system

Abstract

We provide the system. [Solution] A means for providing a user interface for acquiring digital images, A means for generating a specific dish name using a machine learning model to recognize a dish from the digital image, A means for generating a corresponding cooking procedure and ingredient list based on the name of the specific dish, A means of providing access to a delivery service to obtain the necessary materials, A means for providing video content that visually demonstrates the cooking procedure, A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] While the popularity of Japanese cuisine is increasing globally, there is a problem that materials and recipe information necessary for authentic cooking of Japanese cuisine are difficult to obtain overseas. For this reason, consumers who want to cook Japanese cuisine in their own countries face many hurdles in the process. Technical means for solving this problem and making the cooking of Japanese cuisine more accessible are required.

Means for Solving the Problems

[0005] This invention provides a system that uses image recognition technology and generative AI to analyze images of Japanese food taken by users and identify the corresponding dishes. This system generates cooking instructions and a list of necessary ingredients based on the analysis results, and also includes a means to easily obtain ingredients in conjunction with delivery services. Furthermore, by providing users with video content demonstrating cooking methods, it lowers linguistic and physical barriers, enabling them to easily cook Japanese food in their own countries.

[0006] "Digital image" refers to a format of image file containing visual information generated or acquired by a user using their device.

[0007] "User interface" refers to the screens and interaction methods that users use to operate a system and input or retrieve information.

[0008] A "machine learning model" refers to a set of algorithms that learn specific patterns from large amounts of data and generate outputs based on given inputs.

[0009] "Recognizing food" refers to the process of analyzing the contents of a meal contained within a digital image and classifying it into a specific food name or category.

[0010] "Cooking instructions" refer to procedural information that outlines the series of operations and steps necessary to complete a dish.

[0011] An "ingredients list" refers to a list that specifies the names and quantities of ingredients needed to prepare a particular dish.

[0012] "Delivery service" refers to a service provider that delivers goods to a specified address.

[0013] "Video content" refers to dynamic media files that are played to convey information visually and aurally. [Brief explanation of the drawing]

[0014] [Figure 1] It is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

Embodiments for Carrying Out the Invention

[0015] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0016] First, the terms used in the following description will be explained.

[0017] In the following embodiments, a processor with a reference numeral (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0018] In the following embodiments, a RAM (Random Access Memory) with a reference numeral is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0019] In the following embodiments, a storage with a reference numeral is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[0020] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0021] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0022] [First Embodiment]

[0023] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0024] As shown in Figure 1, the 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.

[0025] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0026] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0027] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and 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.

[0028] 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 perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0029] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0030] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0031] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 according to the specific processing program 56 executed on the RAM 30.

[0032] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0033] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0034] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0035] This invention provides a system that makes it easy to cook Japanese food in one's own country. Specifically, it enables a process in which a user takes a picture of a particular Japanese dish, and this image is used to automatically retrieve recipes, ingredient lists, and cooking videos, and further enables the necessary ingredients to be obtained through a delivery service.

[0036] Users use their devices to take photos of Japanese food they have eaten at restaurants in the past or made at home. These images are uploaded to the system via their devices.

[0037] The server uses a machine learning model to analyze the received images. This process allows the server to identify the type of Japanese food in question. The server then extracts recipes for the identified Japanese food from its database and generates cooking instructions and ingredient lists to provide to the user.

[0038] Furthermore, based on the generated ingredient list, the server integrates with delivery service platforms to provide users with the option to purchase the necessary ingredients with just a click. The server also provides users with relevant cooking videos to help them understand the cooking process.

[0039] As a concrete example, let's consider a scenario where a user uploads a photo of "tempura" to the system. In this case, the server performs image analysis and recognizes that the dish is "tempura." Next, the server retrieves a basic tempura recipe and generates cooking instructions detailing each step. It also displays a list of ingredients and shows a function to purchase the necessary ingredients for making "tempura" through a delivery service. Furthermore, the server provides helpful cooking videos to assist users making tempura at home, making it easy to understand even for users with little cooking experience.

[0040] In this form, the present invention expands access to Japanese food preparation methods to users worldwide and enables the sharing of its cultural value.

[0041] The following describes the processing flow.

[0042] Step 1:

[0043] Users select photos of Japanese food on their devices and submit them through the system's upload interface. The device then sends the selected images to the server as digital data.

[0044] Step 2:

[0045] The server temporarily stores the received image and passes it to the image analysis module. The server uses machine learning algorithms to analyze the elements within the image and generates data to identify the dish.

[0046] Step 3:

[0047] The server uses the dish name identified through image analysis to retrieve corresponding recipe information from its internal database. This information includes the necessary ingredients and detailed cooking instructions.

[0048] Step 4:

[0049] The server formats the retrieved recipe information and converts it into a format suitable for the user's terminal. The terminal then displays the cooking instructions and ingredient list in the user interface.

[0050] Step 5:

[0051] Users can view the displayed list of ingredients and select a link or button on their device to procure the necessary ingredients using a delivery service. The server utilizes API integration with delivery websites to create an environment where users can order the necessary ingredients.

[0052] Step 6:

[0053] The server provides video content demonstrating relevant cooking methods to help users understand the cooking process. The terminal displays video links to the user, allowing them to watch them as needed.

[0054] This series of steps allows users to easily obtain information and ingredients for efficiently preparing Japanese food.

[0055] (Example 1)

[0056] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0057] The aim is to provide users who lack knowledge of cooking foreign cuisines, especially Japanese food, with a means to easily and effectively recreate Japanese food while in their own country. Specifically, it aims to resolve challenges in identifying dishes, understanding cooking procedures, and sourcing ingredients.

[0058] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0059] In this invention, the server includes means for providing a user interface for capturing images and converting them into data, means for analyzing the acquired images, identifying dishes using a food classification model, and generating names, and means for generating cooking methods and ingredient information based on the identified dishes. This makes it possible for users unfamiliar with the dishes to instantly obtain appropriate information and ingredients based on the captured images and cook Japanese food.

[0060] A "user interface" is a means or method for a user to interact with a system, and is a mechanism that enables data input and manipulation.

[0061] "Analyzing an image" is the process of analyzing acquired image data using computer algorithms to extract specific information.

[0062] A "food classification model" is a model that uses machine learning algorithms to identify the type of food from image data.

[0063] "Cooking method" refers to information that describes the steps and processes involved in completing a dish using ingredients.

[0064] "Ingredient information" refers to data that shows a list of ingredients and their details, necessary for making a specific dish.

[0065] "Access linked to online services" refers to a means of connecting to necessary services and information via the internet.

[0066] "Visual information" refers to video content provided as visual reference material, which visualizes specific process procedures or knowledge.

[0067] This invention is a system that allows users to easily prepare their desired Japanese food. This system operates through the cooperation of three parties: the user, the terminal, and the server.

[0068] The user first takes a picture of Japanese food using their own device. This device has an interface for uploading the captured image as digital data to the system. The device is able to access the server via the network.

[0069] The server uses a generative AI model to analyze received images. This model is trained to extract culinary features from images and identify specific dish names. The server is equipped with a graphics processing unit (GPU) to enable efficient image processing. Through this analysis, the server accurately identifies the type of dish.

[0070] Next, the server generates cooking instructions and an ingredient list from the database based on the identified dish. In this process, the server uses SQL queries to extract the necessary information from the database and provides it to the user as structured data. The server also integrates with an online shopping platform and provides links to easily purchase ingredients based on the generated ingredient list. The interface provided to the user allows them to order ingredients with just a click.

[0071] Furthermore, the server searches for relevant video information and streams it to the user's device to help them understand the cooking instructions provided. This video information is provided either through the server's own storage or an external video service.

[0072] For example, if a user uploads a photo of tempura, the server analyzes the image and recognizes that the dish is tempura. Next, the server generates a basic cooking method and ingredient list for tempura and provides it to the user. Furthermore, the server provides links to purchase the necessary ingredients online and makes video information about the cooking procedure playable on the user's device. Through this process, users can easily make delicious tempura at home.

[0073] An example of a prompt message would be, "Upload a picture of the tempura you took, and tell me the cooking method and the necessary ingredients." In this way, the system supports the preparation of Japanese food and makes different cultural cuisines more accessible.

[0074] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0075] Step 1:

[0076] The user takes a picture of Japanese food using the device's camera. This image is saved as digital data, and the user can upload it to the system through the program's interface. The input is the captured image data, which is sent to the server for analysis in the next step.

[0077] Step 2:

[0078] The server receives image data sent from the terminal and begins analyzing the image using a generative AI model. This analysis applies a pre-trained food classification model to extract the characteristics of the dishes and identify specific dish names. The input is image data, and the output is the identified dish name. The server then uses this dish name for subsequent database searches.

[0079] Step 3:

[0080] The server consults a database to retrieve cooking instructions and ingredient information based on the identified dish name. Specifically, it uses SQL queries to extract relevant recipes and ingredient lists. The input is the identified dish name, and the output is the cooking procedure and ingredient list for that dish. This information is processed and provided in a format that is easy for the user to understand.

[0081] Step 4:

[0082] The server interacts with the API of an online shopping platform based on the generated material list, setting up a system that allows users to easily purchase the necessary materials. The input is a material list, and the output is links or interfaces for purchasing the materials. Users can purchase the materials by clicking on these links.

[0083] Step 5:

[0084] The server searches for relevant video information to visually support the cooking procedure and configures it to stream to the user's device. The input is cooking procedure information, and the output is a cooking video that the user can watch. The user can cook while watching this video. This process makes it possible for even users with little cooking experience to easily complete a dish.

[0085] (Application Example 1)

[0086] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0087] The goal is to provide methods that enable people around the world to easily prepare Japanese food, particularly by utilizing image recognition technology to quickly obtain necessary recipes and ingredients, and to facilitate the procurement of those ingredients. Furthermore, it aims to provide methods that help even those with little cooking experience understand the process visually.

[0088] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0089] In this invention, the server includes means for providing human-computer interaction for acquiring digital media, means for analyzing the digital media and using a computational model to identify dishes and generate specific meal names, and means for generating corresponding cooking methods and item lists based on the specific meal names. This enables the rapid acquisition of Japanese cooking methods and necessary ingredients from images, easy online purchase of ingredients, and visual cooking assistance.

[0090] "Digital media" refers to electronically recorded information, including images, audio, and video.

[0091] "Human-computer interaction" refers to the interface through which a user exchanges information with a computer system, and includes operations and inputs made through the user interface.

[0092] A "computational model" is a set of algorithms used to recognize specific patterns or information from data, and is primarily used in machine learning and artificial intelligence.

[0093] A "meal name" is a name used to identify a dish, and is the name of a specific dish used in recipes and cooking methods.

[0094] "Cooking method" refers to the procedures and techniques for preparing a meal using specific ingredients.

[0095] A "list of items" is a list of things needed to achieve a specific purpose, and refers to a list of materials and tools.

[0096] "Understanding through visual means" refers to methods of learning and understanding that use visual information such as videos and images, primarily involving the use of educational videos and diagrams.

[0097] This invention provides a system to assist in the preparation of Japanese food. When a user takes a picture of a specific Japanese dish, the terminal uploads the digital media to a server. The server analyzes the image using a computational model and identifies the dish. Based on the identified specific meal name, the server generates a corresponding cooking method and ingredient list, which it provides to the user.

[0098] The system integrates with e-commerce sites such as Amazon API and Rakuten API, providing a means to easily purchase necessary ingredients online based on the generated item list. It also uses the YouTube® API to search for relevant cooking education videos and presents them to the user, providing visual cooking support.

[0099] As a concrete example, when a user takes a picture of "tempura" with their smartphone and uploads it, the server analyzes the image and identifies it as "tempura." Based on the identification result, the server retrieves basic cooking instructions and a list of ingredients from its database and presents them to the user. Furthermore, the user can easily obtain the ingredients for tempura by clicking on a link to purchase them on Amazon. Finally, the system provides the user with a video on "how to make tempura" via the YouTube API, allowing them to cook while watching the video.

[0100] An example of a prompt message is: "What technology stack would be best suited for creating an application that analyzes a photo of sushi, generates a corresponding recipe and ingredient list, and displays a link to purchase it from an online store?"

[0101] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0102] Step 1:

[0103] The user takes a picture of Japanese food using their device. At this point, the input is the digital image taken by the user. The user sends this image from their device to the server. The sent image becomes the output of this step.

