by the camera in the smart glasses or head-mounted display and analyzed by the emotion analysis engine (Affectiva SDK), thereby obtaining the user's emotional information.

[0851] 5. Menu and recipe generation:

[0852] The server generates optimal menus and recipes based on the ingredient information stored in the cloud database, the user's input information, and emotional information. This information is displayed on the user's device.

[0853] 6. Purchase and guidance of missing ingredients:

[0854] A list of missing ingredients is generated based on the recipe. This is checked against the store's inventory information, and if any are missing, they are ordered and purchased through the online purchasing system (Amazon API). If the missing ingredients are available in the store, the user is informed of their location.

[0855] Specific examples

[0856] 1. While walking around the store, the user scans the shelves with smart glasses and then voice-inputs, "I'd like to eat a light Japanese meal today."

[0857] 2. The system recognizes "salmon, tofu, and green onions" from an image of a product shelf, and, combined with the user information, suggests "grilled salmon with salt and tofu miso soup."

[0858] 3. The app will notify you when you are low on soy sauce and use your location information in the store to guide you to the soy sauce section.

[0859] Prompt Sentence Examples

[0860] You're using an "intelligent shopping assistant" app. It analyzes photos of shelves you take in a store and suggests recipes based on ingredients that fit your current mood. You enter, "I want something light today."

[0861] This will enable the system to suggest optimal recipes that match the user's mood and emotions, as well as provide a smooth shopping experience in stores.

[0862] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0863] Step 1:

[0864] Users install the application and enter their name, address, email address, favorite foods, and allergy information. This initial information is stored in a cloud database. The user's personal information and preferences are used as input data to create a user profile. The server uses this data to build an individual user profile.

[0865] Step 2:

[0866] The user takes a photo of the food shelves in the store using smart glasses or a smartphone and uploads it to the app. The image data is analyzed using an image analysis library (e.g., Firebase ML Kit) to extract information about the ingredients. The input is the captured image data, and the output is a list of analyzed ingredients. The device displays this list and also sends it to the server.

[0867] Step 3:

[0868] The user inputs a voice or text message such as "I want something light today." In the case of voice input, the message is converted into text using speech recognition technology and analyzed by a natural language processing engine (e.g., Google Cloud Natural Language API). The input data is voice or text, and the output is text data that reflects the analyzed user's mood and preferences. The server uses the results of this analysis to generate menu suggestions.

[0869] Step 4:

[0870] The user's facial expressions are captured by a camera in smart glasses or a head-mounted display and analyzed by an emotion analysis engine (e.g., Affectiva SDK). The captured image or video data is used as input data, and the output is the user's emotional information. This emotional information is sent to a server and analyzed together with the user's mood and wishes.

[0871] Step 5:

[0872] The server generates optimal menus and recipes based on ingredient information stored in the cloud database, user input information, and emotional information. The input data is the ingredient list, the user's preferences and mood information, and emotional information, and the output is optimized menus and recipes. The generated information is sent to the user's device and displayed to the user.

[0873] Step 6:

[0874] The terminal displays the generated menu and recipes to the user, and also presents a list of missing ingredients. The input data are the generated menu and recipes, and the output is the list of missing ingredients.

[0875] Step 7:

[0876] The server checks the store's inventory information and, if there are shortages, orders and purchases ingredients through an online purchasing system (e.g., Amazon API). If the missing ingredients are available in the store, the server guides the user to their location. The input data is inventory information and a list of ingredients that are in short supply, and the output is purchase completion information and in-store guidance information. The terminal notifies the user of this.

[0877] In this way, a system is formed that inputs, analyzes, and outputs data at each step to provide the user with the optimal experience.

[0878] 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.

[0879] 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.

[0880] 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.

[0881] [Third embodiment]

[0882] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0883] 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.

[0884] 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).

[0885] 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.

[0886] 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.

[0887] 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).

[0888] 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.

[0889] 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.

[0890] 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.

[0891] 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.

[0892] 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.

[0893] 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."

[0894] Overall system configuration

[0895] This invention is a system that proposes optimal menus and recipes based on information about ingredients in the refrigerator, taking into account the user's mood and food preferences, and also allows the user to purchase ingredients online if they are running low. This system is mainly composed of users, a server, and terminals.

[0896] Overall processing of the program

[0897] Registration and Initial Setup

[0898] 1. The user downloads the application and enters their name, address, email address, favorite ingredients, and allergy information when they first launch it.

[0899] 2. The device sends this information to the server, which creates a user profile.

[0900] Photography and food recognition

[0901] 3. The user takes a photo of the refrigerator and uploads it to the app. For example, the user opens the refrigerator door and takes a photo of the inside.

[0902] 4. The device uses image analysis technology to analyze the uploaded photo and create a list of the ingredients in the refrigerator.

[0903] Input of user's mood and desires

[0904] 5. The user inputs their mood and food cravings by voice or text. For example, they might say, "I want something light today."

[0905] 6. The device converts the voice input into text and analyzes it using natural language processing technology.

[0906] 7. The device sends the analysis results to the server, which then generates the optimal menu and recipe based on the information about ingredients in the refrigerator and the information entered by the user.

[0907] Providing menus and recipes

[0908] 8. The server sends the generated menu and recipe information to the device, which displays it to the user. For example, the device suggests a menu of "tomato and cucumber salad, lightly grilled chicken, and egg soup."

[0909] Complementing missing ingredients

[0910] 9. The user checks the proposed menu and, if there are any missing ingredients, selects the "Automatically purchase missing ingredients" option.

[0911] 10. The device sends the list of ingredients needed to the server, and the server connects to the online purchasing system to purchase the ingredients. For example, if mirin is in short supply, the server will purchase it from an online supermarket.

[0912] Notification and Delivery

[0913] 11. The server returns the order details and delivery information to the terminal, and the terminal notifies the user of the order completion and delivery information. For example, it notifies the user that "Mirin will be delivered tomorrow."

[0914] Specific examples

[0915] 1. The user downloads the app and enters the required initial information.

[0916] 2. The user uploads a photo of the inside of their refrigerator to the app, and image analysis technology is used to create a list of "chicken, spinach, carrots, and onions."

[0917] 3. The user inputs "I want to eat Japanese food today" through voice input, which is then analyzed using natural language processing technology.

[0918] 4. The device sends the analysis results to the server, which generates a recipe for "chicken teriyaki, spinach ohitashi, and carrot and onion miso soup," which is then displayed on the device.

[0919] 5. The user checks the proposed menu and, if there are any missing condiments (e.g., mirin), selects "Buy automatically."

[0920] 6. The server connects to the online purchasing system to order mirin and process the delivery.

[0921] 7. The terminal notifies the user that the mirin order has been completed and the estimated delivery date.

[0922] In this way, the system can effectively utilize the ingredients in the user's refrigerator, easily provide a menu that suits the mood of the day, and realize a series of steps to purchase the necessary ingredients online.

[0923] The processing flow will be explained below.

[0924] Step 1:

[0925] The user downloads the application and enters their name, address, email address, favorite ingredients, and allergy information when they first launch it.

[0926] Step 2:

[0927] The terminal transmits the user's input information to the server.

[0928] Step 3:

[0929] The server creates a user profile based on the received user information and stores it in a database.

[0930] Step 4:

[0931] The user takes a photo of the inside of the refrigerator and uploads it to the app. For example, the user opens the refrigerator door, takes a photo of the inside, and presses the "upload" button.

[0932] Step 5:

[0933] The device receives the uploaded photo, analyzes it using OCR and image analysis technology, and generates a list of the ingredients in the refrigerator. For example, it recognizes "tomatoes, cucumbers, eggs, and chicken" from the photo.

[0934] Step 6:

[0935] The terminal transmits the ingredient list generated as a result of the analysis to the server.

[0936] Step 7:

[0937] The server updates the ingredient information in the user profile based on the received ingredient list.

[0938] Step 8:

[0939] The user inputs their mood and what they want to eat that day by voice or text. For example, they can input "I want to eat something light today."

[0940] Step 9:

[0941] The device converts the voice input into text (in the case of voice input) and analyzes it using natural language processing technology. The analyzed results are then sent to the server.

[0942] Step 10:

[0943] The server generates the optimal menu and recipe based on the information about ingredients in the refrigerator and the information entered by the user. For example, it decides on a menu of "tomato and cucumber salad, lightly grilled chicken, and egg soup."

[0944] Step 11:

[0945] The server returns the generated menu and recipe information to the terminal, and the terminal displays the menu and recipe to the user.

[0946] Step 12:

[0947] The user checks the suggested menu and recipes, and if there are any missing ingredients, selects the "Automatically purchase missing ingredients" option.

[0948] Step 13:

[0949] The terminal sends a list of ingredients that are missing to the server.

[0950] Step 14:

[0951] The server connects to the distribution system and processes the necessary ingredients for purchase online. For example, you can order the necessary mirin from an online store.

[0952] Step 15:

[0953] The server returns the order details and delivery information to the terminal, and the terminal notifies the user of the order completion and delivery information. For example, it notifies the user that "Mirin will be delivered tomorrow."

[0954] This allows users to make effective use of ingredients in their refrigerators and enjoy a menu that suits their mood that day, easily and without hassle.

[0955] Example 1

[0956] 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."

[0957] In recent years, there has been a growing need for systems that efficiently utilize ingredients in the refrigerator and propose menus that match the user's mood and preferences. However, current systems have difficulty consistently obtaining information about ingredients in the refrigerator, making proposals based on the user's mood, and even purchasing ingredients online if they are in short supply. There is also a lack of systems that can respond individually to users' preferences and allergies. There is a need for a system that solves these problems.

[0958] 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.

[0959] In this invention, the server includes means for acquiring information about ingredients in the refrigerator stored in a storage device through image analysis, means for analyzing natural language input from the user to interpret the user's mood and food preferences for that day, means for generating optimal menus and recipes based on the information about ingredients in the refrigerator and the user's input information, means for displaying the generated menus and recipes on a user terminal, means for purchasing missing ingredients by linking with an online purchasing system, means for creating a user profile based on initial information input by the user and saving it in a storage device, means for the user to take a photo of the refrigerator and upload it to an application, and means for converting voice input into text and transmitting the analysis results to the server. This makes it possible to consistently suggest optimal menus based on the ingredients in the refrigerator and automatically purchase missing ingredients.

[0960] "Storage device" refers to hardware or software for storing and retrieving data.

[0961] "Information about ingredients in the refrigerator" refers to detailed data such as the type, quantity, and shelf life of various ingredients stored in the refrigerator.

[0962] "Image analysis" refers to the technology of analyzing image data to extract specific information or features.

[0963] "Natural language input" refers to inputting human language such as voice or text.

[0964] "User profile" refers to data that includes personal information such as a user's name, address, email address, favorite foods, and allergy information.

[0965] A "menu" refers to a combination of menus and recipes for a particular meal.

[0966] A "recipe" is a detailed set of instructions on how to prepare a particular dish.

[0967] "User terminal" refers to an electronic device operated by a user, such as a smartphone or tablet.

[0968] An "online purchasing system" refers to a system that allows you to purchase products via the Internet.

[0969] "Voice recognition technology" refers to technology that converts voice data into text.

[0970] "Natural language processing technology" refers to technology that understands and analyzes human language.

[0971] "Upload" refers to the act of sending data from a local device to a remote server or cloud.

[0972] This invention is a system that proposes optimal menus and recipes based on information about ingredients in the refrigerator, taking into account the user's mood and food preferences, and also allows the user to purchase ingredients online if they are running low. This system is mainly composed of users, a server, and terminals.

[0973] Registration and Initial Setup

[0974] When a user downloads a mobile application and launches it for the first time, they enter their name, address, email address, favorite foods, and allergy information. This information is sent from the device to a server, which then uses it to create a user profile in a database. The database is built using MySQL or MongoDB.

