System

Abstract

A system is provided.SOLUTION: A system including means for taking a picture of a food material, means for transmitting the picture of the food material taken to a server, means for analyzing a type and an amount of the food material on the server, means for generating a recipe in consideration of nutrition balance based on an analysis result, means for generating a moving image of a cooking process based on the generated recipe, means for specifying an insufficient food material and ordering an additional necessary food material in cooperation with an external application, and means for returning the generated recipe and the cooking process moving image to a terminal.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] This is to solve the problem of dual-income families with children, housewives, and seniors being unable to easily prepare nutritionally balanced meals amid rising prices, while also reducing the effort required to plan daily menus. There are also problems such as the stress caused by confusing cooking procedures and not having all the necessary ingredients. [Means for solving the problem]

[0005] The present invention solves these problems by providing a system that includes the following means:

[0006] A means of taking photos of ingredients,

[0007] A means for transmitting the photograph of the food ingredient to a server;

[0008] A means of analyzing the type and amount of ingredients on the server,

[0009] A means for generating recipes that take nutritional balance into consideration based on the analysis results;

[0010] A means for generating a video of the cooking process based on the generated recipe;

[0011] A way to identify missing ingredients and order additional ingredients by connecting with an external app.

[0012] The system includes a means for returning the generated recipe and cooking process video to the terminal.

[0013] Furthermore, by including a means to project the generated cooking process video onto a wall via a smart home device and a means to send the photographed food data to a server in JSON format, users can efficiently plan menus and easily cook. Also, the ordering function for ingredients that are in short supply allows necessary ingredients to be gathered immediately, significantly reducing the effort required for cooking.

[0014] "Means for taking photos of ingredients" is a function that allows a user to take photos of ingredients in the refrigerator or elsewhere using a smartphone or camera.

[0015] The "means for transmitting the photographed food ingredient photos to the server" is a communication function that enables the terminal to transmit the photographed photo data to the server.

[0016] The "means for analyzing the type and amount of ingredients on the server" is a function that uses an image analysis algorithm on the server to identify the type and amount of ingredients from the transmitted photograph data.

[0017] The "means for generating recipes that take nutritional balance into consideration based on the analysis results" is a function in which the server combines ingredients based on the image analysis results and automatically generates nutritionally balanced recipes.

[0018] The "means for generating a video of the cooking process based on the generated recipe" is a function that creates a video showing the steps when the user cooks a dish based on the recipe generated by the server.

[0019] "Means to identify missing ingredients and order additional ingredients in cooperation with an external application" is a function that identifies missing ingredients based on the recipe generated by the server and enables the user to order them in cooperation with an external shopping application.

[0020] The "means for returning the generated recipe and cooking process video to the terminal" is a function for transmitting the recipe and cooking process video created by the server to the user's terminal and displaying them.

[0021] "Means for projecting onto a wall via a smart home device" refers to a function for projecting a cooking process video onto a wall using a smart home device (e.g., a projector).

[0022] "Means for sending image data to the server in JSON format" is a function for converting photographed food material data into JSON (JavaScript Object Notation) format and sending it to the server. [Brief explanation of the drawings]

[0023] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0024] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0025] First, the terms used in the following description will be explained.

[0026] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0027] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0028] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0029] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0030] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0031] [First embodiment]

[0032] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0033] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0034] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0035] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0036] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0037] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0038] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0039] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0040] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0041] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0042] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0043] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0044] The present invention is a system that takes photos of ingredients in a refrigerator, analyzes the image data, generates suitable dish options and recipes, and provides videos of the cooking process. Detailed embodiments of the system are described below.

[0045] First, a dedicated application is installed on a user's smartphone, tablet, or other device. The user uses this application to take a photo of the food in the refrigerator. Once the photo is taken, the device automatically converts the photo data into JSON format and sends it to a server via the Internet.

[0046] The photo data received by the server is first analyzed using an image analysis algorithm. During this process, the server identifies the types and amounts of ingredients. The identified ingredient information is then input into a recipe generation algorithm that takes nutritional balance into consideration. This allows the server to generate the best possible recipes for the user.

[0047] Based on the generated recipe, the server generates a video of the cooking process. The server retrieves video clips corresponding to each cooking step from the database and combines them to create a continuous video of the cooking process. This video includes detailed instructions for each cooking step, a timer function, and audio narration.

[0048] Next, the server checks the list of all necessary ingredients based on the generated recipe. If any ingredients that the user does not have are identified, the server connects the list to an external shopping app and provides the user with the option to order additional ingredients. The user can easily purchase the missing ingredients through the shopping app.