[0104] Step 2:

[0105] The server analyzes the received digital image. This input image is fed into a computational model (generative AI model) for image recognition. As part of data processing, the image is pre-processed into the model's input format. As a result of the analysis, a specific meal name is generated, which becomes the output.

[0106] Step 3:

[0107] The server retrieves the corresponding cooking method and ingredient list from the database based on the generated meal name. The input for this step is the identified meal name. Based on this data, a database search is performed to extract the necessary information. The extracted cooking method and ingredient list are the output.

[0108] Step 4:

[0109] The server interacts with e-commerce sites via APIs based on an item list. The input is an item list, and data calculations are performed to convert it into purchasable links. The output is links for purchasing materials.

[0110] Step 5:

[0111] The server uses the YouTube API to search for relevant cooking videos. The input for this step is the name of the meal. Based on this, the API performs a search to retrieve video information. The output is a list of videos provided as search results.

[0112] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0113] This invention provides an interactive system that takes into account the user's emotional state in order to enhance the Japanese food cooking experience. This system not only identifies dishes from images taken by the user and generates recipes and ingredient lists, but also includes an emotion engine that analyzes the user's emotions and optimizes the interaction.

[0114] The user takes a picture of Japanese food using their device and uploads it to the system. At this stage, the device also activates an emotion recognition module that analyzes the user's facial expressions and voice. The emotion recognition module recognizes the user's current emotional state (e.g., excitement, relaxation, anxiety).

[0115] The server performs image analysis to identify the relevant Japanese dish. Simultaneously, it uses an emotion engine to process user emotion data and customize the user interface. For example, if the user expresses anxiety, the cooking instructions may be made more detailed, or the video's tempo may be slowed down to provide more information.

[0116] The server generates cooking recipes, cooking instructions, and ingredient lists, and delivers them through an interface optimized according to the user's emotional state. Ingredient purchases are facilitated through integration with delivery services, making it easy for users to obtain ingredients. Furthermore, cooking video content is also adjusted according to the user's emotional state, providing a relaxed viewing experience.

[0117] As a concrete example, consider a case where a user uploads an image of "ramen." If the user shows some anxiety or hesitation, the server can use an emotion engine to slow down the playback speed of the cooking video or insert more detailed explanations. It can also display an encouraging message such as "We'll help you with your first cooking experience" based on the user's emotions.

[0118] This invention aims to provide a more personalized user experience by going beyond simply offering recipes and enabling a cooking support experience that resonates with the user's emotions.

[0119] The following describes the processing flow.

[0120] Step 1:

[0121] The user takes a picture of Japanese food with their device and uploads the image to the server through the system's application. Along with the photo, the device also transmits the user's facial expressions and voice to an emotion recognition module.

[0122] Step 2:

[0123] The server uses a machine learning model to analyze the received images and detect specific dish names. The server also processes data from an emotion recognition module to identify the user's emotional state.

[0124] Step 3:

[0125] The server retrieves recipe information from the database based on the identified dish name. This information includes a list of ingredients and cooking instructions. The server automatically adjusts the interface display based on the user's emotional state.

[0126] Step 4:

[0127] The terminal displays information received from the server via a customized user interface. If the user expresses anxiety, the terminal displays more detailed cooking instructions and adds encouraging messages.

[0128] Step 5:

[0129] The server prepares cooking videos tailored to the user's emotions and enables playback on the device. For example, if the user expresses anxiety or hesitation, the server adjusts the video playback speed or inserts supplementary explanations.

[0130] Step 6:

[0131] When a user selects a link to purchase ingredients, their device is redirected via the server to the delivery service page. The server automatically prepares a shopping cart containing the relevant ingredients.

[0132] This series of processes allows users to receive information optimized for their emotional state, enabling them to efficiently prepare Japanese food.

[0133] (Example 2)

[0134] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0135] Conventional cooking support systems have been unable to provide an interactive experience that takes into account the user's emotional state, making it difficult to increase user satisfaction. Furthermore, the provision of recipes and assistance with sourcing ingredients is limited, making it difficult to create a personalized service that is convenient for users.

[0136] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0137] In this invention, the server includes means for providing a human-machine interface for acquiring digital images, means for using an artificial intelligence model for recognizing dishes from the digital images and generating specific food names, and means for generating corresponding cooking procedures and ingredient lists based on the specific food names. This makes it possible to provide personalized cooking support that responds to the user's emotional state.

[0138] A "digital image" is visual information expressed in electronic format that can be handled by computers and other digital devices.

[0139] A "human-machine interface" refers to a means of exchanging information between humans and machines, and includes physical or software interfaces that allow users to interact with machines.

[0140] An "artificial intelligence model" refers to an algorithm or system that learns patterns from vast amounts of data and performs a specific task.

[0141] A "food name" is a word or phrase used to refer to cooked or uncooked food.

[0142] "Cooking procedure" refers to a series of operations or actions performed to create a specific dish.

[0143] An "ingredients list" refers to a list of ingredients needed to cook a specific dish.

[0144] "Delivery service" refers to a service that delivers goods or purchased items to a specified location.

[0145] "Video content" is a collection of information that includes dynamic images and sounds, and is provided as a visual medium.

[0146] An "emotion processing engine" refers to a system or technology for recognizing and analyzing a user's emotional state.

[0147] An "interactive interface" is an interface that allows users and systems to exchange information bidirectionally.

[0148] "Information source" refers to the underlying data or database used to obtain or reference specific information.

[0149] This invention is an interactive system designed to enhance the user's cooking experience. It begins with the user taking a picture of a Japanese dish via a terminal and uploading the image. The terminal is equipped with an emotion recognition module, which can analyze the user's emotional state in real time based on their facial expressions and voice.

[0150] Digital images captured by the device are sent to a server. The server is equipped with an advanced artificial intelligence model, which performs image analysis. This model utilizes a generative AI model to identify dishes from the images. Based on the identified dishes, the server generates cooking instructions and a list of ingredients. The server also integrates with delivery services to ensure users can easily obtain the necessary ingredients. Access to delivery services is provided via an online shopping API.

[0151] Furthermore, the server utilizes an emotion processing engine to provide a personalized interface tailored to the user's emotional state. For example, if the user is showing signs of anxiety, the server will display more detailed cooking instructions and adjust the playback speed of the video content. In this way, the server helps optimize the user's cooking experience.

[0152] For example, if a user uploads a picture of ramen and looks a little unsure, the server will provide detailed instructions on how to cook it and play the video in slow motion. Furthermore, it will support the user by displaying a message on the interface that says, "We'll help you with your first cooking experience."

[0153] Examples of prompts include, "Generate a fast-paced cooking video to help the user relax," and "Provide a slow explanation for users who are feeling nervous." These prompts are used as input for the generative AI model to function properly.

[0154] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0155] Step 1:

[0156] The user uses the device to take pictures of Japanese food dishes and uploads them to the system. The input is a high-quality digital image taken with the camera. The output is the transmission of that image file to the system. After this, an emotion recognition module built into the device acquires the user's facial expressions and voice data and analyzes their emotional state in real time. Specifically, the user presses the camera's shutter button, and an interface appears prompting them to upload the image immediately after taking it.

[0157] Step 2:

[0158] Images sent from the terminal are received by the server. The input is an image of a dish uploaded by the user. The server uses a generative AI model to analyze the image and recognize a specific dish. The output is the name of the recognized dish. In this process, the image analysis algorithm analyzes the color, shape, and other visual features to identify a matching dish from an existing database.

[0159] Step 3:

[0160] The server generates the corresponding cooking instructions and ingredient list based on the recognized dish name. The input is the name of the identified dish. The output is the detailed cooking instructions and ingredient list for making that dish. The generated recipe is retrieved from an internal database and formatted in a way that is easiest for the user to understand.

[0161] Step 4:

[0162] The server customizes the interface based on the user's emotional state. The input is emotional data sent from the device. The output is a personalized user interface and video content adjusted as needed. For example, if the user is relaxed, the video tempo is normal, but if they are anxious, more detailed explanations and slow-motion playback are provided. The emotion processing engine makes these adjustments to optimize the user experience.

[0163] Step 5:

[0164] The server provides video content corresponding to cooking instructions and access to delivery services for obtaining necessary ingredients. Inputs are recipe information and ingredient lists. Outputs are links to specific cooking videos and online stores for purchasing ingredients. Delivery services and shopping APIs enable users to quickly purchase ingredients.

[0165] (Application Example 2)

[0166] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0167] Traditional cooking classes and cooking support systems generally provide standardized information, but lack personalized support tailored to individual users' emotions and skill levels. This can make cooking feel difficult, especially for beginners or those feeling anxious. Therefore, there is a need for technologies that improve the cooking experience by taking into account the user's emotional state.

[0168] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0169] In this invention, the server includes means for providing an information input device for acquiring digital images, means for generating a specific food name using an automatic recognition model for recognizing food from the digital images, and means for providing an emotion analysis module for analyzing the user's facial expression data and estimating emotions. This makes it possible to provide customized cooking support tailored to the individual emotional state of the user.

[0170] A "digital image" is a format in which image information is recorded and processed electronically.

[0171] An "information input device" is a device used by users to provide information, and may include cameras and sensors.

[0172] An "automatic recognition model" is an algorithm that uses machine learning to identify specific objects or patterns.

[0173] "Food" refers to ingredients and finished products used in cooking and meals.

[0174] A "specific food name" refers to a unique name assigned to a recognized food item.

[0175] A "delivery service" is a system that provides a service to deliver the materials that users need.

[0176] A "communication channel" refers to a medium or circuit used for sending and receiving data.

[0177] "Visual information" refers to data that includes visual content, and may include videos and still images.

[0178] The "emotion analysis module" is an analysis system for estimating a user's emotional state.

[0179] A "user interface" refers to the means and screen configurations that allow a user and a system to exchange information.

[0180] An "interactive interface" refers to a means of operation or screen that allows a user to exchange information with a system in a two-way manner.

[0181] A "recording device" is a system for storing and making data accessible.

[0182] The system for implementing this invention consists of an interactive system that provides a cooking experience using the user's smart device. The user uploads digital images taken during the cooking process using an information input device such as a smartphone or tablet. These digital images are analyzed by a server, and the food is identified by an automatic recognition model.

[0183] The server generates and provides the user with processing instructions and ingredient lists corresponding to the recognized food. It also provides more specific and user-friendly cooking assistance by offering customized cooking guide videos based on the user's emotional analysis data. The user's facial expressions and voice data are analyzed through an emotional analysis module to estimate their emotional state in real time. Based on this information, the user interface is personalized, displaying appropriate advice and encouraging messages to the user.

[0184] As a concrete example, suppose a user uploads an image of how to prepare "sushi," and the server uses an emotion analysis module to recognize the user's emotional state as relaxed. In this case, the server will omit more detailed explanations than usual and provide a fast-paced cooking video to guide the user at their own pace. Furthermore, to support the purchase of ingredients through a delivery service, a communication channel will be secured to allow for easy acquisition of necessary ingredients.

[0185] In this embodiment, the main software used includes image analysis using Python and OpenCV, sentiment analysis using NVIDIA Maxine, and server-side processing using Django. This enables a personalized cooking assistance service that allows users to improve their cooking skills from the comfort of their homes.

[0186] Examples of prompt messages include the following:

[0187] "Analyze the food images and participants' facial expressions, and provide feedback and next steps that take into account their current emotional state. Image: Uploaded image. Facial expression data: Analyzed facial expression data."

[0188] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0189] Step 1:

[0190] The user takes a picture of the food being cooked using the camera on their smart device. A digital image is obtained as input. The user uploads this image to the server through the application. The output is the image of the food sent to the server.

[0191] Step 2:

[0192] The server inputs the received digital image into an automatic recognition model to identify the food item. The input is a captured digital image. The server performs image analysis and uses the model to generate a specific food name. The output is the recognized food name.

[0193] Step 3:

[0194] The server generates corresponding processing instructions and ingredient lists based on the identified food name. It accepts a food name as input. The server consults a database and extracts the relevant recipe information to provide detailed cooking instructions and ingredient lists as output.

[0195] Step 4:

[0196] The user's smart device uses its camera and microphone to input the user's facial expressions and voice into an emotion analysis module. The input is real-time acquired facial and voice data. The output is the estimated emotional state of the user.

[0197] Step 5:

[0198] The server optimizes the user interface based on the user's emotional state received from the emotion analysis module. It receives the emotional state as input. The server sets the cooking video speed, level of detail in the explanation, and encouraging messages appropriate for the user, and sends the customized interface back to the user's device as output.