[0975] Photography and food recognition

[0976] The user takes a photo of the refrigerator and uploads it to the app. The device analyzes the uploaded photo using image analysis technology (e.g., TensorFlow or OpenCV) and creates a list of the ingredients in the refrigerator. For example, if the app recognizes that the refrigerator contains chicken, spinach, carrots, and onions, it generates a list of these ingredients.

[0977] Input of user's mood and desires

[0978] The user inputs their mood or what they want to eat that day through voice or text input. For example, they can input "I want to eat Japanese food today." The device converts the voice to text using the Google Cloud Speech-to-Text API and analyzes the text using natural language processing techniques such as BERT. The analysis results are sent from the device to the server.

[0979] Providing menus and recipes

[0980] The server uses a generative AI model to generate optimal menus and recipes based on information about ingredients in the refrigerator and user input. The generated menu and recipe information is sent from the server to the device, which displays it to the user. For example, it might suggest a menu such as "Teriyaki chicken, boiled spinach, and miso soup with carrots and onions."

[0981] Complementing missing ingredients

[0982] The user checks the proposed menu and, if there are any missing ingredients, selects the "Automatically purchase missing ingredients" option. The device sends a list of the required ingredients to the server, which then connects to the online purchasing system to purchase the necessary ingredients. For example, if mirin (sweet rice wine) is in short supply, the API of an online supermarket is used to purchase mirin.

[0983] Notification and Delivery

[0984] The server returns the order details and delivery information to the terminal, and the terminal notifies the user of the order completion and delivery information. For example, the terminal may notify the user that "Mirin will be delivered tomorrow."

[0985] Prompt Sentence Examples

[0986] "A user uploads a photo of the inside of their refrigerator, and chicken, spinach, and carrots are listed. If they enter 'I feel like eating Japanese food today,' please suggest the best menu and recipes for them."

[0987] In this way, the system can effectively utilize the ingredients in the user's refrigerator, easily provide a menu that suits the mood of the day, and realize a series of steps to purchase the necessary ingredients online.

[0988] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0989] Step 1:

[0990] A user downloads a mobile application and enters their name, address, email address, favorite ingredients, and allergy information when they first launch it. The entered data is sent from the device to a server. The server analyzes the received data and stores it as a user profile in a database such as MySQL or MongoDB. This profile allows personalized suggestions to be made to each individual user.

[0991] Step 2:

[0992] The user takes a photo of the inside of the refrigerator and uploads it to the application. The device receives this image data and analyzes it using image analysis technology (for example, TensorFlow or OpenCV). As a result of the analysis, the recognized food data is compiled into a list and sent from the device to the server. The server records the food information in the refrigerator based on this list.

[0993] Step 3:

[0994] The user inputs their mood for the day or what they want to eat by voice or text. For example, they can input "I want to eat Japanese food today." The device converts the voice data into text using the Google Cloud Speech-to-Text API, and analyzes this text data using natural language processing technology (such as BERT). The analysis results are sent to the server as data containing the user's input intent.

[0995] Step 4:

[0996] The server uses a generative AI model to generate optimal menus and recipes based on information about ingredients in the refrigerator and information input by the user. As a specific example, if the user requests "Japanese food," the server will suggest a menu such as "chicken teriyaki, spinach ohitashi, carrot and onion miso soup" using the "chicken, spinach, carrot and onion" in the refrigerator. The generated menu and recipe information is sent from the server to the device.

[0997] Step 5:

[0998] The device displays the received menu and recipes to the user. The user checks the displayed suggestions and, if there are any missing ingredients, selects the "Automatically purchase missing ingredients" option. Upon selection, the device sends a list of missing ingredients to the server.

[0999] Step 6:

[1000] The server connects to the online purchasing system and processes the necessary ingredients. For example, if there is a shortage of mirin in the refrigerator, the server uses the online supermarket's API to purchase mirin. After the purchase process is complete, the order details and delivery information are generated.

[1001] Step 7:

[1002] The server sends the order details and delivery information to the device, which then notifies the user. For example, a push notification saying "Mirin will be delivered tomorrow" can be sent to the user's smartphone, allowing them to confirm the necessary information.

[1003] (Application example 1)

[1004] 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."

[1005] Conventional refrigerator ingredient management and menu suggestion systems require users to record and manage ingredient information themselves, which is time-consuming, and the suggested menus do not always match the user's mood or preferences. Furthermore, if necessary ingredients are in short supply, users must manually purchase them, which is time-consuming and labor-intensive. Furthermore, they lack the functionality to notify users of delivery status in real time.

[1006] 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.

[1007] In this invention, the server includes means for acquiring information about ingredients in the refrigerator stored in a storage device through image analysis, means for analyzing natural language input from the user to interpret the user's mood and food preferences for that day, means for generating optimal menus and recipes based on the information about ingredients in the refrigerator and the user's input, means for displaying the generated menus and recipes on a user terminal, means for connecting with an online purchasing system to purchase ingredients that are missing, means for connecting the generated menus and recipes with a food sales system or a distribution system using natural language processing, and means for notifying the user of delivery status in real time. This reduces the burden on the user and makes it possible to automate the entire process from ingredient management to menu suggestions, purchasing procedures for missing ingredients, and notification of delivery status.

[1008] A "storage device" is an electronic device for storing data or information.

[1009] "Image analysis" is a technology that analyzes image data to recognize and distinguish the type and shape of an object.

[1010] "Natural language input" refers to a method of inputting information using a language that the user uses on a daily basis.

[1011] A "menu" refers to the combination of dishes for a particular meal.

[1012] A "recipe" is information that describes the steps and ingredients for making a particular dish.

[1013] An "online purchasing system" is a system for ordering and purchasing products via the Internet.

[1014] "Food sales system" refers to a system for selling ingredients and food.

[1015] A "distribution system" refers to the processes and mechanisms for delivering products and services from producers to consumers.

[1016] "Natural language processing" is a technology that uses computers to understand, analyze, and generate human language.

[1017] "Delivery status notification" is a function that notifies users of the progress and status of the delivery process of ordered items.

[1018] A "user terminal" is an electronic device that allows a user to access an application or system.

[1019] "Real-time notification" is a function that instantly notifies users of events or changes that are occurring.

[1020] This invention is a system that proposes optimal menus and recipes based on the user's mood and preferences based on information about ingredients in the refrigerator, and also allows the user to purchase ingredients that are in short supply online. The main processing flow of this system is shown below.

[1021] System Configuration

[1022] This system consists of a user, a server, and a terminal. The terminal can be a smartphone, smart glasses, a head-mounted display, or a robot. The server is located in a cloud environment and includes a database, an image analysis engine, a natural language processing engine, and a module for linking with the online purchasing system.

[1023] Examples of the invention

[1024] Program generation and processing overview

[1025] 1. Obtaining ingredient information:

[1026] Users take a photo of the inside of the refrigerator using the camera on their smartphone and upload it to the device. This photo is sent to the server, which uses an image analysis engine (e.g., OpenCV or TensorFlow) to recognize the ingredients in the refrigerator and store them as a list in a database.

[1027] 2. Parsing user input:

[1028] Users input their mood or what they want to eat that day into the device by voice or text. In the case of voice input, the device converts the input into text using speech recognition technology (e.g., Google Speech-to-Text API). The converted text is sent to the server and analyzed by a natural language processing engine (e.g., NLTK or SpaCy).

[1029] 3. Menu and recipe suggestions:

[1030] The server generates optimal menus and recipes based on information about ingredients in the refrigerator and user input. A generative AI model is used for generation. The generated menu and recipe information is sent to the device and displayed to the user.

[1031] 4. Automatically purchase ingredients when they are in short supply:

[1032] The user checks the proposed menu and, if any ingredients are missing, selects the "Buy automatically" option on the device. The server connects to the online purchasing system and automatically orders the necessary ingredients. Payment is made using an API (e.g., Stripe or PayPal).

[1033] 5. Delivery Status Notification:

[1034] The server monitors order details and delivery information in real time and notifies the terminal of the delivery status using services such as Firebase Cloud Messaging.

[1035] Specific examples

[1036] The user voice-inputs "I want to eat Italian today" into the device. By taking a photo of the contents of the refrigerator and uploading it to the app, "tomatoes, basil, and mozzarella cheese" are recognized. Based on this information, the server suggests "Caprese salad" and, if any necessary ingredients are missing, automatically completes the online purchase process. Delivery status is notified to the user's device in real time.

[1037] Prompt Sentence Examples

[1038] When a user types "I want to eat Italian food today," the prompt text is:

[1039] I want to eat Italian food today

[1040] In this way, the system of the present invention can perform intelligent ingredient management and menu suggestions, thereby increasing user convenience.

[1041] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1042] Step 1:

[1043] A user takes a photo of the inside of the refrigerator using a device and uploads it to the application. The input is the captured image, and the output is the image data sent to the server.

[1044] Step 2:

[1045] The server analyzes the received image data using an image analysis engine (e.g., OpenCV or TensorFlow). By analyzing the image, it creates a list of ingredients in the refrigerator and stores it in a database. The input is the image data, and the output is the analyzed list of ingredients in the refrigerator.

[1046] Step 3:

[1047] The user inputs their mood for the day and what they want to eat into the device by voice or text. The input is voice or text data. When voice input is used, the device converts the voice into text using voice recognition technology (for example, Google Speech-to-Text API). The output is text data.

[1048] Step 4:

[1049] The device sends the generated text data to the server, which then uses a natural language processing engine (e.g., NLTK or SpaCy) to analyze the user's preferences. The input is the text data, and the output is the analyzed preference information.

[1050] Step 5:

[1051] The server uses a generative AI model to generate optimal menus and recipes based on the information about ingredients in the refrigerator and the user's preferences. The input is the information about ingredients and preferences, and the output is the generated menu and recipes.

[1052] Step 6:

[1053] The server sends the generated menu and recipe information to the terminal, which displays it to the user. The input is the menu and recipe information, and the output is the display to the user.

[1054] Step 7:

[1055] The user reviews the proposed menu and, if there are any missing ingredients, selects the "Buy automatically" option on the device. The input is the user's selection, and the output is a list of missing ingredients.

[1056] Step 8:

[1057] The server connects to an online purchasing system (e.g., Amazon or Rakuten) based on the list of ingredients that are in short supply and orders the necessary ingredients. The input is the list of ingredients that are in short supply, and the output is a notification that the ordering process has been completed.

[1058] Step 9:

[1059] The server monitors order details and delivery information in real time and notifies the terminal of the delivery status. The input is order and delivery information, and the output is a notification to the user.

[1060] Specific examples

[1061] The user voice-records "I want to eat Italian food today" into the device and uploads a photo of what's in the refrigerator. The server analyzes the image and recognizes "tomatoes, basil, and mozzarella cheese," and based on this, suggests "Caprese salad." The user selects the option to automatically purchase ingredients that are running low (e.g., olive oil), and the server links with the online purchasing system to place the order. The user is notified of delivery status in real time.

[1062] 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.

[1063] Overall system configuration

[1064] This invention is a system that proposes optimal menus and recipes based on information about ingredients in the refrigerator, taking into consideration the user's mood, what they want to eat, and even their emotions, and also allows them to purchase ingredients they are running low on online.The system is mainly composed of a user, a server, a terminal, and an emotion engine.

[1065] Overall processing of the program

[1066] Registration and Initial Setup

[1067] 1. The user downloads the application and enters their name, address, email address, favorite ingredients, and allergy information when they first launch it.

[1068] 2. The device sends this information to the server, which creates a user profile.

[1069] Photography and food recognition

[1070] 3. The user takes a photo of the refrigerator and uploads it to the app. For example, the user opens the refrigerator door and takes a photo of the inside.

[1071] 4. The device uses image analysis technology to analyze the uploaded photos and generate a list of ingredients in the refrigerator.

[1072] Input of user's mood, desires, and emotions

[1073] 5. The user inputs their mood and food cravings by voice or text. For example, they might say, "I want something light today."