[0049] Finally, the server sends the generated recipe and cooking process video back to the device. The device receives this data and displays it on the application screen. The user then proceeds with the cooking process based on the displayed information. Furthermore, users of smart home devices can project the cooking process video from the device screen onto the wall. This function frees up the user's hands and allows for more efficient cooking.

[0050] For example, if a user takes a photo of "tomatoes," "chicken," and "cheese" in the refrigerator, the server analyzes this information and generates a recipe for "chicken and tomato pasta." Based on the recipe, the server then creates a video including instructions on how to bake chicken, how to cut tomatoes, how to boil pasta, and so on, providing detailed step-by-step instructions to help the user with the cooking process.

[0051] With the above configuration, the present invention is a system that allows users to efficiently plan menus and easily prepare nutritionally balanced meals. In addition, the system's ability to order ingredients in short supply reduces the risk of abandoning a cooking project due to lack of necessary ingredients.

[0052] The processing flow will be explained below.

[0053] Step 1:

[0054] The user takes a photo of the ingredients in the refrigerator. The user takes a photo of the ingredients using the camera on their smartphone or tablet, and the image data is saved by a dedicated application.

[0055] Step 2:

[0056] The device converts the captured photo data into JSON format and sends it to the server. The device then sends the photo data to the server via the Internet as an HTTP request.

[0057] Step 3:

[0058] The server analyzes the received photo data. After the server receives the photo data, it launches an image analysis algorithm to identify the type and amount of ingredients.

[0059] Step 4:

[0060] The server generates a recipe based on the analysis results, selecting and generating a nutritionally balanced recipe suitable for the identified ingredients from a recipe database stored on the server.

[0061] Step 5:

[0062] The server generates a cooking process video. Based on the generated recipe, the server combines video clips of each cooking step to create a detailed cooking process video. This video also includes audio commentary and a timer function.

[0063] Step 6:

[0064] The server identifies missing ingredients. The server checks the list of ingredients required for the generated recipe and compares it with the list of ingredients the user already has to identify missing ingredients.

[0065] Step 7:

[0066] The server provides a function to order missing ingredients. The server connects to an external shopping app and provides users with a link or option to order missing ingredients.

[0067] Step 8:

[0068] The server returns the generated recipe and cooking process video to the device, and the server sends the recipe and video data to the user's device as an HTTP response.

[0069] Step 9:

[0070] The device displays the received data to the user, and the device application displays the recipe and cooking process video in an easy-to-understand manner for the user, who can refer to it while cooking.

[0071] Step 10:

[0072] If necessary, the device can project cooking process videos onto the wall via smart home devices, allowing users to check the cooking steps while keeping their hands free.

[0073] By using the above process steps, users can efficiently plan their menu and prepare nutritionally balanced meals while referring to detailed cooking instructions. Even if ingredients are in short supply, they can be quickly ordered, reducing the hassle of cooking.

[0074] Example 1

[0075] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0076] With conventional cooking assistance systems, it was difficult for users to effectively use the ingredients in their refrigerator and efficiently prepare nutritionally balanced meals. Furthermore, if an ingredient was insufficient, it was time-consuming to purchase additional ingredients, which was inconvenient for users. Furthermore, the cooking steps could be difficult to understand, which also created an issue of cooking not proceeding smoothly.

[0077] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0078] In this invention, the server includes a means for taking photos of ingredients, a means for converting the photos of ingredients into a data format and sending them to the server, a means for analyzing the types and quantities of ingredients on the server, a means for generating a nutritionally balanced recipe based on the analysis results, a means for generating a video of the cooking process based on the generated recipe, a means for identifying missing ingredients and ordering additional ingredients in cooperation with an external e-commerce system, and a means for returning the generated recipe and cooking process video to the terminal. This allows the user to easily prepare a nutritionally balanced meal using ingredients in the refrigerator and efficiently purchase missing ingredients. Furthermore, the video providing detailed instructions on the cooking process allows the user to smoothly proceed with the cooking process.

[0079] "Means for taking photos of ingredients" refers to equipment or functions for capturing digital images of ingredients in the refrigerator.

[0080] "Means for converting photographs of ingredients into a data format and sending it to a server" refers to devices or functions for converting acquired photos of ingredients into structured data such as JSON format and sending that data to a server via the Internet.

[0081] "Means for analyzing the type and amount of ingredients on the server" refers to image recognition algorithms or software that analyzes image data received by the server and identifies the specific type and amount of ingredients.

[0082] "Means for generating recipes that take nutritional balance into consideration based on analysis results" refers to algorithms and software for generating optimal recipes that take nutritional balance into consideration based on identified ingredient information.

[0083] "Means for generating cooking process videos based on the generated recipe" refers to equipment or software that acquires and edits video clips to create a continuous cooking process video in order to provide cooking steps corresponding to the generated recipe in video format.