[0199] Step 6:

[0200] The server establishes a communication channel to the delivery service to support the purchase of materials. The input is a generated list of materials. The server uses this list to interact with the delivery service and provides the user with options for easily ordering materials. The output is a link to access the materials purchase screen.

[0201] Step 7:

[0202] The user completes the dish by following the provided cooking instructions and watching a customized video guide. The input is cooking instruction information provided by the server. The interface supports the user's actions, and the finished dish is obtained as output.

[0203] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0204] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0205] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0206] [Second Embodiment]

[0207] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0208] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0209] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0210] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0211] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0212] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0213] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0214] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0215] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[0216] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0217] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0218] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. 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".

[0219] This invention provides a system that makes it easy to cook Japanese food in one's own country. Specifically, it enables a process in which a user takes a picture of a particular Japanese dish, and this image is used to automatically retrieve recipes, ingredient lists, and cooking videos, and further enables the necessary ingredients to be obtained through a delivery service.

[0220] Users use their devices to take photos of Japanese food they have eaten at restaurants in the past or made at home. These images are uploaded to the system via their devices.

[0221] The server uses a machine learning model to analyze the received images. This process allows the server to identify the type of Japanese food in question. The server then extracts recipes for the identified Japanese food from its database and generates cooking instructions and ingredient lists to provide to the user.

[0222] Furthermore, based on the generated ingredient list, the server integrates with delivery service platforms to provide users with the option to purchase the necessary ingredients with just a click. The server also provides users with relevant cooking videos to help them understand the cooking process.

[0223] As a concrete example, let's consider a scenario where a user uploads a photo of "tempura" to the system. In this case, the server performs image analysis and recognizes that the dish is "tempura." Next, the server retrieves a basic tempura recipe and generates cooking instructions detailing each step. It also displays a list of ingredients and shows a function to purchase the necessary ingredients for making "tempura" through a delivery service. Furthermore, the server provides helpful cooking videos to assist users making tempura at home, making it easy to understand even for users with little cooking experience.

[0224] In this form, the present invention expands access to Japanese food preparation methods to users worldwide and enables the sharing of its cultural value.

[0225] The following describes the processing flow.

[0226] Step 1:

[0227] Users select photos of Japanese food on their devices and submit them through the system's upload interface. The device then sends the selected images to the server as digital data.

[0228] Step 2:

[0229] The server temporarily stores the received image and passes it to the image analysis module. The server uses machine learning algorithms to analyze the elements within the image and generates data to identify the dish.

[0230] Step 3:

[0231] The server uses the dish name identified through image analysis to retrieve corresponding recipe information from its internal database. This information includes the necessary ingredients and detailed cooking instructions.

[0232] Step 4:

[0233] The server formats the retrieved recipe information and converts it into a format suitable for the user's terminal. The terminal then displays the cooking instructions and ingredient list in the user interface.

[0234] Step 5:

[0235] Users can view the displayed list of ingredients and select a link or button on their device to procure the necessary ingredients using a delivery service. The server utilizes API integration with delivery websites to create an environment where users can order the necessary ingredients.

[0236] Step 6:

[0237] The server provides video content demonstrating relevant cooking methods to help users understand the cooking process. The terminal displays video links to the user, allowing them to watch them as needed.

[0238] This series of steps allows users to easily obtain information and ingredients for efficiently preparing Japanese food.

[0239] (Example 1)

[0240] Next, we will describe Example 1. 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."

[0241] The aim is to provide users who lack knowledge of cooking foreign cuisines, especially Japanese food, with a means to easily and effectively recreate Japanese food while in their own country. Specifically, it aims to resolve challenges in identifying dishes, understanding cooking procedures, and sourcing ingredients.

[0242] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0243] In this invention, the server includes means for providing a user interface for capturing images and converting them into data, means for analyzing the acquired images, identifying dishes using a food classification model, and generating names, and means for generating cooking methods and ingredient information based on the identified dishes. This makes it possible for users unfamiliar with the dishes to instantly obtain appropriate information and ingredients based on the captured images and cook Japanese food.

[0244] A "user interface" is a means or method for a user to interact with a system, and is a mechanism that enables data input and manipulation.

[0245] "Analyzing an image" is the process of analyzing acquired image data using computer algorithms to extract specific information.

[0246] A "food classification model" is a model that uses machine learning algorithms to identify the type of food from image data.

[0247] "Cooking method" refers to information that describes the steps and processes involved in completing a dish using ingredients.

[0248] "Ingredient information" refers to data that shows a list of ingredients and their details, necessary for making a specific dish.

[0249] "Access linked to online services" refers to a means of connecting to necessary services and information via the internet.

[0250] "Visual information" refers to video content provided as visual reference material, which visualizes specific process procedures or knowledge.

[0251] This invention is a system that allows users to easily prepare their desired Japanese food. This system operates through the cooperation of three parties: the user, the terminal, and the server.

[0252] The user first takes a picture of Japanese food using their own device. This device has an interface for uploading the captured image as digital data to the system. The device is able to access the server via the network.

[0253] The server uses a generative AI model to analyze received images. This model is trained to extract culinary features from images and identify specific dish names. The server is equipped with a graphics processing unit (GPU) to enable efficient image processing. Through this analysis, the server accurately identifies the type of dish.

[0254] Next, the server generates cooking instructions and an ingredient list from the database based on the identified dish. In this process, the server uses SQL queries to extract the necessary information from the database and provides it to the user as structured data. The server also integrates with an online shopping platform and provides links to easily purchase ingredients based on the generated ingredient list. The interface provided to the user allows them to order ingredients with just a click.

[0255] Furthermore, the server searches for relevant video information and streams it to the user's device to help them understand the cooking instructions provided. This video information is provided either through the server's own storage or an external video service.

[0256] For example, if a user uploads a photo of tempura, the server analyzes the image and recognizes that the dish is tempura. Next, the server generates a basic cooking method and ingredient list for tempura and provides it to the user. Furthermore, the server provides links to purchase the necessary ingredients online and makes video information about the cooking procedure playable on the user's device. Through this process, users can easily make delicious tempura at home.

[0257] An example of a prompt message would be, "Upload a picture of the tempura you took, and tell me the cooking method and the necessary ingredients." In this way, the system supports the preparation of Japanese food and makes different cultural cuisines more accessible.

[0258] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0259] Step 1:

[0260] The user takes a picture of Japanese food using the device's camera. This image is saved as digital data, and the user can upload it to the system through the program's interface. The input is the captured image data, which is sent to the server for analysis in the next step.

[0261] Step 2:

[0262] The server receives image data sent from the terminal and begins analyzing the image using a generative AI model. This analysis applies a pre-trained food classification model to extract the characteristics of the dishes and identify specific dish names. The input is image data, and the output is the identified dish name. The server then uses this dish name for subsequent database searches.

[0263] Step 3:

[0264] The server consults a database to retrieve cooking instructions and ingredient information based on the identified dish name. Specifically, it uses SQL queries to extract relevant recipes and ingredient lists. The input is the identified dish name, and the output is the cooking procedure and ingredient list for that dish. This information is processed and provided in a format that is easy for the user to understand.

[0265] Step 4:

[0266] The server interacts with the API of an online shopping platform based on the generated material list, setting up a system that allows users to easily purchase the necessary materials. The input is a material list, and the output is links or interfaces for purchasing the materials. Users can purchase the materials by clicking on these links.

[0267] Step 5:

[0268] The server searches for relevant video information to visually support the cooking procedure and configures it to stream to the user's device. The input is cooking procedure information, and the output is a cooking video that the user can watch. The user can cook while watching this video. This process makes it possible for even users with little cooking experience to easily complete a dish.

[0269] (Application Example 1)

[0270] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0271] The goal is to provide methods that enable people around the world to easily prepare Japanese food, particularly by utilizing image recognition technology to quickly obtain necessary recipes and ingredients, and to facilitate the procurement of those ingredients. Furthermore, it aims to provide methods that help even those with little cooking experience understand the process visually.

[0272] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0273] In this invention, the server includes means for providing human-computer interaction for acquiring digital media, means for analyzing the digital media and using a computational model to identify dishes and generate specific meal names, and means for generating corresponding cooking methods and item lists based on the specific meal names. This enables the rapid acquisition of Japanese cooking methods and necessary ingredients from images, easy online purchase of ingredients, and visual cooking assistance.

[0274] "Digital media" refers to electronically recorded information, including images, audio, and video.

[0275] "Human-computer interaction" refers to the interface through which a user exchanges information with a computer system, and includes operations and inputs made through the user interface.

[0276] A "computational model" is a set of algorithms used to recognize specific patterns or information from data, and is primarily used in machine learning and artificial intelligence.

[0277] A "meal name" is a name used to identify a dish, and is the name of a specific dish used in recipes and cooking methods.

[0278] "Cooking method" refers to the procedures and techniques for preparing a meal using specific ingredients.

[0279] A "list of items" is a list of things needed to achieve a specific purpose, and refers to a list of materials and tools.

[0280] "Understanding through visual means" refers to methods of learning and understanding that use visual information such as videos and images, primarily involving the use of educational videos and diagrams.

[0281] This invention provides a system for assisting in Japanese cuisine cooking. When a user takes a photo of a specific Japanese dish, the terminal uploads the digital media to the server. The server analyzes the image using a computational model to identify the dish. Based on the identified specific meal name, the server generates the corresponding cooking method and item list and provides it to the user.

[0282] The system collaborates with e-commerce sites such as Amazon API and Rakuten API to provide a means for easily purchasing the necessary ingredients online based on the generated item list. Also, by using the YouTube API to search for educational videos on related cooking and presenting them to the user, cooking support through vision is provided.

[0283] As a specific example, when a user takes a photo of "tempura" with a smartphone and uploads it, the server analyzes the image to identify "tempura". Based on the identified result, the server extracts the basic cooking method and ingredient list from the database and presents it to the user. Furthermore, when the user clicks on the link where the ingredients can be purchased on Amazon, the tempura ingredients can be easily obtained. And through the YouTube API, a video on "how to make tempura" is provided to the user, allowing them to cook while watching.

[0284] As an example of a prompt sentence, there is "In creating an application that analyzes a photo of sushi, generates the corresponding recipe and ingredient list, and displays links where they can be purchased on e-commerce sites, please tell me what technology stack is optimal."

[0285] The flow of specific processing in Application Example 1 will be described using FIG. 12.

[0286] Step 1:

[0287] The user takes a picture of Japanese food using their device. At this point, the input is the digital image taken by the user. The user sends this image from their device to the server. The sent image becomes the output of this step.

[0288] Step 2:

[0289] The server analyzes the received digital image. This input image is fed into a computational model (generative AI model) for image recognition. As part of data processing, the image is pre-processed into the model's input format. As a result of the analysis, a specific meal name is generated, which becomes the output.

[0290] Step 3:

[0291] The server retrieves the corresponding cooking method and ingredient list from the database based on the generated meal name. The input for this step is the identified meal name. Based on this data, a database search is performed to extract the necessary information. The extracted cooking method and ingredient list are the output.

[0292] Step 4:

[0293] The server interacts with e-commerce sites via APIs based on an item list. The input is an item list, and data calculations are performed to convert it into purchasable links. The output is links for purchasing materials.

[0294] Step 5:

[0295] The server uses the YouTube API to search for relevant cooking videos. The input for this step is the name of the meal. Based on this, the API performs a search to retrieve video information. The output is a list of videos provided as search results.

[0296] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0297] This invention provides an interactive system that takes into account the user's emotional state in order to enhance the Japanese food cooking experience. This system not only identifies dishes from images taken by the user and generates recipes and ingredient lists, but also includes an emotion engine that analyzes the user's emotions and optimizes the interaction.

[0298] The user takes a picture of Japanese food using their device and uploads it to the system. At this stage, the device also activates an emotion recognition module that analyzes the user's facial expressions and voice. The emotion recognition module recognizes the user's current emotional state (e.g., excitement, relaxation, anxiety).

[0299] The server performs image analysis to identify the relevant Japanese dish. Simultaneously, it uses an emotion engine to process user emotion data and customize the user interface. For example, if the user expresses anxiety, the cooking instructions may be made more detailed, or the video's tempo may be slowed down to provide more information.