[1074] 6. The device converts the voice input into text (in the case of voice input) and analyzes it using natural language processing technology. The analyzed results are sent to the server.

[1075] 7. The emotion engine analyzes the user's input and obtains emotion information. For example, if the user is using a facial recognition camera, it will determine the emotion from their facial expressions.

[1076] 8. The device also sends the analyzed emotional information to the server.

[1077] Menu and recipe generation

[1078] 9. The server generates the optimal menu and recipe based on the information about ingredients in the refrigerator, the user's input, and emotional information. For example, it decides on a menu of "tomato and cucumber salad, lightly grilled chicken, and egg soup."

[1079] 10. The server sends the generated menu and recipe information to the terminal, which displays it to the user.

[1080] Complementing missing ingredients

[1081] 11. The user reviews the suggested menu and recipes and, if there are any missing ingredients, selects the "Automatically purchase missing ingredients" option.

[1082] 12. The device sends a list of ingredients that are missing to the server.

[1083] 13. The server connects to the distribution system and processes the necessary ingredients for purchase online. For example, the customer orders the necessary mirin from an online store.

[1084] Notification and Delivery

[1085] 14. The server returns the order details and delivery information to the terminal, and the terminal notifies the user of the order completion and delivery information. For example, it notifies the user that "Mirin will be delivered tomorrow."

[1086] Specific examples

[1087] 1. The user downloads the app and enters the required initial information.

[1088] 2. The user uploads a photo of the inside of their refrigerator to the app, and image analysis technology is used to create a list of "chicken, spinach, carrots, and onions."

[1089] 3. The user inputs "I want to eat Japanese food today" through voice input, which is then analyzed using natural language processing technology.

[1090] 4. The emotion engine obtains emotional information from the user's input and facial expressions and determines whether the user is in a "relaxed mood."

[1091] 5. The device sends this information to the server, which then generates a recipe for "Teriyaki chicken, boiled spinach, and miso soup with carrots and onions," which the device then displays.

[1092] 6. The user checks the proposed menu and, if there are any missing condiments (e.g., mirin), selects "Buy automatically."

[1093] 7. The server connects to the online purchasing system to order mirin and process the delivery.

[1094] 8. The terminal notifies the user that the mirin order has been completed and the estimated delivery date.

[1095] In this way, the system can effectively utilize the ingredients in the user's refrigerator, easily provide a menu that matches the user's mood or emotions that day, and enable the user to purchase the necessary ingredients online.

[1096] The processing flow will be explained below.

[1097] Step 1:

[1098] The user downloads the application and enters their name, address, email address, favorite ingredients, and allergy information when they first launch it.

[1099] Step 2:

[1100] The terminal transmits this user information to the server.

[1101] Step 3:

[1102] The server creates a user profile based on the received user information and stores it in a database.

[1103] Step 4:

[1104] The user takes a photo of the inside of the refrigerator and uploads it to the app. For example, the user opens the refrigerator door and takes a photo of the inside.

[1105] Step 5:

[1106] The device receives the uploaded photo, analyzes it using image analysis technology (such as OCR), and identifies the ingredients in the refrigerator to generate a list. For example, it recognizes "tomatoes, cucumbers, eggs, and chicken" from the photo.

[1107] Step 6:

[1108] The terminal transmits the analyzed ingredient list to the server.

[1109] Step 7:

[1110] The server updates the user profile based on the received ingredient list.

[1111] Step 8:

[1112] The user inputs their mood and what they want to eat that day by voice or text. For example, they might say, "I want to eat something light today."

[1113] Step 9:

[1114] The device converts the voice input into text (if voice input is used) and analyzes it using natural language processing technology. The analyzed information is then sent to the server.

[1115] Step 10:

[1116] The emotion engine infers the user's emotions based on the user's input, for example, by using a facial recognition camera to determine if the user wants to relax.

[1117] Step 11:

[1118] The terminal also transmits emotion information acquired by the emotion engine to the server.

[1119] Step 12:

[1120] The server generates optimal menus and recipes based on information about ingredients in the refrigerator, user input, and emotional information. For example, it suggests a menu of "tomato and cucumber salad, lightly grilled chicken, and egg soup."

[1121] Step 13:

[1122] The server sends the generated menu and recipe information to the terminal, which then displays it to the user. For example, the menu and recipes for each dish are displayed.

[1123] Step 14:

[1124] The user checks the suggested menu and recipes, and if there are any missing ingredients, selects the "Automatically purchase missing ingredients" option.

[1125] Step 15:

[1126] The terminal sends a list of ingredients that are missing to the server.

[1127] Step 16:

[1128] The server connects to the online purchasing system and processes the necessary ingredients. For example, the server orders the necessary mirin from the online store.

[1129] Step 17:

[1130] The server returns the order details and delivery information to the terminal. The terminal notifies the user of the order completion and delivery information. For example, it notifies the user that "Mirin will be delivered tomorrow."

[1131] This series of processes allows users to efficiently use the ingredients in their refrigerator, easily plan a menu that matches their mood or emotions for the day, and purchase the necessary ingredients online.

[1132] Example 2

[1133] 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."

[1134] In modern society, busy lives mean limited time for planning menus, making it difficult to propose meals that take into account the user's mood, health status, individual preferences, and allergies. Another problem is the lack of systems that can effectively utilize ingredients in the refrigerator while automatically purchasing ingredients that are in short supply. To solve this situation, a system is needed that proposes optimal menus based on the ingredients in the user's refrigerator and their mood and emotions for the day, and automatically purchases the necessary ingredients online.

[1135] 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.

[1136] In this invention, the server includes means for acquiring information about ingredients in the refrigerator stored in a storage device through image analysis, means for analyzing natural language input from the user to interpret the user's mood and food preferences for that day, means for generating optimal menus and recipes based on the information about ingredients in the refrigerator and the user's input and emotional information, means for displaying the generated menus and recipes on a user terminal, means for purchasing ingredients that are in short supply by connecting to an online purchasing system, and means for acquiring emotional information by recognizing the user's facial expression. This makes the user's life more comfortable and convenient, allowing them to easily decide on daily menus and automatically replenish necessary ingredients.

[1137] "Storage device" refers to hardware or software for storing data, and specifically includes hard disk drives and solid-state drives.

[1138] "Image analysis" is a technology that uses photographs or image data to recognize specific objects or extract specific information.

[1139] "Natural language input" is a method for allowing a system to understand and analyze the content of input in the language (voice or text) that a user uses on a daily basis.

[1140] "Menu" refers to the combination and order of dishes served at a meal.

[1141] A "recipe" is a set of instructions that outlines the steps and ingredient combinations required to prepare a particular dish.

[1142] A "user terminal" is a device that is directly operated by a user, and specifically refers to electronic devices such as smartphones and tablets.

[1143] An "online purchasing system" is a system for purchasing products via the Internet, and specifically includes online stores and e-commerce platforms.

[1144] "User profile information" is a collection of individual pieces of information related to a user, including name, address, email address, favorite ingredients, allergy information, and the like.

[1145] "Speech recognition technology" is a technology that analyzes input voice data and converts it into text data.

[1146] "Natural language processing technology" is a technology that allows computers to understand and analyze natural human language, and specifically includes semantic analysis and context understanding.

[1147] "Emotion information" is information relating to the user's mood or emotional state, and specifically is data indicating an emotional state such as "I want to relax" or "I feel stressed."

[1148] This invention is a system that proposes optimal menus and recipes based on information about ingredients in the refrigerator, taking into consideration the user's mood, what they want to eat, and even their emotions, and also allows them to purchase ingredients they are running low on online.The system is mainly composed of a server, terminals, users, and an emotion engine.

[1149] Registration and Initial Setup

[1150] A user downloads and installs the application onto a device such as a smartphone or tablet. When the application is launched for the first time, the user enters their name, address, email address, favorite foods, allergy information, etc. The device sends this information to the server, which then creates a user profile based on the received information and stores it in a database.

[1151] Photography and food recognition

[1152] The user takes a photo of the inside of the refrigerator and uploads it to the application. The device analyzes the photo using image analysis technology (e.g., Google Cloud Vision API), creates a list of the ingredients in the refrigerator, and sends it to the server.

[1153] Input of user's mood, desires, and emotions

[1154] The user tells the app how they feel that day and what they want to eat by voice or text input. For example, they might say, "I want something light today." The device uses voice recognition technology (e.g., Google Cloud Speech-to-Text) to convert the speech into text, which is then analyzed using natural language processing technology (e.g., OpenAI GPT-4). The analysis results are sent to the server.

[1155] The emotion engine analyzes emotional information based on the user's input data and data acquired from the facial recognition camera. For example, it analyzes emotional information such as "I feel like relaxing." The device then sends this emotional information to the server.

[1156] Menu and recipe generation

[1157] The server generates the optimal menu and recipes based on information about ingredients in the refrigerator, the user's mood, preferences, and emotional information. For example, it generates a menu such as "tomato and cucumber salad, lightly grilled chicken, and egg soup." The server sends the generated menu and recipe information to the terminal, which then displays the information to the user.

[1158] Complementing missing ingredients

[1159] The user checks the provided menu and recipe, and if there are any missing ingredients, selects the "Automatically purchase missing ingredients" option. The device sends a list of the missing ingredients to the server. The server then connects to the online purchasing system and carries out the procedure to purchase the missing ingredients online. For example, the user orders the required "mirin" from an online store.

[1160] Notification and Delivery

[1161] The server returns the order details and delivery information to the terminal, and the terminal notifies the user of the order completion and delivery information. For example, it notifies the user that "Mirin will be delivered the next day."

[1162] Examples of specific examples and prompts

[1163] Specific examples

[1164] The user downloads the app and enters initial information such as name, address, email address, favorite ingredients, and allergy information.

[1165] Users take a photo of the inside of their refrigerator and upload it to the app, which uses image analysis technology to analyze the photo and produce a list of "chicken, spinach, carrots, and onions."

[1166] The user inputs "I want to eat Japanese food today" by voice, and the device converts this into text using voice recognition technology, which is then analyzed using natural language processing technology.

[1167] The emotion engine determines whether you feel like relaxing based on input and facial expression data.

[1168] The device sends information about ingredients, mood, and emotions to the server, and the server generates a recipe for "teriyaki chicken, boiled spinach, and miso soup with carrots and onions," which is then displayed on the device.

[1169] The user reviews the suggested menu and selects the option to automatically purchase any mirin they are running low on.

[1170] The server connects to the online purchasing system to order mirin and process the delivery.

[1171] The terminal notifies the user of the order completion and estimated delivery date.

[1172] Prompt Sentence Examples

[1173] "Please upload a photo of the inside of your refrigerator and list the ingredients in it."

[1174] "I want to eat Japanese food today. I'd like you to suggest a relaxing menu."

[1175] "Show me recipes for chicken teriyaki, spinach ohitashi, and carrot and onion miso soup."

[1176] "Automatically purchase mirin online if I'm running low on it."

[1177] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1178] System processing steps

[1179] Step 1: Enter your user information and create your profile

[1180] The user downloads and installs the application.

[1181] Input: Name, address, email address, favorite ingredients, allergy information.

[1182] Specific operations: Launch the app and enter the required information on the initial setup screen.

[1183] The terminal transmits the input information to the server.

[1184] Output: The user profile is saved on the server.

[1185] Step 2: Take a photo of the inside of your refrigerator and upload it

[1186] A user opens the refrigerator door and takes a photo of the inside.

[1187] Input: A photo of the inside of a refrigerator.

[1188] Specific operation: Take a photo of the inside of the refrigerator using your smartphone camera and upload it to the app.

[1189] The device analyzes the uploaded photos using image analysis technology (e.g., Google Cloud Vision API).

[1190] Output: A list of ingredients in the refrigerator is generated.

[1191] Step 3: Submit and save ingredient information

[1192] The terminal transmits the generated ingredient list to the server.

[1193] Input: Ingredient list.