[0300] The server generates cooking recipes, cooking instructions, and ingredient lists, and delivers them through an interface optimized according to the user's emotional state. Ingredient purchases are facilitated through integration with delivery services, making it easy for users to obtain ingredients. Furthermore, cooking video content is also adjusted according to the user's emotional state, providing a relaxed viewing experience.

[0301] As a concrete example, consider a case where a user uploads an image of "ramen." If the user shows some anxiety or hesitation, the server can use an emotion engine to slow down the playback speed of the cooking video or insert more detailed explanations. It can also display an encouraging message such as "We'll help you with your first cooking experience" based on the user's emotions.

[0302] The present invention aims to realize a more personalized user experience by enabling a cooking support experience that goes beyond mere recipe provision by empathizing with the user's emotions.

[0303] The processing flow will be described below.

[0304] Step 1:

[0305] The user takes a photo of Japanese food with a terminal and uploads the image to the server through the system application. The terminal also sends the user's expression and voice to the emotion recognition module along with the photo.

[0306] Step 2:

[0307] The server uses a machine learning model to analyze the received image and detect a specific dish name. The server processes the data from the emotion recognition module to identify the user's emotional state.

[0308] Step 3:

[0309] The server retrieves recipe information from the database based on the identified dish name. This information includes a list of ingredients and cooking procedures. The server automatically adjusts the display method of the interface according to the user's emotional state.

[0310] Step 4:

[0311] The terminal displays the information received from the server through a customized user interface. If the user shows anxiety, the terminal displays the cooking procedure in more detail and adds a cheering message.

[0312] Step 5:

[0313] The server prepares cooking videos tailored to the user's emotions and enables playback on the device. For example, if the user expresses anxiety or hesitation, the server adjusts the video playback speed or inserts supplementary explanations.

[0314] Step 6:

[0315] When a user selects a link to purchase ingredients, their device is redirected via the server to the delivery service page. The server automatically prepares a shopping cart containing the relevant ingredients.

[0316] This series of processes allows users to receive information optimized for their emotional state, enabling them to efficiently prepare Japanese food.

[0317] (Example 2)

[0318] Next, we will describe Example 2. 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".

[0319] Conventional cooking support systems have been unable to provide an interactive experience that takes into account the user's emotional state, making it difficult to increase user satisfaction. Furthermore, the provision of recipes and assistance with sourcing ingredients is limited, making it difficult to create a personalized service that is convenient for users.

[0320] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0321] In this invention, the server includes means for providing a human-machine interface for acquiring digital images, means for using an artificial intelligence model for recognizing dishes from the digital images and generating specific food names, and means for generating corresponding cooking procedures and ingredient lists based on the specific food names. This makes it possible to provide personalized cooking support that responds to the user's emotional state.

[0322] A "digital image" is visual information expressed in electronic format that can be handled by computers and other digital devices.

[0323] A "human-machine interface" refers to a means of exchanging information between humans and machines, and includes physical or software interfaces that allow users to interact with machines.

[0324] An "artificial intelligence model" refers to an algorithm or system that learns patterns from vast amounts of data and performs a specific task.

[0325] A "food name" is a word or phrase used to refer to cooked or uncooked food.

[0326] "Cooking procedure" refers to a series of operations or actions performed to create a specific dish.

[0327] An "ingredients list" refers to a list of ingredients needed to cook a specific dish.

[0328] "Delivery service" refers to a service that delivers goods or purchased items to a specified location.

[0329] "Video content" is a collection of information that includes dynamic images and sounds, and is provided as a visual medium.

[0330] An "emotion processing engine" refers to a system or technology for recognizing and analyzing a user's emotional state.

[0331] An "interactive interface" is an interface that allows users and systems to exchange information bidirectionally.

[0332] "Information source" refers to the underlying data or database used to obtain or reference specific information.

[0333] This invention is an interactive system designed to enhance the user's cooking experience. It begins with the user taking a picture of a Japanese dish via a terminal and uploading the image. The terminal is equipped with an emotion recognition module, which can analyze the user's emotional state in real time based on their facial expressions and voice.

[0334] Digital images captured by the device are sent to a server. The server is equipped with an advanced artificial intelligence model, which performs image analysis. This model utilizes a generative AI model to identify dishes from the images. Based on the identified dishes, the server generates cooking instructions and a list of ingredients. The server also integrates with delivery services to ensure users can easily obtain the necessary ingredients. Access to delivery services is provided via an online shopping API.

[0335] Furthermore, the server utilizes an emotion processing engine to provide a personalized interface tailored to the user's emotional state. For example, if the user is showing signs of anxiety, the server will display more detailed cooking instructions and adjust the playback speed of the video content. In this way, the server helps optimize the user's cooking experience.

[0336] For example, if a user uploads a picture of ramen and looks a little unsure, the server will provide detailed instructions on how to cook it and play the video in slow motion. Furthermore, it will support the user by displaying a message on the interface that says, "We'll help you with your first cooking experience."

[0337] Examples of prompts include, "Generate a fast-paced cooking video to help the user relax," and "Provide a slow explanation for users who are feeling nervous." These prompts are used as input for the generative AI model to function properly.

[0338] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0339] Step 1:

[0340] The user uses the device to take pictures of Japanese food dishes and uploads them to the system. The input is a high-quality digital image taken with the camera. The output is the transmission of that image file to the system. After this, an emotion recognition module built into the device acquires the user's facial expressions and voice data and analyzes their emotional state in real time. Specifically, the user presses the camera's shutter button, and an interface appears prompting them to upload the image immediately after taking it.

[0341] Step 2:

[0342] Images sent from the terminal are received by the server. The input is an image of a dish uploaded by the user. The server uses a generative AI model to analyze the image and recognize a specific dish. The output is the name of the recognized dish. In this process, the image analysis algorithm analyzes the color, shape, and other visual features to identify a matching dish from an existing database.

[0343] Step 3:

[0344] The server generates the corresponding cooking instructions and ingredient list based on the recognized dish name. The input is the name of the identified dish. The output is the detailed cooking instructions and ingredient list for making that dish. The generated recipe is retrieved from an internal database and formatted in a way that is easiest for the user to understand.

[0345] Step 4:

[0346] The server customizes the interface based on the user's emotional state. The input is emotional data sent from the device. The output is a personalized user interface and video content adjusted as needed. For example, if the user is relaxed, the video tempo is normal, but if they are anxious, more detailed explanations and slow-motion playback are provided. The emotion processing engine makes these adjustments to optimize the user experience.

[0347] Step 5:

[0348] The server provides video content corresponding to cooking instructions and access to delivery services for obtaining necessary ingredients. Inputs are recipe information and ingredient lists. Outputs are links to specific cooking videos and online stores for purchasing ingredients. Delivery services and shopping APIs enable users to quickly purchase ingredients.

[0349] (Application Example 2)

[0350] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0351] Traditional cooking classes and cooking support systems generally provide standardized information, but lack personalized support tailored to individual users' emotions and skill levels. This can make cooking feel difficult, especially for beginners or those feeling anxious. Therefore, there is a need for technologies that improve the cooking experience by taking into account the user's emotional state.

[0352] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0353] In this invention, the server includes means for providing an information input device for acquiring digital images, means for generating a specific food name using an automatic recognition model for recognizing food from the digital images, and means for providing an emotion analysis module for analyzing the user's facial expression data and estimating emotions. This makes it possible to provide customized cooking support tailored to the individual emotional state of the user.

[0354] A "digital image" is a format in which image information is recorded and processed electronically.

[0355] An "information input device" is a device used by users to provide information, and may include cameras and sensors.

[0356] An "automatic recognition model" is an algorithm that uses machine learning to identify specific objects or patterns.

[0357] "Food" refers to ingredients and finished products used in cooking and meals.

[0358] A "specific food name" refers to a unique name assigned to a recognized food item.

[0359] A "delivery service" is a system that provides a service to deliver the materials that users need.

[0360] A "communication channel" refers to a medium or circuit used for sending and receiving data.

[0361] "Visual information" refers to data that includes visual content, and may include videos and still images.

[0362] The "emotion analysis module" is an analysis system for estimating a user's emotional state.

[0363] A "user interface" refers to the means and screen configurations that allow a user and a system to exchange information.

[0364] An "interactive interface" refers to a means of operation or screen that allows a user to exchange information with a system in a two-way manner.

[0365] A "recording device" is a system for storing and making data accessible.

[0366] The system for implementing this invention consists of an interactive system that provides a cooking experience using the user's smart device. The user uploads digital images taken during the cooking process using an information input device such as a smartphone or tablet. These digital images are analyzed by a server, and the food is identified by an automatic recognition model.

[0367] The server generates and provides the user with processing instructions and ingredient lists corresponding to the recognized food. It also provides more specific and user-friendly cooking assistance by offering customized cooking guide videos based on the user's emotional analysis data. The user's facial expressions and voice data are analyzed through an emotional analysis module to estimate their emotional state in real time. Based on this information, the user interface is personalized, displaying appropriate advice and encouraging messages to the user.

[0368] As a concrete example, suppose a user uploads an image of how to prepare "sushi," and the server uses an emotion analysis module to recognize the user's emotional state as relaxed. In this case, the server will omit more detailed explanations than usual and provide a fast-paced cooking video to guide the user at their own pace. Furthermore, to support the purchase of ingredients through a delivery service, a communication channel will be secured to allow for easy acquisition of necessary ingredients.

[0369] In this embodiment, the main software used includes image analysis using Python and OpenCV, sentiment analysis using NVIDIA Maxine, and server-side processing using Django. This enables a personalized cooking assistance service that allows users to improve their cooking skills from the comfort of their homes.

[0370] Examples of prompt messages include the following:

[0371] "Analyze the food images and participants' facial expressions, and provide feedback and next steps that take into account their current emotional state. Image: Uploaded image. Facial expression data: Analyzed facial expression data."

[0372] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0373] Step 1:

[0374] The user takes a picture of the food being cooked using the camera on their smart device. A digital image is obtained as input. The user uploads this image to the server through the application. The output is the image of the food sent to the server.

[0375] Step 2:

[0376] The server inputs the received digital image into an automatic recognition model to identify the food item. The input is a captured digital image. The server performs image analysis and uses the model to generate a specific food name. The output is the recognized food name.

[0377] Step 3:

[0378] The server generates corresponding processing instructions and ingredient lists based on the identified food name. It accepts a food name as input. The server consults a database and extracts the relevant recipe information to provide detailed cooking instructions and ingredient lists as output.

[0379] Step 4:

[0380] The user's smart device uses its camera and microphone to input the user's facial expressions and voice into an emotion analysis module. The input is real-time acquired facial and voice data. The output is the estimated emotional state of the user.

[0381] Step 5:

[0382] The server optimizes the user interface based on the user's emotional state received from the emotion analysis module. It receives the emotional state as input. The server sets the cooking video speed, level of detail in the explanation, and encouraging messages appropriate for the user, and sends the customized interface back to the user's device as output.

[0383] Step 6:

[0384] The server establishes a communication channel to the delivery service to support the purchase of materials. The input is a generated list of materials. The server uses this list to interact with the delivery service and provides the user with options for easily ordering materials. The output is a link to access the materials purchase screen.

[0385] Step 7:

[0386] The user completes the dish by following the provided cooking instructions and watching a customized video guide. The input is cooking instruction information provided by the server. The interface supports the user's actions, and the finished dish is obtained as output.

[0387] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0388] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0389] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0390] [Third Embodiment]

[0391] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0392] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0393] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0394] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0395] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0396] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0397] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0398] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0399] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[0400] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0401] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0402] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0403] This invention provides a system that makes it easy to cook Japanese food in one's own country. Specifically, it enables a process in which a user takes a picture of a particular Japanese dish, and this image is used to automatically retrieve recipes, ingredient lists, and cooking videos, and further enables the necessary ingredients to be obtained through a delivery service.

[0404] Users use their devices to take photos of Japanese food they have eaten at restaurants in the past or made at home. These images are uploaded to the system via their devices.

[0405] The server uses a machine learning model to analyze the received images. This process allows the server to identify the type of Japanese food in question. The server then extracts recipes for the identified Japanese food from its database and generates cooking instructions and ingredient lists to provide to the user.

[0406] Furthermore, based on the generated ingredient list, the server integrates with delivery service platforms to provide users with the option to purchase the necessary ingredients with just a click. The server also provides users with relevant cooking videos to help them understand the cooking process.

[0407] As a concrete example, let's consider a scenario where a user uploads a photo of "tempura" to the system. In this case, the server performs image analysis and recognizes that the dish is "tempura." Next, the server retrieves a basic tempura recipe and generates cooking instructions detailing each step. It also displays a list of ingredients and shows a function to purchase the necessary ingredients for making "tempura" through a delivery service. Furthermore, the server provides helpful cooking videos to assist users making tempura at home, making it easy to understand even for users with little cooking experience.

[0408] In this form, the present invention expands access to Japanese food preparation methods to users worldwide and enables the sharing of its cultural value.

[0409] The following describes the processing flow.

[0410] Step 1:

[0411] Users select photos of Japanese food on their devices and submit them through the system's upload interface. The device then sends the selected images to the server as digital data.

[0412] Step 2:

[0413] The server temporarily stores the received image and passes it to the image analysis module. The server uses machine learning algorithms to analyze the elements within the image and generates data to identify the dish.

[0414] Step 3:

[0415] The server uses the dish name identified through image analysis to retrieve corresponding recipe information from its internal database. This information includes the necessary ingredients and detailed cooking instructions.

[0416] Step 4:

[0417] The server formats the retrieved recipe information and converts it into a format suitable for the user's terminal. The terminal then displays the cooking instructions and ingredient list in the user interface.

[0418] Step 5:

[0419] Users can view the displayed list of ingredients and select a link or button on their device to procure the necessary ingredients using a delivery service. The server utilizes API integration with delivery websites to create an environment where users can order the necessary ingredients.

[0420] Step 6:

[0421] The server provides video content demonstrating relevant cooking methods to help users understand the cooking process. The terminal displays video links to the user, allowing them to watch them as needed.

[0422] This series of steps allows users to easily obtain information and ingredients for efficiently preparing Japanese food.

[0423] (Example 1)

[0424] Next, we will describe Example 1. 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."

[0425] The aim is to provide users who lack knowledge of cooking foreign cuisines, especially Japanese food, with a means to easily and effectively recreate Japanese food while in their own country. Specifically, it aims to resolve challenges in identifying dishes, understanding cooking procedures, and sourcing ingredients.

[0426] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0427] In this invention, the server includes means for providing a user interface for capturing images and converting them into data, means for analyzing the acquired images, identifying dishes using a food classification model, and generating names, and means for generating cooking methods and ingredient information based on the identified dishes. This makes it possible for users unfamiliar with the dishes to instantly obtain appropriate information and ingredients based on the captured images and cook Japanese food.

[0428] A "user interface" is a means or method for a user to interact with a system, and is a mechanism that enables data input and manipulation.

[0429] "Analyzing an image" is the process of analyzing acquired image data using computer algorithms to extract specific information.

[0430] A "food classification model" is a model that uses machine learning algorithms to identify the type of food from image data.

[0431] "Cooking method" refers to information that describes the steps and processes involved in completing a dish using ingredients.

[0432] "Ingredient information" refers to data that shows a list of ingredients and their details, necessary for making a specific dish.

[0433] "Access linked to online services" refers to a means of connecting to necessary services and information via the internet.

[0434] "Visual information" refers to video content provided as visual reference material, which visualizes specific process procedures or knowledge.

[0435] This invention is a system that allows users to easily prepare their desired Japanese food. This system operates through the cooperation of three parties: the user, the terminal, and the server.

[0436] The user first takes a picture of Japanese food using their own device. This device has an interface for uploading the captured image as digital data to the system. The device is able to access the server via the network.

[0437] The server uses a generative AI model to analyze received images. This model is trained to extract culinary features from images and identify specific dish names. The server is equipped with a graphics processing unit (GPU) to enable efficient image processing. Through this analysis, the server accurately identifies the type of dish.

[0438] Next, the server generates cooking instructions and an ingredient list from the database based on the identified dish. In this process, the server uses SQL queries to extract the necessary information from the database and provides it to the user as structured data. The server also integrates with an online shopping platform and provides links to easily purchase ingredients based on the generated ingredient list. The interface provided to the user allows them to order ingredients with just a click.

[0439] Furthermore, the server searches for relevant video information and streams it to the user's device to help them understand the cooking instructions provided. This video information is provided either through the server's own storage or an external video service.

[0440] For example, if a user uploads a photo of tempura, the server analyzes the image and recognizes that the dish is tempura. Next, the server generates a basic cooking method and ingredient list for tempura and provides it to the user. Furthermore, the server provides links to purchase the necessary ingredients online and makes video information about the cooking procedure playable on the user's device. Through this process, users can easily make delicious tempura at home.

[0441] An example of a prompt message would be, "Upload a picture of the tempura you took, and tell me the cooking method and the necessary ingredients." In this way, the system supports the preparation of Japanese food and makes different cultural cuisines more accessible.

[0442] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0443] Step 1:

[0444] The user takes a picture of Japanese food using the device's camera. This image is saved as digital data, and the user can upload it to the system through the program's interface. The input is the captured image data, which is sent to the server for analysis in the next step.

[0445] Step 2:

[0446] The server receives image data sent from the terminal and begins analyzing the image using a generative AI model. This analysis applies a pre-trained food classification model to extract the characteristics of the dishes and identify specific dish names. The input is image data, and the output is the identified dish name. The server then uses this dish name for subsequent database searches.

[0447] Step 3:

[0448] The server consults a database to retrieve cooking instructions and ingredient information based on the identified dish name. Specifically, it uses SQL queries to extract relevant recipes and ingredient lists. The input is the identified dish name, and the output is the cooking procedure and ingredient list for that dish. This information is processed and provided in a format that is easy for the user to understand.

[0449] Step 4:

[0450] The server interacts with the API of an online shopping platform based on the generated material list, setting up a system that allows users to easily purchase the necessary materials. The input is a material list, and the output is links or interfaces for purchasing the materials. Users can purchase the materials by clicking on these links.

[0451] Step 5:

[0452] The server searches for relevant video information to visually support the cooking procedure and configures it to stream to the user's device. The input is cooking procedure information, and the output is a cooking video that the user can watch. The user can cook while watching this video. This process makes it possible for even users with little cooking experience to easily complete a dish.

[0453] (Application Example 1)

[0454] Next, we will explain Application Example 1. In the following explanation, 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."

[0455] The goal is to provide methods that enable people around the world to easily prepare Japanese food, particularly by utilizing image recognition technology to quickly obtain necessary recipes and ingredients, and to facilitate the procurement of those ingredients. Furthermore, it aims to provide methods that help even those with little cooking experience understand the process visually.

[0456] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0457] In this invention, the server includes means for providing human-computer interaction for acquiring digital media, means for analyzing the digital media and using a computational model to identify dishes and generate specific meal names, and means for generating corresponding cooking methods and item lists based on the specific meal names. This enables the rapid acquisition of Japanese cooking methods and necessary ingredients from images, easy online purchase of ingredients, and visual cooking assistance.

[0458] "Digital media" refers to electronically recorded information, including images, audio, and video.

[0459] "Human-computer interaction" refers to the interface through which a user exchanges information with a computer system, and includes operations and inputs made through the user interface.

[0460] A "computational model" is a set of algorithms used to recognize specific patterns or information from data, and is primarily used in machine learning and artificial intelligence.

[0461] A "meal name" is a name used to identify a dish, and is the name of a specific dish used in recipes and cooking methods.

[0462] "Cooking method" refers to the procedures and techniques for preparing a meal using specific ingredients.

[0463] A "list of items" is a list of things needed to achieve a specific purpose, and refers to a list of materials and tools.

[0464] "Understanding through visual means" refers to methods of learning and understanding that use visual information such as videos and images, primarily involving the use of educational videos and diagrams.

[0465] This invention provides a system to assist in the preparation of Japanese food. When a user takes a picture of a specific Japanese dish, the terminal uploads the digital media to a server. The server analyzes the image using a computational model and identifies the dish. Based on the identified specific meal name, the server generates a corresponding cooking method and ingredient list, which it provides to the user.

[0466] The system integrates with e-commerce sites such as Amazon API and Rakuten API, providing a simple way to purchase necessary ingredients online based on the generated item list. It also uses the YouTube API to search for relevant cooking instructional videos and presents them to the user, providing visual cooking support.

[0467] As a concrete example, when a user takes a picture of "tempura" with their smartphone and uploads it, the server analyzes the image and identifies it as "tempura." Based on the identification result, the server retrieves basic cooking instructions and a list of ingredients from its database and presents them to the user. Furthermore, the user can easily obtain the ingredients for tempura by clicking on a link to purchase them on Amazon. Finally, the system provides the user with a video on "how to make tempura" via the YouTube API, allowing them to cook while watching the video.

[0468] An example of a prompt message is: "What technology stack would be best suited for creating an application that analyzes a photo of sushi, generates a corresponding recipe and ingredient list, and displays a link to purchase it from an online store?"

[0469] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0470] Step 1:

[0471] The user takes a picture of Japanese food using their device. At this point, the input is the digital image taken by the user. The user sends this image from their device to the server. The sent image becomes the output of this step.

[0472] Step 2:

[0473] The server analyzes the received digital image. This input image is fed into a computational model (generative AI model) for image recognition. As part of data processing, the image is pre-processed into the model's input format. As a result of the analysis, a specific meal name is generated, which becomes the output.

[0474] Step 3:

[0475] The server retrieves the corresponding cooking method and ingredient list from the database based on the generated meal name. The input for this step is the identified meal name. Based on this data, a database search is performed to extract the necessary information. The extracted cooking method and ingredient list are the output.

[0476] Step 4:

[0477] The server interacts with e-commerce sites via APIs based on an item list. The input is an item list, and data calculations are performed to convert it into purchasable links. The output is links for purchasing materials.

[0478] Step 5:

[0479] The server uses the YouTube API to search for relevant cooking videos. The input for this step is the name of the meal. Based on this, the API performs a search to retrieve video information. The output is a list of videos provided as search results.

[0480] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0481] This invention provides an interactive system that takes into account the user's emotional state in order to enhance the Japanese food cooking experience. This system not only identifies dishes from images taken by the user and generates recipes and ingredient lists, but also includes an emotion engine that analyzes the user's emotions and optimizes the interaction.

[0482] The user takes a picture of Japanese food using their device and uploads it to the system. At this stage, the device also activates an emotion recognition module that analyzes the user's facial expressions and voice. The emotion recognition module recognizes the user's current emotional state (e.g., excitement, relaxation, anxiety).

[0483] The server performs image analysis to identify the relevant Japanese dish. Simultaneously, it uses an emotion engine to process user emotion data and customize the user interface. For example, if the user expresses anxiety, the cooking instructions may be made more detailed, or the video's tempo may be slowed down to provide more information.

[0484] The server generates cooking recipes, cooking instructions, and ingredient lists, and delivers them through an interface optimized according to the user's emotional state. Ingredient purchases are facilitated through integration with delivery services, making it easy for users to obtain ingredients. Furthermore, cooking video content is also adjusted according to the user's emotional state, providing a relaxed viewing experience.

[0485] As a concrete example, consider a case where a user uploads an image of "ramen." If the user shows some anxiety or hesitation, the server can use an emotion engine to slow down the playback speed of the cooking video or insert more detailed explanations. It can also display an encouraging message such as "We'll help you with your first cooking experience" based on the user's emotions.

[0486] This invention aims to provide a more personalized user experience by going beyond simply offering recipes and enabling a cooking support experience that resonates with the user's emotions.

[0487] The following describes the processing flow.

[0488] Step 1:

[0489] The user takes a picture of Japanese food with their device and uploads the image to the server through the system's application. Along with the photo, the device also transmits the user's facial expressions and voice to an emotion recognition module.

[0490] Step 2:

[0491] The server uses a machine learning model to analyze the received images and detect specific dish names. The server also processes data from an emotion recognition module to identify the user's emotional state.

[0492] Step 3:

[0493] The server retrieves recipe information from the database based on the identified dish name. This information includes a list of ingredients and cooking instructions. The server automatically adjusts the interface display based on the user's emotional state.

[0494] Step 4:

[0495] The terminal displays information received from the server via a customized user interface. If the user expresses anxiety, the terminal displays more detailed cooking instructions and adds encouraging messages.

[0496] Step 5:

[0497] The server prepares cooking videos tailored to the user's emotions and enables playback on the device. For example, if the user expresses anxiety or hesitation, the server adjusts the video playback speed or inserts supplementary explanations.

[0498] Step 6:

[0499] When a user selects a link to purchase ingredients, their device is redirected via the server to the delivery service page. The server automatically prepares a shopping cart containing the relevant ingredients.