[1194] Specific operation: The ingredient list obtained through image analysis technology is sent to the server.

[1195] The server stores the ingredient information in a database.

[1196] Output: Ingredient information is saved in the database.

[1197] Step 4: Enter the user's mood and wishes

[1198] The user inputs their mood and what they want to eat that day using voice or text.

[1199] Input: Voice or text to indicate your mood or what you want to eat that day.

[1200] What it does: Enter your desired eating style using the app's input field or voice input function.

[1201] The device uses voice recognition technology (e.g., Google Cloud Speech-to-Text) to convert the speech into text, which is then analyzed using natural language processing technology (e.g., OpenAI GPT-4).

[1202] Output: The analyzed mood and preference information is available in text format.

[1203] Step 5: Send and save your mood and preferences

[1204] The device sends the analyzed mood and preference information to the server.

[1205] Input: Mood and preference information in text format.

[1206] Specific operation: Executes a process to send the analysis results to the server.

[1207] The server stores mood and preference information in a database.

[1208] Output: Mood and preference information is saved in a database.

[1209] Step 6: Acquiring and transmitting emotional information

[1210] The emotion engine recognizes the user's facial expressions and analyzes emotional information.

[1211] Input: User's facial expression data.

[1212] Specific operation: Using a facial expression recognition camera, the user's facial expressions are captured and their emotions are analyzed.

[1213] The emotion engine sends the emotion information to the server.

[1214] Output: Emotion information is saved in text format on the server.

[1215] Step 7: Create menus and recipes

[1216] The server combines information about ingredients in the refrigerator with the user's mood, preferences, and emotional information to generate optimal menus and recipes.

[1217] Input: Ingredient information, mood and preference information, emotional information.

[1218] Specific operation: Extracts information on ingredients, mood, preferences, and emotions from the database, and analyzes them comprehensively to generate appropriate menus and recipes.

[1219] Output: Generated menu and recipe information.

[1220] Step 8: Send and display menu and recipe information

[1221] The server sends the generated menu and recipe information to the terminal.

[1222] Input: Menu and recipe information.

[1223] Specific operation: The generated menu and recipes are sent to the user's terminal and presented to the user through the display function.

[1224] Output: Menu and recipes displayed on the terminal.

[1225] Step 9: Select the option to automatically purchase ingredients when they run out

[1226] The user checks the menu and, if there are any missing ingredients, selects the "Automatically purchase missing ingredients" option.

[1227] Input: User's choice.

[1228] What it does: Check menus and recipes, then tap to select the option you want.

[1229] The terminal sends a list of ingredients that are missing to the server.

[1230] Output: List of missing ingredients.

[1231] Step 10: Purchase missing ingredients online

[1232] The server connects to the online purchasing system and carries out the procedure to purchase the necessary ingredients.

[1233] Input: List of missing ingredients.

[1234] Specific operation: Use the API of the online purchasing system to automatically proceed with the purchase procedure and start the process to order the specified ingredients.

[1235] Output: Order completed notification.

[1236] Step 11: Order and Shipping Information Notification

[1237] The server returns the order details and delivery information to the terminal.

[1238] Input: Order details and shipping information.

[1239] Specific operation: After the order is completed, the contents and estimated delivery date are notified to the user's terminal.

[1240] The terminal notifies the user of the order completion and delivery information.

[1241] Output: The user receives a notification of order completion and shipping information.

[1242] The above are the specific processing steps of this system.

[1243] (Application example 2)

[1244] 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."

[1245] While conventional food management systems have the ability to suggest recipes to users based on information about ingredients in the refrigerator, many lack the ability to link with user emotions and store inventory information, making it impossible to suggest recipes that best suit the user's current mood or emotions. Furthermore, they lack the ability to identify the location of ingredients that are missing in the store, which can make the user's shopping experience more complicated.

[1246] The identification process by the identification 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 means for acquiring ingredient information stored in a storage device through image analysis, means for analyzing natural language input from the user to interpret the user's mood and food preferences for that day, means for analyzing the user's emotional information and optimizing menus and recipes based on the analysis, means for linking with in-store inventory information to provide information on the location of missing ingredients, means for displaying the generated menus and recipes on the user's terminal, and means for linking with an online purchasing system to purchase missing ingredients. This enables the server to suggest optimal recipes tailored to the user's mood and emotions and provide a smooth shopping experience in the store.

[1247] A "storage device" is hardware or software for storing data and information.

[1248] "Food information" refers to data such as the type, quantity, and freshness of food and ingredients in the refrigerator or store.

[1249] "Image analysis" is a technique for analyzing image data and extracting useful information from it.

[1250] "Natural language input" refers to the input of instructions or questions by a user using everyday language, either by voice or text.

[1251] "User input" means data provided to the system by a user through natural language input or other means.

[1252] "Menu" refers to the entire meal menu for that day.

[1253] A "recipe" is a detailed set of instructions and ingredients for making a particular dish.

[1254] "Emotional information" is data that indicates the user's emotional state and is obtained from facial expression analysis, voice tone, etc.

[1255] A "user terminal" is a device that is directly operated by a user, such as a smartphone, smart glasses, or a head-mounted display.

[1256] An "online purchasing system" is a platform for purchasing products via the Internet.

[1257] "Store inventory information" is data on products and ingredients currently available in the store.

[1258] "Guidance means" refers to a method or system that shows the user the location of a destination or product.

[1259] This invention is an in-store ingredient management and recommendation system that proposes optimal menus and recipes based on the user's mood and emotions, and also supports the purchase of ingredients that are in short supply. The system is mainly composed of a storage device, a user terminal, a server, an emotion engine, and an in-store inventory information system.

[1260] Hardware and software used

[1261] Hardware:

[1262] Smartphones, smart glasses, head-mounted displays (user devices)

[1263] Camera (for facial expression analysis)

[1264] software:

[1265] Image analysis libraries (e.g., Firebase ML Kit)

[1266] Natural language processing engine (e.g. Google Cloud Natural Language API)

[1267] Sentiment analysis engine (e.g. Affectiva SDK)

[1268] Cloud databases (e.g. Firebase Firestore)

[1269] Online purchasing system API (e.g. Amazon API)

[1270] Processing flow

[1271] 1. User Profile Management:

[1272] A user installs the app and enters initial information such as their name, address, email address, favorite ingredients, allergy information, etc. This user information is stored in a cloud database (Firebase Firestore).

[1273] 2. Food recognition:

[1274] The user takes a photo of the food shelves in the store using smart glasses or a smartphone. The image data is analyzed using an image analysis library (Firebase ML Kit) to obtain food information.

[1275] 3. Parsing user-entered information:

[1276] The user inputs something like "I want to eat something light today" via voice or text. This input is converted into text using speech recognition technology, and then analyzed by a natural language processing engine (Google Cloud Natural Language API).

[1277] 4. Acquiring emotional information:

[1278] The user's facial expressions are captured by the camera in the smart glasses or head-mounted display and analyzed by the emotion analysis engine (Affectiva SDK), thereby obtaining the user's emotional information.

[1279] 5. Menu and recipe generation:

[1280] The server generates optimal menus and recipes based on the ingredient information stored in the cloud database, the user's input information, and emotional information. This information is displayed on the user's device.

[1281] 6. Purchase and guidance of missing ingredients:

[1282] A list of missing ingredients is generated based on the recipe. This is checked against the store's inventory information, and if any are missing, they are ordered and purchased through the online purchasing system (Amazon API). If the missing ingredients are available in the store, the user is informed of their location.

[1283] Specific examples

[1284] 1. While walking around the store, the user scans the shelves with smart glasses and then voice-inputs, "I'd like to eat a light Japanese meal today."

[1285] 2. The system recognizes "salmon, tofu, and green onions" from an image of a product shelf, and, combined with the user information, suggests "grilled salmon with salt and tofu miso soup."

[1286] 3. The app will notify you when you are low on soy sauce and use your location information in the store to guide you to the soy sauce section.

[1287] Prompt Sentence Examples

[1288] You're using an "intelligent shopping assistant" app. It analyzes photos of shelves you take in a store and suggests recipes based on ingredients that fit your current mood. You enter, "I want something light today."

[1289] This will enable the system to suggest optimal recipes that match the user's mood and emotions, as well as provide a smooth shopping experience in stores.

[1290] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1291] Step 1:

[1292] Users install the application and enter their name, address, email address, favorite foods, and allergy information. This initial information is stored in a cloud database. The user's personal information and preferences are used as input data to create a user profile. The server uses this data to build an individual user profile.

[1293] Step 2:

[1294] The user takes a photo of the food shelves in the store using smart glasses or a smartphone and uploads it to the app. The image data is analyzed using an image analysis library (e.g., Firebase ML Kit) to extract information about the ingredients. The input is the captured image data, and the output is a list of analyzed ingredients. The device displays this list and also sends it to the server.

[1295] Step 3:

[1296] The user inputs a voice or text message such as "I want something light today." In the case of voice input, the message is converted into text using speech recognition technology and analyzed by a natural language processing engine (e.g., Google Cloud Natural Language API). The input data is voice or text, and the output is text data that reflects the analyzed user's mood and preferences. The server uses the results of this analysis to generate menu suggestions.

[1297] Step 4:

[1298] The user's facial expressions are captured by a camera in smart glasses or a head-mounted display and analyzed by an emotion analysis engine (e.g., Affectiva SDK). The captured image or video data is used as input data, and the output is the user's emotional information. This emotional information is sent to a server and analyzed together with the user's mood and wishes.

[1299] Step 5:

[1300] The server generates optimal menus and recipes based on ingredient information stored in the cloud database, user input information, and emotional information. The input data is the ingredient list, the user's preferences and mood information, and emotional information, and the output is optimized menus and recipes. The generated information is sent to the user's device and displayed to the user.

[1301] Step 6:

[1302] The terminal displays the generated menu and recipes to the user, and also presents a list of missing ingredients. The input data are the generated menu and recipes, and the output is the list of missing ingredients.

[1303] Step 7:

[1304] The server checks the store's inventory information and, if there are shortages, orders and purchases ingredients through an online purchasing system (e.g., Amazon API). If the missing ingredients are available in the store, the server guides the user to their location. The input data is inventory information and a list of ingredients that are in short supply, and the output is purchase completion information and in-store guidance information. The terminal notifies the user of this.

[1305] In this way, a system is formed that inputs, analyzes, and outputs data at each step to provide the user with the optimal experience.

[1306] 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.

[1307] 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.

[1308] 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.

[1309] [Fourth embodiment]

[1310] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1311] 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.

[1312] 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).

[1313] 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.

[1314] 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.

[1315] 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).

[1316] 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.

[1317] 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.

[1318] 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.

[1319] 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.

[1320] 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.

[1321] 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.

[1322] 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."

[1323] Overall system configuration

[1324] This invention is a system that proposes optimal menus and recipes based on information about ingredients in the refrigerator, taking into account the user's mood and food preferences, and also allows the user to purchase ingredients online if they are running low. This system is mainly composed of users, a server, and terminals.

[1325] Overall processing of the program

[1326] Registration and Initial Setup

[1327] 1. The user downloads the application and enters their name, address, email address, favorite ingredients, and allergy information when they first launch it.

[1328] 2. The device sends this information to the server, which creates a user profile.

[1329] Photography and food recognition

[1330] 3. The user takes a photo of the refrigerator and uploads it to the app. For example, the user opens the refrigerator door and takes a photo of the inside.

[1331] 4. The device uses image analysis technology to analyze the uploaded photo and create a list of the ingredients in the refrigerator.

[1332] Input of user's mood and desires

[1333] 5. The user inputs their mood and food cravings by voice or text. For example, they might say, "I want something light today."

[1334] 6. The device converts the voice input into text and analyzes it using natural language processing technology.

[1335] 7. The device sends the analysis results to the server, which then generates the optimal menu and recipe based on the information about ingredients in the refrigerator and the information entered by the user.