[0500] This series of processes allows users to receive information optimized for their emotional state, enabling them to efficiently prepare Japanese food.

[0501] (Example 2)

[0502] Next, we will describe Example 2. 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."

[0503] Conventional cooking support systems have been unable to provide an interactive experience that takes into account the user's emotional state, making it difficult to increase user satisfaction. Furthermore, the provision of recipes and assistance with sourcing ingredients is limited, making it difficult to create a personalized service that is convenient for users.

[0504] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0505] In this invention, the server includes means for providing a human-machine interface for acquiring digital images, means for using an artificial intelligence model for recognizing dishes from the digital images and generating specific food names, and means for generating corresponding cooking procedures and ingredient lists based on the specific food names. This makes it possible to provide personalized cooking support that responds to the user's emotional state.

[0506] A "digital image" is visual information expressed in electronic format that can be handled by computers and other digital devices.

[0507] A "human-machine interface" refers to a means of exchanging information between humans and machines, and includes physical or software interfaces that allow users to interact with machines.

[0508] An "artificial intelligence model" refers to an algorithm or system that learns patterns from vast amounts of data and performs a specific task.

[0509] A "food name" is a word or phrase used to refer to cooked or uncooked food.

[0510] "Cooking procedure" refers to a series of operations or actions performed to create a specific dish.

[0511] An "ingredients list" refers to a list of ingredients needed to cook a specific dish.

[0512] "Delivery service" refers to a service that delivers goods or purchased items to a specified location.

[0513] "Video content" is a collection of information that includes dynamic images and sounds, and is provided as a visual medium.

[0514] An "emotion processing engine" refers to a system or technology for recognizing and analyzing a user's emotional state.

[0515] An "interactive interface" is an interface that allows users and systems to exchange information bidirectionally.

[0516] "Information source" refers to the underlying data or database used to obtain or reference specific information.

[0517] This invention is an interactive system designed to enhance the user's cooking experience. It begins with the user taking a picture of a Japanese dish via a terminal and uploading the image. The terminal is equipped with an emotion recognition module, which can analyze the user's emotional state in real time based on their facial expressions and voice.

[0518] Digital images captured by the device are sent to a server. The server is equipped with an advanced artificial intelligence model, which performs image analysis. This model utilizes a generative AI model to identify dishes from the images. Based on the identified dishes, the server generates cooking instructions and a list of ingredients. The server also integrates with delivery services to ensure users can easily obtain the necessary ingredients. Access to delivery services is provided via an online shopping API.

[0519] Furthermore, the server utilizes an emotion processing engine to provide a personalized interface tailored to the user's emotional state. For example, if the user is showing signs of anxiety, the server will display more detailed cooking instructions and adjust the playback speed of the video content. In this way, the server helps optimize the user's cooking experience.

[0520] For example, if a user uploads a picture of ramen and looks a little unsure, the server will provide detailed instructions on how to cook it and play the video in slow motion. Furthermore, it will support the user by displaying a message on the interface that says, "We'll help you with your first cooking experience."

[0521] Examples of prompts include, "Generate a fast-paced cooking video to help the user relax," and "Provide a slow explanation for users who are feeling nervous." These prompts are used as input for the generative AI model to function properly.

[0522] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0523] Step 1:

[0524] The user uses the device to take pictures of Japanese food dishes and uploads them to the system. The input is a high-quality digital image taken with the camera. The output is the transmission of that image file to the system. After this, an emotion recognition module built into the device acquires the user's facial expressions and voice data and analyzes their emotional state in real time. Specifically, the user presses the camera's shutter button, and an interface appears prompting them to upload the image immediately after taking it.

[0525] Step 2:

[0526] Images sent from the terminal are received by the server. The input is an image of a dish uploaded by the user. The server uses a generative AI model to analyze the image and recognize a specific dish. The output is the name of the recognized dish. In this process, the image analysis algorithm analyzes the color, shape, and other visual features to identify a matching dish from an existing database.

[0527] Step 3:

[0528] The server generates the corresponding cooking instructions and ingredient list based on the recognized dish name. The input is the name of the identified dish. The output is the detailed cooking instructions and ingredient list for making that dish. The generated recipe is retrieved from an internal database and formatted in a way that is easiest for the user to understand.

[0529] Step 4:

[0530] The server customizes the interface based on the user's emotional state. The input is emotional data sent from the device. The output is a personalized user interface and video content adjusted as needed. For example, if the user is relaxed, the video tempo is normal, but if they are anxious, more detailed explanations and slow-motion playback are provided. The emotion processing engine makes these adjustments to optimize the user experience.

[0531] Step 5:

[0532] The server provides video content corresponding to cooking instructions and access to delivery services for obtaining necessary ingredients. Inputs are recipe information and ingredient lists. Outputs are links to specific cooking videos and online stores for purchasing ingredients. Delivery services and shopping APIs enable users to quickly purchase ingredients.

[0533] (Application Example 2)

[0534] Next, we will explain application example 2. In the following explanation, 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."

[0535] Traditional cooking classes and cooking support systems generally provide standardized information, but lack personalized support tailored to individual users' emotions and skill levels. This can make cooking feel difficult, especially for beginners or those feeling anxious. Therefore, there is a need for technologies that improve the cooking experience by taking into account the user's emotional state.

[0536] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0537] In this invention, the server includes means for providing an information input device for acquiring digital images, means for generating a specific food name using an automatic recognition model for recognizing food from the digital images, and means for providing an emotion analysis module for analyzing the user's facial expression data and estimating emotions. This makes it possible to provide customized cooking support tailored to the individual emotional state of the user.

[0538] A "digital image" is a format in which image information is recorded and processed electronically.

[0539] An "information input device" is a device used by users to provide information, and may include cameras and sensors.

[0540] An "automatic recognition model" is an algorithm that uses machine learning to identify specific objects or patterns.

[0541] "Food" refers to ingredients and finished products used in cooking and meals.

[0542] A "specific food name" refers to a unique name assigned to a recognized food item.

[0543] A "delivery service" is a system that provides a service to deliver the materials that users need.

[0544] A "communication channel" refers to a medium or circuit used for sending and receiving data.

[0545] "Visual information" refers to data that includes visual content, and may include videos and still images.

[0546] The "emotion analysis module" is an analysis system for estimating a user's emotional state.

[0547] A "user interface" refers to the means and screen configurations that allow a user and a system to exchange information.

[0548] An "interactive interface" refers to a means of operation or screen that allows a user to exchange information with a system in a two-way manner.

[0549] A "recording device" is a system for storing and making data accessible.

[0550] The system for implementing this invention consists of an interactive system that provides a cooking experience using the user's smart device. The user uploads digital images taken during the cooking process using an information input device such as a smartphone or tablet. These digital images are analyzed by a server, and the food is identified by an automatic recognition model.

[0551] The server generates and provides the user with processing instructions and ingredient lists corresponding to the recognized food. It also provides more specific and user-friendly cooking assistance by offering customized cooking guide videos based on the user's emotional analysis data. The user's facial expressions and voice data are analyzed through an emotional analysis module to estimate their emotional state in real time. Based on this information, the user interface is personalized, displaying appropriate advice and encouraging messages to the user.

[0552] As a concrete example, suppose a user uploads an image of how to prepare "sushi," and the server uses an emotion analysis module to recognize the user's emotional state as relaxed. In this case, the server will omit more detailed explanations than usual and provide a fast-paced cooking video to guide the user at their own pace. Furthermore, to support the purchase of ingredients through a delivery service, a communication channel will be secured to allow for easy acquisition of necessary ingredients.

[0553] In this embodiment, the main software used includes image analysis using Python and OpenCV, sentiment analysis using NVIDIA Maxine, and server-side processing using Django. This enables a personalized cooking assistance service that allows users to improve their cooking skills from the comfort of their homes.

[0554] Examples of prompt messages include the following:

[0555] "Analyze the food images and participants' facial expressions, and provide feedback and next steps that take into account their current emotional state. Image: Uploaded image. Facial expression data: Analyzed facial expression data."

[0556] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0557] Step 1:

[0558] The user takes a picture of the food being cooked using the camera on their smart device. A digital image is obtained as input. The user uploads this image to the server through the application. The output is the image of the food sent to the server.

[0559] Step 2:

[0560] The server inputs the received digital image into an automatic recognition model to identify the food item. The input is a captured digital image. The server performs image analysis and uses the model to generate a specific food name. The output is the recognized food name.

[0561] Step 3:

[0562] The server generates corresponding processing instructions and ingredient lists based on the identified food name. It accepts a food name as input. The server consults a database and extracts the relevant recipe information to provide detailed cooking instructions and ingredient lists as output.

[0563] Step 4:

[0564] The user's smart device uses its camera and microphone to input the user's facial expressions and voice into an emotion analysis module. The input is real-time acquired facial and voice data. The output is the estimated emotional state of the user.

[0565] Step 5:

[0566] The server optimizes the user interface based on the user's emotional state received from the emotion analysis module. It receives the emotional state as input. The server sets the cooking video speed, level of detail in the explanation, and encouraging messages appropriate for the user, and sends the customized interface back to the user's device as output.

[0567] Step 6:

[0568] The server establishes a communication channel to the delivery service to support the purchase of materials. The input is a generated list of materials. The server uses this list to interact with the delivery service and provides the user with options for easily ordering materials. The output is a link to access the materials purchase screen.

[0569] Step 7:

[0570] The user completes the dish by following the provided cooking instructions and watching a customized video guide. The input is cooking instruction information provided by the server. The interface supports the user's actions, and the finished dish is obtained as output.

[0571] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0572] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0573] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0574] [Fourth Embodiment]

[0575] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0576] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0577] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0578] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0579] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0580] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0581] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0582] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0583] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0584] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[0585] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0586] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0587] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0588] This invention provides a system that makes it easy to cook Japanese food in one's own country. Specifically, it enables a process in which a user takes a picture of a particular Japanese dish, and this image is used to automatically retrieve recipes, ingredient lists, and cooking videos, and further enables the necessary ingredients to be obtained through a delivery service.

[0589] Users use their devices to take photos of Japanese food they have eaten at restaurants in the past or made at home. These images are uploaded to the system via their devices.

[0590] The server uses a machine learning model to analyze the received images. This process allows the server to identify the type of Japanese food in question. The server then extracts recipes for the identified Japanese food from its database and generates cooking instructions and ingredient lists to provide to the user.

[0591] Furthermore, based on the generated ingredient list, the server integrates with delivery service platforms to provide users with the option to purchase the necessary ingredients with just a click. The server also provides users with relevant cooking videos to help them understand the cooking process.

[0592] As a concrete example, let's consider a scenario where a user uploads a photo of "tempura" to the system. In this case, the server performs image analysis and recognizes that the dish is "tempura." Next, the server retrieves a basic tempura recipe and generates cooking instructions detailing each step. It also displays a list of ingredients and shows a function to purchase the necessary ingredients for making "tempura" through a delivery service. Furthermore, the server provides helpful cooking videos to assist users making tempura at home, making it easy to understand even for users with little cooking experience.

[0593] In this form, the present invention expands access to Japanese food preparation methods to users worldwide and enables the sharing of its cultural value.

[0594] The following describes the processing flow.

[0595] Step 1:

[0596] Users select photos of Japanese food on their devices and submit them through the system's upload interface. The device then sends the selected images to the server as digital data.

[0597] Step 2:

[0598] The server temporarily stores the received image and passes it to the image analysis module. The server uses machine learning algorithms to analyze the elements within the image and generates data to identify the dish.

[0599] Step 3:

[0600] The server uses the dish name identified through image analysis to retrieve corresponding recipe information from its internal database. This information includes the necessary ingredients and detailed cooking instructions.

[0601] Step 4:

[0602] The server formats the retrieved recipe information and converts it into a format suitable for the user's terminal. The terminal then displays the cooking instructions and ingredient list in the user interface.

[0603] Step 5:

[0604] Users can view the displayed list of ingredients and select a link or button on their device to procure the necessary ingredients using a delivery service. The server utilizes API integration with delivery websites to create an environment where users can order the necessary ingredients.

[0605] Step 6:

[0606] The server provides video content demonstrating relevant cooking methods to help users understand the cooking process. The terminal displays video links to the user, allowing them to watch them as needed.

[0607] This series of steps allows users to easily obtain information and ingredients for efficiently preparing Japanese food.