[1336] Providing menus and recipes

[1337] 8. The server sends the generated menu and recipe information to the device, which displays it to the user. For example, the device suggests a menu of "tomato and cucumber salad, lightly grilled chicken, and egg soup."

[1338] Complementing missing ingredients

[1339] 9. The user checks the proposed menu and, if there are any missing ingredients, selects the "Automatically purchase missing ingredients" option.

[1340] 10. The device sends the list of ingredients needed to the server, and the server connects to the online purchasing system to purchase the ingredients. For example, if mirin is in short supply, the server will purchase it from an online supermarket.

[1341] Notification and Delivery

[1342] 11. The server returns the order details and delivery information to the terminal, and the terminal notifies the user of the order completion and delivery information. For example, it notifies the user that "Mirin will be delivered tomorrow."

[1343] Specific examples

[1344] 1. The user downloads the app and enters the required initial information.

[1345] 2. The user uploads a photo of the inside of their refrigerator to the app, and image analysis technology is used to create a list of "chicken, spinach, carrots, and onions."

[1346] 3. The user inputs "I want to eat Japanese food today" through voice input, which is then analyzed using natural language processing technology.

[1347] 4. The device sends the analysis results to the server, which generates a recipe for "chicken teriyaki, spinach ohitashi, and carrot and onion miso soup," which is then displayed on the device.

[1348] 5. The user checks the proposed menu and, if there are any missing condiments (e.g., mirin), selects "Buy automatically."

[1349] 6. The server connects to the online purchasing system to order mirin and process the delivery.

[1350] 7. The terminal notifies the user that the mirin order has been completed and the estimated delivery date.

[1351] In this way, the system can effectively utilize the ingredients in the user's refrigerator, easily provide a menu that suits the mood of the day, and realize a series of steps to purchase the necessary ingredients online.

[1352] The processing flow will be explained below.

[1353] Step 1:

[1354] The user downloads the application and enters their name, address, email address, favorite ingredients, and allergy information when they first launch it.

[1355] Step 2:

[1356] The terminal transmits the user's input information to the server.

[1357] Step 3:

[1358] The server creates a user profile based on the received user information and stores it in a database.

[1359] Step 4:

[1360] The user takes a photo of the inside of the refrigerator and uploads it to the app. For example, the user opens the refrigerator door, takes a photo of the inside, and presses the "upload" button.

[1361] Step 5:

[1362] The device receives the uploaded photo, analyzes it using OCR and image analysis technology, and generates a list of the ingredients in the refrigerator. For example, it recognizes "tomatoes, cucumbers, eggs, and chicken" from the photo.

[1363] Step 6:

[1364] The terminal transmits the ingredient list generated as a result of the analysis to the server.

[1365] Step 7:

[1366] The server updates the ingredient information in the user profile based on the received ingredient list.

[1367] Step 8:

[1368] The user inputs their mood and what they want to eat that day by voice or text. For example, they can input "I want to eat something light today."

[1369] Step 9:

[1370] The device converts the voice input into text (in the case of voice input) and analyzes it using natural language processing technology. The analyzed results are then sent to the server.

[1371] Step 10:

[1372] The server generates the optimal menu and recipe based on the information about ingredients in the refrigerator and the information entered by the user. For example, it decides on a menu of "tomato and cucumber salad, lightly grilled chicken, and egg soup."

[1373] Step 11:

[1374] The server returns the generated menu and recipe information to the terminal, and the terminal displays the menu and recipe to the user.

[1375] Step 12:

[1376] The user checks the suggested menu and recipes, and if there are any missing ingredients, selects the "Automatically purchase missing ingredients" option.

[1377] Step 13:

[1378] The terminal sends a list of ingredients that are missing to the server.

[1379] Step 14:

[1380] The server connects to the distribution system and processes the necessary ingredients for purchase online. For example, you can order the necessary mirin from an online store.

[1381] Step 15:

[1382] The server returns the order details and delivery information to the terminal, and the terminal notifies the user of the order completion and delivery information. For example, it notifies the user that "Mirin will be delivered tomorrow."

[1383] This allows users to make effective use of ingredients in their refrigerators and enjoy a menu that suits their mood that day, easily and without hassle.

[1384] Example 1

[1385] 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."

[1386] In recent years, there has been a growing need for systems that efficiently utilize ingredients in the refrigerator and propose menus that match the user's mood and preferences. However, current systems have difficulty consistently obtaining information about ingredients in the refrigerator, making proposals based on the user's mood, and even purchasing ingredients online if they are in short supply. There is also a lack of systems that can respond individually to users' preferences and allergies. There is a need for a system that solves these problems.

[1387] 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.

[1388] In this invention, the server includes means for acquiring information about ingredients in the refrigerator stored in a storage device through image analysis, means for analyzing natural language input from the user to interpret the user's mood and food preferences for that day, means for generating optimal menus and recipes based on the information about ingredients in the refrigerator and the user's input information, means for displaying the generated menus and recipes on a user terminal, means for purchasing missing ingredients by linking with an online purchasing system, means for creating a user profile based on initial information input by the user and saving it in a storage device, means for the user to take a photo of the refrigerator and upload it to an application, and means for converting voice input into text and transmitting the analysis results to the server. This makes it possible to consistently suggest optimal menus based on the ingredients in the refrigerator and automatically purchase missing ingredients.

[1389] "Storage device" refers to hardware or software for storing and retrieving data.

[1390] "Information about ingredients in the refrigerator" refers to detailed data such as the type, quantity, and shelf life of various ingredients stored in the refrigerator.

[1391] "Image analysis" refers to the technology of analyzing image data to extract specific information or features.

[1392] "Natural language input" refers to inputting human language such as voice or text.

[1393] "User profile" refers to data that includes personal information such as a user's name, address, email address, favorite foods, and allergy information.

[1394] A "menu" refers to a combination of menus and recipes for a particular meal.

[1395] A "recipe" is a detailed set of instructions on how to prepare a particular dish.

[1396] "User terminal" refers to an electronic device operated by a user, such as a smartphone or tablet.

[1397] An "online purchasing system" refers to a system that allows you to purchase products via the Internet.

[1398] "Voice recognition technology" refers to technology that converts voice data into text.

[1399] "Natural language processing technology" refers to technology that understands and analyzes human language.

[1400] "Upload" refers to the act of sending data from a local device to a remote server or cloud.

[1401] This invention is a system that proposes optimal menus and recipes based on information about ingredients in the refrigerator, taking into account the user's mood and food preferences, and also allows the user to purchase ingredients online if they are running low. This system is mainly composed of users, a server, and terminals.

[1402] Registration and Initial Setup

[1403] When a user downloads a mobile application and launches it for the first time, they enter their name, address, email address, favorite foods, and allergy information. This information is sent from the device to a server, which then uses it to create a user profile in a database. The database is built using MySQL or MongoDB.

[1404] Photography and food recognition

[1405] The user takes a photo of the refrigerator and uploads it to the app. The device analyzes the uploaded photo using image analysis technology (e.g., TensorFlow or OpenCV) and creates a list of the ingredients in the refrigerator. For example, if the app recognizes that the refrigerator contains chicken, spinach, carrots, and onions, it generates a list of these ingredients.

[1406] Input of user's mood and desires

[1407] The user inputs their mood or what they want to eat that day through voice or text input. For example, they can input "I want to eat Japanese food today." The device converts the voice to text using the Google Cloud Speech-to-Text API and analyzes the text using natural language processing techniques such as BERT. The analysis results are sent from the device to the server.

[1408] Providing menus and recipes

[1409] The server uses a generative AI model to generate optimal menus and recipes based on information about ingredients in the refrigerator and user input. The generated menu and recipe information is sent from the server to the device, which displays it to the user. For example, it might suggest a menu such as "Teriyaki chicken, boiled spinach, and miso soup with carrots and onions."

[1410] Complementing missing ingredients

[1411] The user checks the proposed menu and, if there are any missing ingredients, selects the "Automatically purchase missing ingredients" option. The device sends a list of the required ingredients to the server, which then connects to the online purchasing system to purchase the necessary ingredients. For example, if mirin (sweet rice wine) is in short supply, the API of an online supermarket is used to purchase mirin.

[1412] Notification and Delivery

[1413] The server returns the order details and delivery information to the terminal, and the terminal notifies the user of the order completion and delivery information. For example, the terminal may notify the user that "Mirin will be delivered tomorrow."

[1414] Prompt Sentence Examples

[1415] "A user uploads a photo of the inside of their refrigerator, and chicken, spinach, and carrots are listed. If they enter 'I feel like eating Japanese food today,' please suggest the best menu and recipes for them."

[1416] In this way, the system can effectively utilize the ingredients in the user's refrigerator, easily provide a menu that suits the mood of the day, and realize a series of steps to purchase the necessary ingredients online.

[1417] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1418] Step 1:

[1419] A user downloads a mobile application and enters their name, address, email address, favorite ingredients, and allergy information when they first launch it. The entered data is sent from the device to a server. The server analyzes the received data and stores it as a user profile in a database such as MySQL or MongoDB. This profile allows personalized suggestions to be made to each individual user.

[1420] Step 2:

[1421] The user takes a photo of the inside of the refrigerator and uploads it to the application. The device receives this image data and analyzes it using image analysis technology (for example, TensorFlow or OpenCV). As a result of the analysis, the recognized food data is compiled into a list and sent from the device to the server. The server records the food information in the refrigerator based on this list.

[1422] Step 3:

[1423] The user inputs their mood for the day or what they want to eat by voice or text. For example, they can input "I want to eat Japanese food today." The device converts the voice data into text using the Google Cloud Speech-to-Text API, and analyzes this text data using natural language processing technology (such as BERT). The analysis results are sent to the server as data containing the user's input intent.

[1424] Step 4:

[1425] The server uses a generative AI model to generate optimal menus and recipes based on information about ingredients in the refrigerator and information input by the user. As a specific example, if the user requests "Japanese food," the server will suggest a menu such as "chicken teriyaki, spinach ohitashi, carrot and onion miso soup" using the "chicken, spinach, carrot and onion" in the refrigerator. The generated menu and recipe information is sent from the server to the device.

[1426] Step 5:

[1427] The device displays the received menu and recipes to the user. The user checks the displayed suggestions and, if there are any missing ingredients, selects the "Automatically purchase missing ingredients" option. Upon selection, the device sends a list of missing ingredients to the server.

[1428] Step 6:

[1429] The server connects to the online purchasing system and processes the necessary ingredients. For example, if there is a shortage of mirin in the refrigerator, the server uses the online supermarket's API to purchase mirin. After the purchase process is complete, the order details and delivery information are generated.

[1430] Step 7:

[1431] The server sends the order details and delivery information to the device, which then notifies the user. For example, a push notification saying "Mirin will be delivered tomorrow" can be sent to the user's smartphone, allowing them to confirm the necessary information.

[1432] (Application example 1)

[1433] 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."

[1434] Conventional refrigerator ingredient management and menu suggestion systems require users to record and manage ingredient information themselves, which is time-consuming, and the suggested menus do not always match the user's mood or preferences. Furthermore, if necessary ingredients are in short supply, users must manually purchase them, which is time-consuming and labor-intensive. Furthermore, they lack the functionality to notify users of delivery status in real time.

[1435] 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.

[1436] In this invention, the server includes means for acquiring information about ingredients in the refrigerator stored in a storage device through image analysis, means for analyzing natural language input from the user to interpret the user's mood and food preferences for that day, means for generating optimal menus and recipes based on the information about ingredients in the refrigerator and the user's input, means for displaying the generated menus and recipes on a user terminal, means for connecting with an online purchasing system to purchase ingredients that are missing, means for connecting the generated menus and recipes with a food sales system or a distribution system using natural language processing, and means for notifying the user of delivery status in real time. This reduces the burden on the user and makes it possible to automate the entire process from ingredient management to menu suggestions, purchasing procedures for missing ingredients, and notification of delivery status.

[1437] A "storage device" is an electronic device for storing data or information.

[1438] "Image analysis" is a technology that analyzes image data to recognize and distinguish the type and shape of an object.

[1439] "Natural language input" refers to a method of inputting information using a language that the user uses on a daily basis.

[1440] A "menu" refers to the combination of dishes for a particular meal.

[1441] A "recipe" is information that describes the steps and ingredients for making a particular dish.

[1442] An "online purchasing system" is a system for ordering and purchasing products via the Internet.

[1443] "Food sales system" refers to a system for selling ingredients and food.

[1444] A "distribution system" refers to the processes and mechanisms for delivering products and services from producers to consumers.

[1445] "Natural language processing" is a technology that uses computers to understand, analyze, and generate human language.

[1446] "Delivery status notification" is a function that notifies users of the progress and status of the delivery process of ordered items.

[1447] A "user terminal" is an electronic device that allows a user to access an application or system.

[1448] "Real-time notification" is a function that instantly notifies users of events or changes that are occurring.

[1449] This invention is a system that proposes optimal menus and recipes based on the user's mood and preferences based on information about ingredients in the refrigerator, and also allows the user to purchase ingredients that are in short supply online. The main processing flow of this system is shown below.

[1450] System Configuration

[1451] This system consists of a user, a server, and a terminal. The terminal can be a smartphone, smart glasses, a head-mounted display, or a robot. The server is located in a cloud environment and includes a database, an image analysis engine, a natural language processing engine, and a module for linking with the online purchasing system.

[1452] Examples of the invention

[1453] Program generation and processing overview

[1454] 1. Obtaining ingredient information:

[1455] Users take a photo of the inside of the refrigerator using the camera on their smartphone and upload it to the device. This photo is sent to the server, which uses an image analysis engine (e.g., OpenCV or TensorFlow) to recognize the ingredients in the refrigerator and store them as a list in a database.

[1456] 2. Parsing user input:

[1457] Users input their mood or what they want to eat that day into the device by voice or text. In the case of voice input, the device converts the input into text using speech recognition technology (e.g., Google Speech-to-Text API). The converted text is sent to the server and analyzed by a natural language processing engine (e.g., NLTK or SpaCy).

[1458] 3. Menu and recipe suggestions:

[1459] The server generates optimal menus and recipes based on information about ingredients in the refrigerator and user input. A generative AI model is used for generation. The generated menu and recipe information is sent to the device and displayed to the user.

[1460] 4. Automatically purchase ingredients when they are in short supply:

[1461] The user checks the proposed menu and, if any ingredients are missing, selects the "Buy automatically" option on the device. The server connects to the online purchasing system and automatically orders the necessary ingredients. Payment is made using an API (e.g., Stripe or PayPal).

[1462] 5. Delivery Status Notification:

[1463] The server monitors order details and delivery information in real time and notifies the terminal of the delivery status using services such as Firebase Cloud Messaging.

[1464] Specific examples

[1465] The user voice-inputs "I want to eat Italian today" into the device. By taking a photo of the contents of the refrigerator and uploading it to the app, "tomatoes, basil, and mozzarella cheese" are recognized. Based on this information, the server suggests "Caprese salad" and, if any necessary ingredients are missing, automatically completes the online purchase process. Delivery status is notified to the user's device in real time.

[1466] Prompt Sentence Examples

[1467] When a user types "I want to eat Italian food today," the prompt text is:

[1468] I want to eat Italian food today

[1469] In this way, the system of the present invention can perform intelligent ingredient management and menu suggestions, thereby increasing user convenience.

[1470] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1471] Step 1:

[1472] A user takes a photo of the inside of the refrigerator using a device and uploads it to the application. The input is the captured image, and the output is the image data sent to the server.

[1473] Step 2:

[1474] The server analyzes the received image data using an image analysis engine (e.g., OpenCV or TensorFlow). By analyzing the image, it creates a list of ingredients in the refrigerator and stores it in a database. The input is the image data, and the output is the analyzed list of ingredients in the refrigerator.

[1475] Step 3:

[1476] The user inputs their mood for the day and what they want to eat into the device by voice or text. The input is voice or text data. When voice input is used, the device converts the voice into text using voice recognition technology (for example, Google Speech-to-Text API). The output is text data.

[1477] Step 4:

[1478] The device sends the generated text data to the server, which then uses a natural language processing engine (e.g., NLTK or SpaCy) to analyze the user's preferences. The input is the text data, and the output is the analyzed preference information.

[1479] Step 5:

[1480] The server uses a generative AI model to generate optimal menus and recipes based on the information about ingredients in the refrigerator and the user's preferences. The input is the information about ingredients and preferences, and the output is the generated menu and recipes.

[1481] Step 6:

[1482] The server sends the generated menu and recipe information to the terminal, which displays it to the user. The input is the menu and recipe information, and the output is the display to the user.

[1483] Step 7:

[1484] The user reviews the proposed menu and, if there are any missing ingredients, selects the "Buy automatically" option on the device. The input is the user's selection, and the output is a list of missing ingredients.

[1485] Step 8:

[1486] The server connects to an online purchasing system (e.g., Amazon or Rakuten) based on the list of ingredients that are in short supply and orders the necessary ingredients. The input is the list of ingredients that are in short supply, and the output is a notification that the ordering process has been completed.

[1487] Step 9:

[1488] The server monitors order details and delivery information in real time and notifies the terminal of the delivery status. The input is order and delivery information, and the output is a notification to the user.

[1489] Specific examples

[1490] The user voice-records "I want to eat Italian food today" into the device and uploads a photo of what's in the refrigerator. The server analyzes the image and recognizes "tomatoes, basil, and mozzarella cheese," and based on this, suggests "Caprese salad." The user selects the option to automatically purchase ingredients that are running low (e.g., olive oil), and the server links with the online purchasing system to place the order. The user is notified of delivery status in real time.

[1491] 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.

[1492] Overall system configuration

[1493] This invention is a system that proposes optimal menus and recipes based on information about ingredients in the refrigerator, taking into consideration the user's mood, what they want to eat, and even their emotions, and also allows them to purchase ingredients they are running low on online.The system is mainly composed of a user, a server, a terminal, and an emotion engine.

[1494] Overall processing of the program

[1495] Registration and Initial Setup

[1496] 1. The user downloads the application and enters their name, address, email address, favorite ingredients, and allergy information when they first launch it.

[1497] 2. The device sends this information to the server, which creates a user profile.

[1498] Photography and food recognition

[1499] 3. The user takes a photo of the refrigerator and uploads it to the app. For example, the user opens the refrigerator door and takes a photo of the inside.

[1500] 4. The device uses image analysis technology to analyze the uploaded photos and generate a list of ingredients in the refrigerator.

[1501] Input of user's mood, desires, and emotions

[1502] 5. The user inputs their mood and food cravings by voice or text. For example, they might say, "I want something light today."

[1503] 6. The device converts the voice input into text (in the case of voice input) and analyzes it using natural language processing technology. The analyzed results are sent to the server.

[1504] 7. The emotion engine analyzes the user's input and obtains emotion information. For example, if the user is using a facial recognition camera, it will determine the emotion from their facial expressions.

[1505] 8. The device also sends the analyzed emotional information to the server.

[1506] Menu and recipe generation

[1507] 9. The server generates the optimal menu and recipe based on the information about ingredients in the refrigerator, the user's input, and emotional information. For example, it decides on a menu of "tomato and cucumber salad, lightly grilled chicken, and egg soup."

[1508] 10. The server sends the generated menu and recipe information to the terminal, which displays it to the user.

[1509] Complementing missing ingredients

[1510] 11. The user reviews the suggested menu and recipes and, if there are any missing ingredients, selects the "Automatically purchase missing ingredients" option.

[1511] 12. The device sends a list of ingredients that are missing to the server.

[1512] 13. The server connects to the distribution system and processes the necessary ingredients for purchase online. For example, the customer orders the necessary mirin from an online store.

[1513] Notification and Delivery

[1514] 14. The server returns the order details and delivery information to the terminal, and the terminal notifies the user of the order completion and delivery information. For example, it notifies the user that "Mirin will be delivered tomorrow."

[1515] Specific examples

[1516] 1. The user downloads the app and enters the required initial information.

[1517] 2. The user uploads a photo of the inside of their refrigerator to the app, and image analysis technology is used to create a list of "chicken, spinach, carrots, and onions."

[1518] 3. The user inputs "I want to eat Japanese food today" through voice input, which is then analyzed using natural language processing technology.

[1519] 4. The emotion engine obtains emotional information from the user's input and facial expressions and determines whether the user is in a "relaxed mood."

[1520] 5. The device sends this information to the server, which then generates a recipe for "Teriyaki chicken, boiled spinach, and miso soup with carrots and onions," which the device then displays.

[1521] 6. The user checks the proposed menu and, if there are any missing condiments (e.g., mirin), selects "Buy automatically."

[1522] 7. The server connects to the online purchasing system to order mirin and process the delivery.

[1523] 8. The terminal notifies the user that the mirin order has been completed and the estimated delivery date.

[1524] In this way, the system can effectively utilize the ingredients in the user's refrigerator, easily provide a menu that matches the user's mood or emotions that day, and enable the user to purchase the necessary ingredients online.

[1525] The processing flow will be explained below.

[1526] Step 1:

[1527] The user downloads the application and enters their name, address, email address, favorite ingredients, and allergy information when they first launch it.

[1528] Step 2:

[1529] The terminal transmits this user information to the server.

[1530] Step 3:

[1531] The server creates a user profile based on the received user information and stores it in a database.

[1532] Step 4:

[1533] The user takes a photo of the inside of the refrigerator and uploads it to the app. For example, the user opens the refrigerator door and takes a photo of the inside.

[1534] Step 5:

[1535] The device receives the uploaded photo, analyzes it using image analysis technology (such as OCR), and identifies the ingredients in the refrigerator to generate a list. For example, it recognizes "tomatoes, cucumbers, eggs, and chicken" from the photo.

[1536] Step 6:

[1537] The terminal transmits the analyzed ingredient list to the server.

[1538] Step 7:

[1539] The server updates the user profile based on the received ingredient list.

[1540] Step 8:

[1541] The user inputs their mood and what they want to eat that day by voice or text. For example, they might say, "I want to eat something light today."

[1542] Step 9:

[1543] The device converts the voice input into text (if voice input is used) and analyzes it using natural language processing technology. The analyzed information is then sent to the server.

[1544] Step 10:

[1545] The emotion engine infers the user's emotions based on the user's input, for example, by using a facial recognition camera to determine if the user wants to relax.

[1546] Step 11:

[1547] The terminal also transmits emotion information acquired by the emotion engine to the server.

[1548] Step 12:

[1549] The server generates optimal menus and recipes based on information about ingredients in the refrigerator, user input, and emotional information. For example, it suggests a menu of "tomato and cucumber salad, lightly grilled chicken, and egg soup."

[1550] Step 13:

[1551] The server sends the generated menu and recipe information to the terminal, which then displays it to the user. For example, the menu and recipes for each dish are displayed.

[1552] Step 14:

[1553] The user checks the suggested menu and recipes, and if there are any missing ingredients, selects the "Automatically purchase missing ingredients" option.

[1554] Step 15:

[1555] The terminal sends a list of ingredients that are missing to the server.

[1556] Step 16:

[1557] The server connects to the online purchasing system and processes the necessary ingredients. For example, the server orders the necessary mirin from the online store.

[1558] Step 17:

[1559] The server returns the order details and delivery information to the terminal. The terminal notifies the user of the order completion and delivery information. For example, it notifies the user that "Mirin will be delivered tomorrow."

[1560] This series of processes allows users to efficiently use the ingredients in their refrigerator, easily plan a menu that matches their mood or emotions for the day, and purchase the necessary ingredients online.

[1561] Example 2

[1562] 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."