[0608] (Example 1)

[0609] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0610] The aim is to provide users who lack knowledge of cooking foreign cuisines, especially Japanese food, with a means to easily and effectively recreate Japanese food while in their own country. Specifically, it aims to resolve challenges in identifying dishes, understanding cooking procedures, and sourcing ingredients.

[0611] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0612] In this invention, the server includes means for providing a user interface for capturing images and converting them into data, means for analyzing the acquired images, identifying dishes using a food classification model, and generating names, and means for generating cooking methods and ingredient information based on the identified dishes. This makes it possible for users unfamiliar with the dishes to instantly obtain appropriate information and ingredients based on the captured images and cook Japanese food.

[0613] A "user interface" is a means or method for a user to interact with a system, and is a mechanism that enables data input and manipulation.

[0614] "Analyzing an image" is the process of analyzing acquired image data using computer algorithms to extract specific information.

[0615] A "food classification model" is a model that uses machine learning algorithms to identify the type of food from image data.

[0616] "Cooking method" refers to information that describes the steps and processes involved in completing a dish using ingredients.

[0617] "Ingredient information" refers to data that shows a list of ingredients and their details, necessary for making a specific dish.

[0618] "Access linked to online services" refers to a means of connecting to necessary services and information via the internet.

[0619] "Visual information" refers to video content provided as visual reference material, which visualizes specific process procedures or knowledge.

[0620] This invention is a system that allows users to easily prepare their desired Japanese food. This system operates through the cooperation of three parties: the user, the terminal, and the server.

[0621] The user first takes a picture of Japanese food using their own device. This device has an interface for uploading the captured image as digital data to the system. The device is able to access the server via the network.

[0622] The server uses a generative AI model to analyze received images. This model is trained to extract culinary features from images and identify specific dish names. The server is equipped with a graphics processing unit (GPU) to enable efficient image processing. Through this analysis, the server accurately identifies the type of dish.

[0623] Next, the server generates cooking instructions and an ingredient list from the database based on the identified dish. In this process, the server uses SQL queries to extract the necessary information from the database and provides it to the user as structured data. The server also integrates with an online shopping platform and provides links to easily purchase ingredients based on the generated ingredient list. The interface provided to the user allows them to order ingredients with just a click.

[0624] Furthermore, the server searches for relevant video information and streams it to the user's device to help them understand the cooking instructions provided. This video information is provided either through the server's own storage or an external video service.

[0625] For example, if a user uploads a photo of tempura, the server analyzes the image and recognizes that the dish is tempura. Next, the server generates a basic cooking method and ingredient list for tempura and provides it to the user. Furthermore, the server provides links to purchase the necessary ingredients online and makes video information about the cooking procedure playable on the user's device. Through this process, users can easily make delicious tempura at home.

[0626] An example of a prompt message would be, "Upload a picture of the tempura you took, and tell me the cooking method and the necessary ingredients." In this way, the system supports the preparation of Japanese food and makes different cultural cuisines more accessible.

[0627] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0628] Step 1:

[0629] The user takes a picture of Japanese food using the device's camera. This image is saved as digital data, and the user can upload it to the system through the program's interface. The input is the captured image data, which is sent to the server for analysis in the next step.

[0630] Step 2:

[0631] The server receives image data sent from the terminal and begins analyzing the image using a generative AI model. This analysis applies a pre-trained food classification model to extract the characteristics of the dishes and identify specific dish names. The input is image data, and the output is the identified dish name. The server then uses this dish name for subsequent database searches.

[0632] Step 3:

[0633] The server consults a database to retrieve cooking instructions and ingredient information based on the identified dish name. Specifically, it uses SQL queries to extract relevant recipes and ingredient lists. The input is the identified dish name, and the output is the cooking procedure and ingredient list for that dish. This information is processed and provided in a format that is easy for the user to understand.

[0634] Step 4:

[0635] The server interacts with the API of an online shopping platform based on the generated material list, setting up a system that allows users to easily purchase the necessary materials. The input is a material list, and the output is links or interfaces for purchasing the materials. Users can purchase the materials by clicking on these links.

[0636] Step 5:

[0637] The server searches for relevant video information to visually support the cooking procedure and configures it to stream to the user's device. The input is cooking procedure information, and the output is a cooking video that the user can watch. The user can cook while watching this video. This process makes it possible for even users with little cooking experience to easily complete a dish.

[0638] (Application Example 1)

[0639] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0640] The goal is to provide methods that enable people around the world to easily prepare Japanese food, particularly by utilizing image recognition technology to quickly obtain necessary recipes and ingredients, and to facilitate the procurement of those ingredients. Furthermore, it aims to provide methods that help even those with little cooking experience understand the process visually.

[0641] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0642] In this invention, the server includes means for providing human-computer interaction for acquiring digital media, means for analyzing the digital media and using a computational model to identify dishes and generate specific meal names, and means for generating corresponding cooking methods and item lists based on the specific meal names. This enables the rapid acquisition of Japanese cooking methods and necessary ingredients from images, easy online purchase of ingredients, and visual cooking assistance.

[0643] "Digital media" refers to electronically recorded information, including images, audio, and video.

[0644] "Human-computer interaction" refers to the interface through which a user exchanges information with a computer system, and includes operations and inputs made through the user interface.

[0645] A "computational model" is a set of algorithms used to recognize specific patterns or information from data, and is primarily used in machine learning and artificial intelligence.

[0646] A "meal name" is a name used to identify a dish, and is the name of a specific dish used in recipes and cooking methods.

[0647] "Cooking method" refers to the procedures and techniques for preparing a meal using specific ingredients.

[0648] A "list of items" is a list of things needed to achieve a specific purpose, and refers to a list of materials and tools.

[0649] "Understanding through visual means" refers to methods of learning and understanding that use visual information such as videos and images, primarily involving the use of educational videos and diagrams.

[0650] This invention provides a system to assist in the preparation of Japanese food. When a user takes a picture of a specific Japanese dish, the terminal uploads the digital media to a server. The server analyzes the image using a computational model and identifies the dish. Based on the identified specific meal name, the server generates a corresponding cooking method and ingredient list, which it provides to the user.

[0651] The system integrates with e-commerce sites such as Amazon API and Rakuten API, providing a simple way to purchase necessary ingredients online based on the generated item list. It also uses the YouTube API to search for relevant cooking instructional videos and presents them to the user, providing visual cooking support.

[0652] As a concrete example, when a user takes a picture of "tempura" with their smartphone and uploads it, the server analyzes the image and identifies it as "tempura." Based on the identification result, the server retrieves basic cooking instructions and a list of ingredients from its database and presents them to the user. Furthermore, the user can easily obtain the ingredients for tempura by clicking on a link to purchase them on Amazon. Finally, the system provides the user with a video on "how to make tempura" via the YouTube API, allowing them to cook while watching the video.

[0653] An example of a prompt message is: "What technology stack would be best suited for creating an application that analyzes a photo of sushi, generates a corresponding recipe and ingredient list, and displays a link to purchase it from an online store?"

[0654] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0655] Step 1:

[0656] The user takes a picture of Japanese food using their device. At this point, the input is the digital image taken by the user. The user sends this image from their device to the server. The sent image becomes the output of this step.

[0657] Step 2:

[0658] The server analyzes the received digital image. This input image is fed into a computational model (generative AI model) for image recognition. As part of data processing, the image is pre-processed into the model's input format. As a result of the analysis, a specific meal name is generated, which becomes the output.

[0659] Step 3:

[0660] The server retrieves the corresponding cooking method and ingredient list from the database based on the generated meal name. The input for this step is the identified meal name. Based on this data, a database search is performed to extract the necessary information. The extracted cooking method and ingredient list are the output.

[0661] Step 4:

[0662] The server interacts with e-commerce sites via APIs based on an item list. The input is an item list, and data calculations are performed to convert it into purchasable links. The output is links for purchasing materials.

[0663] Step 5:

[0664] The server uses the YouTube API to search for relevant cooking videos. The input for this step is the name of the meal. Based on this, the API performs a search to retrieve video information. The output is a list of videos provided as search results.

[0665] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0666] This invention provides an interactive system that takes into account the user's emotional state in order to enhance the Japanese food cooking experience. This system not only identifies dishes from images taken by the user and generates recipes and ingredient lists, but also includes an emotion engine that analyzes the user's emotions and optimizes the interaction.

[0667] The user takes a picture of Japanese food using their device and uploads it to the system. At this stage, the device also activates an emotion recognition module that analyzes the user's facial expressions and voice. The emotion recognition module recognizes the user's current emotional state (e.g., excitement, relaxation, anxiety).

[0668] The server performs image analysis to identify the relevant Japanese dish. Simultaneously, it uses an emotion engine to process user emotion data and customize the user interface. For example, if the user expresses anxiety, the cooking instructions may be made more detailed, or the video's tempo may be slowed down to provide more information.

[0669] The server generates cooking recipes, cooking instructions, and ingredient lists, and delivers them through an interface optimized according to the user's emotional state. Ingredient purchases are facilitated through integration with delivery services, making it easy for users to obtain ingredients. Furthermore, cooking video content is also adjusted according to the user's emotional state, providing a relaxed viewing experience.

[0670] As a concrete example, consider a case where a user uploads an image of "ramen." If the user shows some anxiety or hesitation, the server can use an emotion engine to slow down the playback speed of the cooking video or insert more detailed explanations. It can also display an encouraging message such as "We'll help you with your first cooking experience" based on the user's emotions.

[0671] This invention aims to provide a more personalized user experience by going beyond simply offering recipes and enabling a cooking support experience that resonates with the user's emotions.

[0672] The following describes the processing flow.

[0673] Step 1:

[0674] The user takes a picture of Japanese food with their device and uploads the image to the server through the system's application. Along with the photo, the device also transmits the user's facial expressions and voice to an emotion recognition module.

[0675] Step 2:

[0676] The server uses a machine learning model to analyze the received images and detect specific dish names. The server also processes data from an emotion recognition module to identify the user's emotional state.

[0677] Step 3:

[0678] The server retrieves recipe information from the database based on the identified dish name. This information includes a list of ingredients and cooking instructions. The server automatically adjusts the interface display based on the user's emotional state.

[0679] Step 4:

[0680] The terminal displays information received from the server via a customized user interface. If the user expresses anxiety, the terminal displays more detailed cooking instructions and adds encouraging messages.

[0681] Step 5:

[0682] The server prepares cooking videos tailored to the user's emotions and enables playback on the device. For example, if the user expresses anxiety or hesitation, the server adjusts the video playback speed or inserts supplementary explanations.

[0683] Step 6:

[0684] When a user selects a link to purchase ingredients, their device is redirected via the server to the delivery service page. The server automatically prepares a shopping cart containing the relevant ingredients.

[0685] This series of processes allows users to receive information optimized for their emotional state, enabling them to efficiently prepare Japanese food.

[0686] (Example 2)

[0687] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0688] Conventional cooking support systems have been unable to provide an interactive experience that takes into account the user's emotional state, making it difficult to increase user satisfaction. Furthermore, the provision of recipes and assistance with sourcing ingredients is limited, making it difficult to create a personalized service that is convenient for users.

[0689] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0690] In this invention, the server includes means for providing a human-machine interface for acquiring digital images, means for using an artificial intelligence model for recognizing dishes from the digital images and generating specific food names, and means for generating corresponding cooking procedures and ingredient lists based on the specific food names. This makes it possible to provide personalized cooking support that responds to the user's emotional state.

[0691] A "digital image" is visual information expressed in electronic format that can be handled by computers and other digital devices.

[0692] A "human-machine interface" refers to a means of exchanging information between humans and machines, and includes physical or software interfaces that allow users to interact with machines.

[0693] An "artificial intelligence model" refers to an algorithm or system that learns patterns from vast amounts of data and performs a specific task.

[0694] A "food name" is a word or phrase used to refer to cooked or uncooked food.

[0695] "Cooking procedure" refers to a series of operations or actions performed to create a specific dish.

[0696] An "ingredients list" refers to a list of ingredients needed to cook a specific dish.

[0697] "Delivery service" refers to a service that delivers goods or purchased items to a specified location.

[0698] "Video content" is a collection of information that includes dynamic images and sounds, and is provided as a visual medium.

[0699] An "emotion processing engine" refers to a system or technology for recognizing and analyzing a user's emotional state.

[0700] An "interactive interface" is an interface that allows users and systems to exchange information bidirectionally.

[0701] "Information source" refers to the underlying data or database used to obtain or reference specific information.

[0702] This invention is an interactive system designed to enhance the user's cooking experience. It begins with the user taking a picture of a Japanese dish via a terminal and uploading the image. The terminal is equipped with an emotion recognition module, which can analyze the user's emotional state in real time based on their facial expressions and voice.

[0703] Digital images captured by the device are sent to a server. The server is equipped with an advanced artificial intelligence model, which performs image analysis. This model utilizes a generative AI model to identify dishes from the images. Based on the identified dishes, the server generates cooking instructions and a list of ingredients. The server also integrates with delivery services to ensure users can easily obtain the necessary ingredients. Access to delivery services is provided via an online shopping API.

[0704] Furthermore, the server utilizes an emotion processing engine to provide a personalized interface tailored to the user's emotional state. For example, if the user is showing signs of anxiety, the server will display more detailed cooking instructions and adjust the playback speed of the video content. In this way, the server helps optimize the user's cooking experience.

[0705] For example, if a user uploads a picture of ramen and looks a little unsure, the server will provide detailed instructions on how to cook it and play the video in slow motion. Furthermore, it will support the user by displaying a message on the interface that says, "We'll help you with your first cooking experience."

[0706] Examples of prompts include, "Generate a fast-paced cooking video to help the user relax," and "Provide a slow explanation for users who are feeling nervous." These prompts are used as input for the generative AI model to function properly.

[0707] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0708] Step 1:

[0709] The user uses the device to take pictures of Japanese food dishes and uploads them to the system. The input is a high-quality digital image taken with the camera. The output is the transmission of that image file to the system. After this, an emotion recognition module built into the device acquires the user's facial expressions and voice data and analyzes their emotional state in real time. Specifically, the user presses the camera's shutter button, and an interface appears prompting them to upload the image immediately after taking it.

[0710] Step 2:

[0711] Images sent from the terminal are received by the server. The input is an image of a dish uploaded by the user. The server uses a generative AI model to analyze the image and recognize a specific dish. The output is the name of the recognized dish. In this process, the image analysis algorithm analyzes the color, shape, and other visual features to identify a matching dish from an existing database.

[0712] Step 3:

[0713] The server generates the corresponding cooking instructions and ingredient list based on the recognized dish name. The input is the name of the identified dish. The output is the detailed cooking instructions and ingredient list for making that dish. The generated recipe is retrieved from an internal database and formatted in a way that is easiest for the user to understand.

[0714] Step 4:

[0715] The server customizes the interface based on the user's emotional state. The input is emotional data sent from the device. The output is a personalized user interface and video content adjusted as needed. For example, if the user is relaxed, the video tempo is normal, but if they are anxious, more detailed explanations and slow-motion playback are provided. The emotion processing engine makes these adjustments to optimize the user experience.

[0716] Step 5:

[0717] The server provides video content corresponding to cooking instructions and access to delivery services for obtaining necessary ingredients. Inputs are recipe information and ingredient lists. Outputs are links to specific cooking videos and online stores for purchasing ingredients. Delivery services and shopping APIs enable users to quickly purchase ingredients.

[0718] (Application Example 2)

[0719] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0720] Traditional cooking classes and cooking support systems generally provide standardized information, but lack personalized support tailored to individual users' emotions and skill levels. This can make cooking feel difficult, especially for beginners or those feeling anxious. Therefore, there is a need for technologies that improve the cooking experience by taking into account the user's emotional state.

[0721] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0722] In this invention, the server includes means for providing an information input device for acquiring digital images, means for generating a specific food name using an automatic recognition model for recognizing food from the digital images, and means for providing an emotion analysis module for analyzing the user's facial expression data and estimating emotions. This makes it possible to provide customized cooking support tailored to the individual emotional state of the user.

[0723] A "digital image" is a format in which image information is recorded and processed electronically.

[0724] An "information input device" is a device used by users to provide information, and may include cameras and sensors.

[0725] An "automatic recognition model" is an algorithm that uses machine learning to identify specific objects or patterns.

[0726] "Food" refers to ingredients and finished products used in cooking and meals.

[0727] A "specific food name" refers to a unique name assigned to a recognized food item.

[0728] A "delivery service" is a system that provides a service to deliver the materials that users need.

[0729] A "communication channel" refers to a medium or circuit used for sending and receiving data.

[0730] "Visual information" refers to data that includes visual content, and may include videos and still images.

[0731] The "emotion analysis module" is an analysis system for estimating a user's emotional state.

[0732] A "user interface" refers to the means and screen configurations that allow a user and a system to exchange information.

[0733] An "interactive interface" refers to a means of operation or screen that allows a user to exchange information with a system in a two-way manner.

[0734] A "recording device" is a system for storing and making data accessible.

[0735] The system for implementing this invention consists of an interactive system that provides a cooking experience using the user's smart device. The user uploads digital images taken during the cooking process using an information input device such as a smartphone or tablet. These digital images are analyzed by a server, and the food is identified by an automatic recognition model.

[0736] The server generates and provides the user with processing instructions and ingredient lists corresponding to the recognized food. It also provides more specific and user-friendly cooking assistance by offering customized cooking guide videos based on the user's emotional analysis data. The user's facial expressions and voice data are analyzed through an emotional analysis module to estimate their emotional state in real time. Based on this information, the user interface is personalized, displaying appropriate advice and encouraging messages to the user.

[0737] As a concrete example, suppose a user uploads an image of how to prepare "sushi," and the server uses an emotion analysis module to recognize the user's emotional state as relaxed. In this case, the server will omit more detailed explanations than usual and provide a fast-paced cooking video to guide the user at their own pace. Furthermore, to support the purchase of ingredients through a delivery service, a communication channel will be secured to allow for easy acquisition of necessary ingredients.

[0738] In this embodiment, the main software used includes image analysis using Python and OpenCV, sentiment analysis using NVIDIA Maxine, and server-side processing using Django. This enables a personalized cooking assistance service that allows users to improve their cooking skills from the comfort of their homes.

[0739] Examples of prompt messages include the following:

[0740] "Analyze the food images and participants' facial expressions, and provide feedback and next steps that take into account their current emotional state. Image: Uploaded image. Facial expression data: Analyzed facial expression data."

[0741] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0742] Step 1:

[0743] The user takes a picture of the food being cooked using the camera on their smart device. A digital image is obtained as input. The user uploads this image to the server through the application. The output is the image of the food sent to the server.

[0744] Step 2:

[0745] The server inputs the received digital image into an automatic recognition model to identify the food item. The input is a captured digital image. The server performs image analysis and uses the model to generate a specific food name. The output is the recognized food name.

[0746] Step 3:

[0747] The server generates corresponding processing instructions and ingredient lists based on the identified food name. It accepts a food name as input. The server consults a database and extracts the relevant recipe information to provide detailed cooking instructions and ingredient lists as output.

[0748] Step 4:

[0749] The user's smart device uses its camera and microphone to input the user's facial expressions and voice into an emotion analysis module. The input is real-time acquired facial and voice data. The output is the estimated emotional state of the user.

[0750] Step 5:

[0751] The server optimizes the user interface based on the user's emotional state received from the emotion analysis module. It receives the emotional state as input. The server sets the cooking video speed, level of detail in the explanation, and encouraging messages appropriate for the user, and sends the customized interface back to the user's device as output.

[0752] Step 6:

[0753] The server establishes a communication channel to the delivery service to support the purchase of materials. The input is a generated list of materials. The server uses this list to interact with the delivery service and provides the user with options for easily ordering materials. The output is a link to access the materials purchase screen.

[0754] Step 7:

[0755] The user completes the dish by following the provided cooking instructions and watching a customized video guide. The input is cooking instruction information provided by the server. The interface supports the user's actions, and the finished dish is obtained as output.

[0756] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0757] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0758] 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 this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0759] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0760] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0761] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0762] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0763] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0764] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0765] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0766] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0767] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0768] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0769] 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.

[0770] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0771] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0772] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0773] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0774] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0775] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0776] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0777] The following is further disclosed regarding the embodiments described above.

[0778] (Claim 1)

[0779] A means for providing a user interface for acquiring digital images,

[0780] A means for generating a specific dish name using a machine learning model to recognize a dish from the digital image,

[0781] A means for generating a corresponding cooking procedure and ingredient list based on the name of the specific dish,

[0782] A means of providing access to a delivery service to obtain the necessary materials,

[0783] A means for providing video content that visually demonstrates the cooking procedure,

[0784] A system that includes this.

[0785] (Claim 2)

[0786] The system according to claim 1, which provides an interactive interface for a user to select and purchase materials.

[0787] (Claim 3)

[0788] The system according to claim 1, which refers to and updates a database for optimizing the process of identifying dishes and sourcing ingredients.

[0789] "Example 1"

[0790] (Claim 1)

[0791] A means of providing a user interface for capturing images and converting them into data,

[0792] A means for analyzing acquired images, identifying dishes using a food classification model, and generating names,

[0793] A means for generating cooking method and ingredient information based on the identified dish,

[0794] A means of providing access to online services that allow for easy purchase of materials,

[0795] A means of providing video information that visually shows cooking methods,

[0796] A system that includes this.

[0797] (Claim 2)

[0798] The system according to claim 1, which provides an interactive interface for a user to select the materials they need and complete a transaction.

[0799] (Claim 3)

[0800] The system according to claim 1, which references and updates an information infrastructure to streamline food classification and material procurement procedures.

[0801] "Application Example 1"

[0802] (Claim 1)

[0803] A means of providing human-computer interaction for acquiring digital media,

[0804] A means for generating a specific meal name by using a computational model to analyze the digital media and identify the dish,

[0805] A means for generating a corresponding cooking method and item list based on the specific meal name,

[0806] Means of providing access to delivery services for obtaining necessary goods,

[0807] Means for providing audiovisual content that visually demonstrates the cooking method,

[0808] A means of providing interactive information exchange for users to select and acquire items,

[0809] A system that includes this.

[0810] (Claim 2)

[0811] The system according to claim 1, which refers to and updates a set of information for optimizing the process of identifying dishes and retrieving items.

[0812] (Claim 3)

[0813] The system according to claim 1, which provides perceptual support by searching for and presenting educational videos related to the cooking process.

[0814] "Example 2 of combining an emotion engine"

[0815] (Claim 1)

[0816] A means for providing a human-machine interface for acquiring digital images,

[0817] A means for generating a specific food name using an artificial intelligence model for recognizing food from the digital image,

[0818] A means for generating a corresponding cooking procedure and ingredient list based on the specific food name,

[0819] A means of providing access to delivery services to obtain the necessary materials,

[0820] A means for providing video content that visually illustrates the cooking procedure,

[0821] A means including an emotion processing engine for analyzing the user's emotional state and personalizing the human-machine interface,

[0822] A system that includes this.

[0823] (Claim 2)

[0824] The system according to claim 1, which provides an interactive interface for a user to select and purchase materials.

[0825] (Claim 3)

[0826] The system according to claim 1, which references and updates information sources for optimizing food identification and material procurement processes.

[0827] "Application example 2 when combining with an emotional engine"

[0828] (Claim 1)

[0829] Means for providing an information input device for acquiring digital images,

[0830] A means for generating a specific food name using an automatic recognition model for recognizing food from the digital image,

[0831] A means for generating a corresponding processing procedure and ingredient list based on the specific food name,

[0832] A means of providing a communication channel to a delivery service for obtaining necessary materials,

[0833] Means for providing video information that visually represents the processing procedure,

[0834] A means for providing an emotion analysis module that analyzes user facial expression data and estimates emotions,

[0835] A means for optimizing the user interface and providing personalized information in accordance with the user's emotions estimated by the emotion analysis module,

[0836] A system that includes this.

[0837] (Claim 2)

[0838] The system according to claim 1, which provides an interactive interface for selecting and purchasing materials.

[0839] (Claim 3)

[0840] The system according to claim 1, which references and updates a recording device for optimizing the process of identifying food products and procuring ingredients. [Explanation of Symbols]

[0841] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means for providing a user interface for acquiring digital images, A means for generating a specific dish name using a machine learning model to recognize a dish from the digital image, A means for generating a corresponding cooking procedure and ingredient list based on the name of the specific dish, A means of providing access to a delivery service to obtain the necessary materials, A means for providing video content that visually demonstrates the cooking procedure, A system that includes this.

2. The system according to claim 1, which provides an interactive interface for a user to select and purchase materials.

3. The system according to claim 1, which refers to and updates a database for optimizing the process of identifying dishes and sourcing ingredients.

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