[1563] In modern society, busy lives mean limited time for planning menus, making it difficult to propose meals that take into account the user's mood, health status, individual preferences, and allergies. Another problem is the lack of systems that can effectively utilize ingredients in the refrigerator while automatically purchasing ingredients that are in short supply. To solve this situation, a system is needed that proposes optimal menus based on the ingredients in the user's refrigerator and their mood and emotions for the day, and automatically purchases the necessary ingredients online.

[1564] 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.

[1565] In this invention, the server includes means for acquiring information about ingredients in the refrigerator stored in a storage device through image analysis, means for analyzing natural language input from the user to interpret the user's mood and food preferences for that day, means for generating optimal menus and recipes based on the information about ingredients in the refrigerator and the user's input and emotional information, means for displaying the generated menus and recipes on a user terminal, means for purchasing ingredients that are in short supply by connecting to an online purchasing system, and means for acquiring emotional information by recognizing the user's facial expression. This makes the user's life more comfortable and convenient, allowing them to easily decide on daily menus and automatically replenish necessary ingredients.

[1566] "Storage device" refers to hardware or software for storing data, and specifically includes hard disk drives and solid-state drives.

[1567] "Image analysis" is a technology that uses photographs or image data to recognize specific objects or extract specific information.

[1568] "Natural language input" is a method for allowing a system to understand and analyze the content of input in the language (voice or text) that a user uses on a daily basis.

[1569] "Menu" refers to the combination and order of dishes served at a meal.

[1570] A "recipe" is a set of instructions that outlines the steps and ingredient combinations required to prepare a particular dish.

[1571] A "user terminal" is a device that is directly operated by a user, and specifically refers to electronic devices such as smartphones and tablets.

[1572] An "online purchasing system" is a system for purchasing products via the Internet, and specifically includes online stores and e-commerce platforms.

[1573] "User profile information" is a collection of individual pieces of information related to a user, including name, address, email address, favorite ingredients, allergy information, and the like.

[1574] "Speech recognition technology" is a technology that analyzes input voice data and converts it into text data.

[1575] "Natural language processing technology" is a technology that allows computers to understand and analyze natural human language, and specifically includes semantic analysis and context understanding.

[1576] "Emotion information" is information relating to the user's mood or emotional state, and specifically is data indicating an emotional state such as "I want to relax" or "I feel stressed."

[1577] This invention is a system that proposes optimal menus and recipes based on information about ingredients in the refrigerator, taking into consideration the user's mood, what they want to eat, and even their emotions, and also allows them to purchase ingredients they are running low on online.The system is mainly composed of a server, terminals, users, and an emotion engine.

[1578] Registration and Initial Setup

[1579] A user downloads and installs the application onto a device such as a smartphone or tablet. When the application is launched for the first time, the user enters their name, address, email address, favorite foods, allergy information, etc. The device sends this information to the server, which then creates a user profile based on the received information and stores it in a database.

[1580] Photography and food recognition

[1581] The user takes a photo of the inside of the refrigerator and uploads it to the application. The device analyzes the photo using image analysis technology (e.g., Google Cloud Vision API), creates a list of the ingredients in the refrigerator, and sends it to the server.

[1582] Input of user's mood, desires, and emotions

[1583] The user tells the app how they feel that day and what they want to eat by voice or text input. For example, they might say, "I want something light today." The device uses voice recognition technology (e.g., Google Cloud Speech-to-Text) to convert the speech into text, which is then analyzed using natural language processing technology (e.g., OpenAI GPT-4). The analysis results are sent to the server.

[1584] The emotion engine analyzes emotional information based on the user's input data and data acquired from the facial recognition camera. For example, it analyzes emotional information such as "I feel like relaxing." The device then sends this emotional information to the server.

[1585] Menu and recipe generation

[1586] The server generates the optimal menu and recipes based on information about ingredients in the refrigerator, the user's mood, preferences, and emotional information. For example, it generates a menu such as "tomato and cucumber salad, lightly grilled chicken, and egg soup." The server sends the generated menu and recipe information to the terminal, which then displays the information to the user.

[1587] Complementing missing ingredients

[1588] The user checks the provided menu and recipe, and if there are any missing ingredients, selects the "Automatically purchase missing ingredients" option. The device sends a list of the missing ingredients to the server. The server then connects to the online purchasing system and carries out the procedure to purchase the missing ingredients online. For example, the user orders the required "mirin" from an online store.

[1589] Notification and Delivery

[1590] The server returns the order details and delivery information to the terminal, and the terminal notifies the user of the order completion and delivery information. For example, it notifies the user that "Mirin will be delivered the next day."

[1591] Examples of specific examples and prompts

[1592] Specific examples

[1593] The user downloads the app and enters initial information such as name, address, email address, favorite ingredients, and allergy information.

[1594] Users take a photo of the inside of their refrigerator and upload it to the app, which uses image analysis technology to analyze the photo and produce a list of "chicken, spinach, carrots, and onions."

[1595] The user inputs "I want to eat Japanese food today" by voice, and the device converts this into text using voice recognition technology, which is then analyzed using natural language processing technology.

[1596] The emotion engine determines whether you feel like relaxing based on input and facial expression data.

[1597] The device sends information about ingredients, mood, and emotions to the server, and the server generates a recipe for "teriyaki chicken, boiled spinach, and miso soup with carrots and onions," which is then displayed on the device.

[1598] The user reviews the suggested menu and selects the option to automatically purchase any mirin they are running low on.

[1599] The server connects to the online purchasing system to order mirin and process the delivery.

[1600] The terminal notifies the user of the order completion and estimated delivery date.

[1601] Prompt Sentence Examples

[1602] "Please upload a photo of the inside of your refrigerator and list the ingredients in it."

[1603] "I want to eat Japanese food today. I'd like you to suggest a relaxing menu."

[1604] "Show me recipes for chicken teriyaki, spinach ohitashi, and carrot and onion miso soup."

[1605] "Automatically purchase mirin online if I'm running low on it."

[1606] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1607] System processing steps

[1608] Step 1: Enter your user information and create your profile

[1609] The user downloads and installs the application.

[1610] Input: Name, address, email address, favorite ingredients, allergy information.

[1611] Specific operations: Launch the app and enter the required information on the initial setup screen.

[1612] The terminal transmits the input information to the server.

[1613] Output: The user profile is saved on the server.

[1614] Step 2: Take a photo of the inside of your refrigerator and upload it

[1615] A user opens the refrigerator door and takes a photo of the inside.

[1616] Input: A photo of the inside of a refrigerator.

[1617] Specific operation: Take a photo of the inside of the refrigerator using your smartphone camera and upload it to the app.

[1618] The device analyzes the uploaded photos using image analysis technology (e.g., Google Cloud Vision API).

[1619] Output: A list of ingredients in the refrigerator is generated.

[1620] Step 3: Submit and save ingredient information

[1621] The terminal transmits the generated ingredient list to the server.

[1622] Input: Ingredient list.

[1623] Specific operation: The ingredient list obtained through image analysis technology is sent to the server.

[1624] The server stores the ingredient information in a database.

[1625] Output: Ingredient information is saved in the database.

[1626] Step 4: Enter the user's mood and wishes

[1627] The user inputs their mood and what they want to eat that day using voice or text.

[1628] Input: Voice or text to indicate your mood or what you want to eat that day.

[1629] What it does: Enter your desired eating style using the app's input field or voice input function.

[1630] The device uses voice recognition technology (e.g., Google Cloud Speech-to-Text) to convert the speech into text, which is then analyzed using natural language processing technology (e.g., OpenAI GPT-4).

[1631] Output: The analyzed mood and preference information is available in text format.

[1632] Step 5: Send and save your mood and preferences

[1633] The device sends the analyzed mood and preference information to the server.

[1634] Input: Mood and preference information in text format.

[1635] Specific operation: Executes a process to send the analysis results to the server.

[1636] The server stores mood and preference information in a database.

[1637] Output: Mood and preference information is saved in a database.

[1638] Step 6: Acquiring and transmitting emotional information

[1639] The emotion engine recognizes the user's facial expressions and analyzes emotional information.

[1640] Input: User's facial expression data.

[1641] Specific operation: Using a facial expression recognition camera, the user's facial expressions are captured and their emotions are analyzed.

[1642] The emotion engine sends the emotion information to the server.

[1643] Output: Emotion information is saved in text format on the server.

[1644] Step 7: Create menus and recipes

[1645] The server combines information about ingredients in the refrigerator with the user's mood, preferences, and emotional information to generate optimal menus and recipes.

[1646] Input: Ingredient information, mood and preference information, emotional information.

[1647] Specific operation: Extracts information on ingredients, mood, preferences, and emotions from the database, and analyzes them comprehensively to generate appropriate menus and recipes.

[1648] Output: Generated menu and recipe information.

[1649] Step 8: Send and display menu and recipe information

[1650] The server sends the generated menu and recipe information to the terminal.

[1651] Input: Menu and recipe information.

[1652] Specific operation: The generated menu and recipes are sent to the user's terminal and presented to the user through the display function.

[1653] Output: Menu and recipes displayed on the terminal.

[1654] Step 9: Select the option to automatically purchase ingredients when they run out

[1655] The user checks the menu and, if there are any missing ingredients, selects the "Automatically purchase missing ingredients" option.

[1656] Input: User's choice.

[1657] What it does: Check menus and recipes, then tap to select the option you want.

[1658] The terminal sends a list of ingredients that are missing to the server.

[1659] Output: List of missing ingredients.

[1660] Step 10: Purchase missing ingredients online

[1661] The server connects to the online purchasing system and carries out the procedure to purchase the necessary ingredients.

[1662] Input: List of missing ingredients.

[1663] Specific operation: Use the API of the online purchasing system to automatically proceed with the purchase procedure and start the process to order the specified ingredients.

[1664] Output: Order completed notification.

[1665] Step 11: Order and Shipping Information Notification

[1666] The server returns the order details and delivery information to the terminal.

[1667] Input: Order details and shipping information.

[1668] Specific operation: After the order is completed, the contents and estimated delivery date are notified to the user's terminal.

[1669] The terminal notifies the user of the order completion and delivery information.

[1670] Output: The user receives a notification of order completion and shipping information.

[1671] The above are the specific processing steps of this system.

[1672] (Application example 2)

[1673] 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."

[1674] While conventional food management systems have the ability to suggest recipes to users based on information about ingredients in the refrigerator, many lack the ability to link with user emotions and store inventory information, making it impossible to suggest recipes that best suit the user's current mood or emotions. Furthermore, they lack the ability to identify the location of ingredients that are missing in the store, which can make the user's shopping experience more complicated.

[1675] The identification process by the identification 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 means for acquiring ingredient information stored in a storage device through image analysis, means for analyzing natural language input from the user to interpret the user's mood and food preferences for that day, means for analyzing the user's emotional information and optimizing menus and recipes based on the analysis, means for linking with in-store inventory information to provide information on the location of missing ingredients, means for displaying the generated menus and recipes on the user's terminal, and means for linking with an online purchasing system to purchase missing ingredients. This enables the server to suggest optimal recipes tailored to the user's mood and emotions and provide a smooth shopping experience in the store.

[1676] A "storage device" is hardware or software for storing data and information.

[1677] "Food information" refers to data such as the type, quantity, and freshness of food and ingredients in the refrigerator or store.

[1678] "Image analysis" is a technique for analyzing image data and extracting useful information from it.

[1679] "Natural language input" refers to the input of instructions or questions by a user using everyday language, either by voice or text.

[1680] "User input" means data provided to the system by a user through natural language input or other means.

[1681] "Menu" refers to the entire meal menu for that day.

[1682] A "recipe" is a detailed set of instructions and ingredients for making a particular dish.

[1683] "Emotional information" is data that indicates the user's emotional state and is obtained from facial expression analysis, voice tone, etc.

[1684] A "user terminal" is a device that is directly operated by a user, such as a smartphone, smart glasses, or a head-mounted display.

[1685] An "online purchasing system" is a platform for purchasing products via the Internet.

[1686] "Store inventory information" is data on products and ingredients currently available in the store.

[1687] "Guidance means" refers to a method or system that shows the user the location of a destination or product.

[1688] This invention is an in-store ingredient management and recommendation system that proposes optimal menus and recipes based on the user's mood and emotions, and also supports the purchase of ingredients that are in short supply. The system is mainly composed of a storage device, a user terminal, a server, an emotion engine, and an in-store inventory information system.

[1689] Hardware and software used

[1690] Hardware:

[1691] Smartphones, smart glasses, head-mounted displays (user devices)

[1692] Camera (for facial expression analysis)

[1693] software:

[1694] Image analysis libraries (e.g., Firebase ML Kit)

[1695] Natural language processing engine (e.g. Google Cloud Natural Language API)

[1696] Sentiment analysis engine (e.g. Affectiva SDK)

[1697] Cloud databases (e.g. Firebase Firestore)

[1698] Online purchasing system API (e.g. Amazon API)

[1699] Processing flow

[1700] 1. User Profile Management:

[1701] A user installs the app and enters initial information such as their name, address, email address, favorite ingredients, allergy information, etc. This user information is stored in a cloud database (Firebase Firestore).

[1702] 2. Food recognition:

[1703] The user takes a photo of the food shelves in the store using smart glasses or a smartphone. The image data is analyzed using an image analysis library (Firebase ML Kit) to obtain food information.

[1704] 3. Parsing user-entered information:

[1705] The user inputs something like "I want to eat something light today" via voice or text. This input is converted into text using speech recognition technology, and then analyzed by a natural language processing engine (Google Cloud Natural Language API).

[1706] 4. Acquiring emotional information:

[1707] The user's facial expressions are captured by the camera in the smart glasses or head-mounted display and analyzed by the emotion analysis engine (Affectiva SDK), thereby obtaining the user's emotional information.

[1708] 5. Menu and recipe generation:

[1709] The server generates optimal menus and recipes based on the ingredient information stored in the cloud database, the user's input information, and emotional information. This information is displayed on the user's device.

[1710] 6. Purchase and guidance of missing ingredients:

[1711] A list of missing ingredients is generated based on the recipe. This is checked against the store's inventory information, and if any are missing, they are ordered and purchased through the online purchasing system (Amazon API). If the missing ingredients are available in the store, the user is informed of their location.

[1712] Specific examples

[1713] 1. While walking around the store, the user scans the shelves with smart glasses and then voice-inputs, "I'd like to eat a light Japanese meal today."

[1714] 2. The system recognizes "salmon, tofu, and green onions" from an image of a product shelf, and, combined with the user information, suggests "grilled salmon with salt and tofu miso soup."

[1715] 3. The app will notify you when you are low on soy sauce and use your location information in the store to guide you to the soy sauce section.

[1716] Prompt Sentence Examples

[1717] You're using an "intelligent shopping assistant" app. It analyzes photos of shelves you take in a store and suggests recipes based on ingredients that fit your current mood. You enter, "I want something light today."

[1718] This will enable the system to suggest optimal recipes that match the user's mood and emotions, as well as provide a smooth shopping experience in stores.

[1719] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1720] Step 1:

[1721] Users install the application and enter their name, address, email address, favorite foods, and allergy information. This initial information is stored in a cloud database. The user's personal information and preferences are used as input data to create a user profile. The server uses this data to build an individual user profile.

[1722] Step 2:

[1723] The user takes a photo of the food shelves in the store using smart glasses or a smartphone and uploads it to the app. The image data is analyzed using an image analysis library (e.g., Firebase ML Kit) to extract information about the ingredients. The input is the captured image data, and the output is a list of analyzed ingredients. The device displays this list and also sends it to the server.

[1724] Step 3:

[1725] The user inputs a voice or text message such as "I want something light today." In the case of voice input, the message is converted into text using speech recognition technology and analyzed by a natural language processing engine (e.g., Google Cloud Natural Language API). The input data is voice or text, and the output is text data that reflects the analyzed user's mood and preferences. The server uses the results of this analysis to generate menu suggestions.

[1726] Step 4:

[1727] The user's facial expressions are captured by a camera in smart glasses or a head-mounted display and analyzed by an emotion analysis engine (e.g., Affectiva SDK). The captured image or video data is used as input data, and the output is the user's emotional information. This emotional information is sent to a server and analyzed together with the user's mood and wishes.

[1728] Step 5:

[1729] The server generates optimal menus and recipes based on ingredient information stored in the cloud database, user input information, and emotional information. The input data is the ingredient list, the user's preferences and mood information, and emotional information, and the output is optimized menus and recipes. The generated information is sent to the user's device and displayed to the user.

[1730] Step 6:

[1731] The terminal displays the generated menu and recipes to the user, and also presents a list of missing ingredients. The input data are the generated menu and recipes, and the output is the list of missing ingredients.

[1732] Step 7:

[1733] The server checks the store's inventory information and, if there are shortages, orders and purchases ingredients through an online purchasing system (e.g., Amazon API). If the missing ingredients are available in the store, the server guides the user to their location. The input data is inventory information and a list of ingredients that are in short supply, and the output is purchase completion information and in-store guidance information. The terminal notifies the user of this.

[1734] In this way, a system is formed that inputs, analyzes, and outputs data at each step to provide the user with the optimal experience.

[1735] 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.

[1736] 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.

[1737] 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.

[1738] 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.

[1739] 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.

[1740] 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.

[1741] 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).

[1742] 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.

[1743] 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."

[1744] 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.

[1745] 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).

[1746] 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.

[1747] 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.

[1748] 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.

[1749] 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.

[1750] 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.

[1751] 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.

[1752] 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.

[1753] 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.

[1754] 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.

[1755] 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.

[1756] The following is further disclosed regarding the above embodiment.

[1757] (Claim 1)

[1758] A means for acquiring information about ingredients in the refrigerator stored in a storage device by image analysis;

[1759] A means of analyzing natural language input from users to interpret their mood and what they want to eat that day,

[1760] A means for generating optimal menus and recipes based on information about ingredients in the refrigerator and information input by the user;

[1761] a means for displaying the generated menu and recipes on a user terminal;

[1762] Linking with the online purchasing system to purchase the ingredients needed for shortages,

[1763] A system including:

[1764] (Claim 2)

[1765] The system further includes a means for saving user profile information in a storage device and proposing menus and recipes based on the information and taking into consideration the user's preferences and allergy information.

[1766] 10. The system of claim 1.

[1767] (Claim 3)

[1768] The system further includes a means for converting the user's speech into text using a speech recognition technology and analyzing the text using a natural language processing technology.

[1769] 10. The system of claim 1.

[1770] (Claim 4)

[1771] The system further includes a means for notifying a destination user of the generated menu and recipe information.

[1772] 10. The system of claim 1.

[1773] "Example 1"

[1774] (Claim 1)

[1775] A means for acquiring information about ingredients in the refrigerator stored in a storage device by image analysis;

[1776] A means of analyzing natural language input from users to interpret their mood and what they want to eat that day,

[1777] A means for generating optimal menus and recipes based on information about ingredients in the refrigerator and information input by the user;

[1778] a means for displaying the generated menu and recipes on a user terminal;

[1779] Linking with the online purchasing system to purchase the ingredients needed for shortages,

[1780] means for creating a user profile based on initial information input by the user and storing the profile in a storage device;

[1781] a means for a user to take a photo of the refrigerator and upload it to the application;

[1782] A means for converting voice input into text and transmitting the analysis results to a server;

[1783] A system including:

[1784] (Claim 2)

[1785] The system further includes a means for saving user profile information in a storage device and proposing menus and recipes based on the information and taking into consideration the user's preferences and allergy information.

[1786] 10. The system of claim 1.

[1787] (Claim 3)

[1788] The system further includes a means for converting the user's speech into text using a speech recognition technology and analyzing the text using a natural language processing technology.

[1789] 10. The system of claim 1.

[1790] "Application Example 1"

[1791] (Claim 1)

[1792] A means for acquiring information about ingredients in the refrigerator stored in a storage device by image analysis;

[1793] A means of analyzing natural language input from users to interpret their mood and what they want to eat that day,

[1794] A means for generating optimal menus and recipes based on information about ingredients in the refrigerator and information input by the user;

[1795] a means for displaying the generated menu and recipes on a user terminal;

[1796] Linking with the online purchasing system to purchase the ingredients needed for shortages,

[1797] A means to link the generated menu and recipe information with food sales systems and distribution systems using natural language processing,

[1798] a means for notifying the user of the delivery status in real time;

[1799] A system including:

[1800] (Claim 2)

[1801] The system further includes a means for saving user profile information in a storage device and proposing menus and recipes based on the information and taking into consideration the user's preferences and allergy information.

[1802] 10. The system of claim 1.

[1803] (Claim 3)

[1804] The system further includes a means for converting the user's speech into text using a speech recognition technology and analyzing the text using a natural language processing technology.

[1805] 10. The system of claim 1.

[1806] "Example 2: Combining Emotion Engines"

[1807] (Claim 1)

[1808] A means for acquiring information about ingredients in the refrigerator stored in a storage device by image analysis;

[1809] A means of analyzing natural language input from users to interpret their mood and what they want to eat that day,

[1810] A means for generating optimal menus and recipes based on information about ingredients in the refrigerator and information input by the user and emotional information;

[1811] a means for displaying the generated menu and recipes on a user terminal;

[1812] Linking with the online purchasing system to purchase the ingredients needed for shortages,

[1813] means for recognizing a user's facial expression and acquiring emotional information;

[1814] A system including:

[1815] (Claim 2)

[1816] 2. The system according to claim 1, further comprising means for storing user profile information in a storage device and for suggesting menus and recipes based on the information and taking into consideration the user's preferences and allergy information.

[1817] (Claim 3)

[1818] 10. The system of claim 1, further comprising means for converting a user's speech into text using speech recognition techniques and analyzing the text using natural language processing techniques.

[1819] "Application example 2 when combining emotion engines"

[1820] (Claim 1)

[1821] A means for acquiring ingredient information stored in a storage device by image analysis;

[1822] A means of analyzing natural language input from users to interpret their mood and what they want to eat that day,

[1823] A means for generating optimal menus and recipes based on ingredient information and user input information;

[1824] A method for analyzing user emotional information and optimizing menus and recipes based on that information;

[1825] a means for displaying the generated menu and recipes on a user terminal;

[1826] Linking with the online purchasing system to purchase the ingredients needed for shortages,

[1827] Linking with in-store inventory information to provide guidance on where ingredients are in short supply,

[1828] A system including:

[1829] (Claim 2)

[1830] The system further includes a means for saving user profile information in a storage device and proposing menus and recipes based on the information and taking into consideration the user's preferences and allergy information.

[1831] 10. The system of claim 1.

[1832] (Claim 3)

[1833] The system further includes a means for converting the user's speech into text using a speech recognition technology and analyzing the text using a natural language processing technology.

[1834] 10. The system of claim 1. [Explanation of symbols]

[1835] 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. A means for acquiring information about ingredients in the refrigerator stored in a storage device by image analysis; A means of analyzing natural language input from users to interpret their mood and what they want to eat that day, A means for generating optimal menus and recipes based on information about ingredients in the refrigerator and information input by the user; a means for displaying the generated menu and recipes on a user terminal; Linking with the online purchasing system to purchase the ingredients needed for shortages, A system including:

2. The system further includes a means for saving user profile information in a storage device and proposing menus and recipes based on the information and taking into consideration the user's preferences and allergy information. The system of claim 1 .

3. The system further includes a means for converting the user's speech into text using a speech recognition technology and analyzing the text using a natural language processing technology. The system of claim 1 .

4. The system further includes a means for notifying a destination user of the generated menu and recipe information. The system of claim 1 .

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