[0084] "Means of identifying missing ingredients and linking with an external e-commerce system to order additional ingredients" refers to a function that identifies ingredients that the user does not have and links with the e-commerce platform to complete the purchase process for the missing ingredients.

[0085] "Means for returning the generated recipe and cooking process video to the terminal" refers to the function for sending the recipe and cooking process video generated by the server to the user's terminal and displaying that information.

[0086] "Smart home appliances" refer to electronic devices that are connected to a network and provide various functions through voice control, touch control, etc.

[0087] A "structured data format" is a data format that organizes information in a specified format and makes it easy for machines to process. JSON format is a common example.

[0088] MODE FOR CARRYING OUT THE INVENTION

[0089] The present invention is a system that takes photos of ingredients in a refrigerator, analyzes the image data, generates suitable dish candidates and recipes, and provides videos of the cooking process. Specific embodiments of the system are described below.

[0090] First, a dedicated application is installed on a user's smartphone, tablet, or other device. The user uses this application to take photos of the ingredients in the refrigerator. For example, the user opens the refrigerator and takes photos of "tomatoes," "chicken," and "cheese." Once the photos are taken, the device automatically converts the photo data into JSON format and sends it to a server via the Internet.

[0091] The photo data received by the server is first analyzed using an image analysis algorithm. This analysis uses common image recognition software. Specifically, the server identifies the type and quantity of ingredients. For example, specific data such as "3 tomatoes," "500g of chicken," and "200g of cheese" is extracted.

[0092] Based on the identified ingredient information, the server uses a recipe generation algorithm that takes nutritional balance into account to generate optimal dish candidates and recipes. This process utilizes a generative AI model. Specifically, models such as OpenAI are used. This algorithm generates recipes taking into account the user's usage history and the freshness of the ingredients. For example, a recipe for "chicken and tomato pasta" is generated.

[0093] Based on the generated recipe, the server generates a video of the cooking process. The server retrieves video clips corresponding to each cooking step from the database and combines them to create a continuous cooking process video. This process is performed using video editing software such as Adobe Premiere Pro. The video includes detailed explanations of each cooking step, a timer function, and audio narration.

[0094] Next, the server checks the list of all ingredients required based on the generated recipe. If any ingredients that the user does not have are identified, the server connects the list to an external e-commerce system and provides the user with options for ordering additional ingredients. For example, it uses online shopping platforms such as Amazon Fresh or Rakuten Ichiba. This allows the user to easily purchase the missing ingredients.

[0095] Finally, the server sends the generated recipe and cooking process video back to the device. The device receives this data and displays it on the application screen. The user then proceeds with the cooking process based on the displayed information. Furthermore, users of smart home appliances can project the cooking process video from the device screen onto the wall. This function frees up the user's hands and makes cooking more efficient.

[0096] For example, if a user takes a photo of "tomatoes," "chicken," and "cheese" in the refrigerator, the server analyzes this information and generates a recipe for "chicken and tomato pasta." Based on the recipe, the server then creates a video that includes instructions on how to bake chicken, how to cut tomatoes, how to boil pasta, and other steps, providing detailed step-by-step instructions that will help the user as they cook.

[0097] The following are examples of specific prompt sentences:

[0098] Take a photo of "tomatoes," "chicken," and "cheese" in your refrigerator and generate a recipe and cooking video using these ingredients.

[0099] With the above configuration, the present invention is a system that allows users to efficiently plan menus and easily prepare nutritionally balanced meals. In addition, the system's ability to order ingredients in short supply reduces the risk of abandoning a cooking project due to lack of necessary ingredients.

[0100] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0101] Step 1:

[0102] The user takes a photo of the ingredients in the refrigerator using a smartphone or tablet. The user then launches the application and takes a photo of an ingredient, such as "tomato," "chicken," or "cheese." The input is the photo of the ingredient, and the output is a digital image file.

[0103] Step 2:

[0104] The device automatically converts photos taken into JSON format. Specifically, an image analysis module in the application reads the photos and converts them into a format that can be processed as digital data. The input is a digital image file, and the output is JSON-formatted data.

[0105] Step 3:

[0106] The device sends the converted JSON-formatted photo data to the server via the Internet. This operation is performed through a communication module in the application. The input is JSON-formatted data, and the output is a data stream sent to the server.

[0107] Step 4:

[0108] The server analyzes the received photo data. This analysis is performed using an image recognition algorithm. The server uses image recognition tools such as Amazon Rekognition or Google Cloud Vision to identify the type and quantity of ingredients. The input is JSON-formatted photo data, and the output is ingredient information (e.g., "3 tomatoes," "500g chicken," and "200g cheese").

[0109] Step 5:

[0110] The server generates a recipe that takes nutritional balance into consideration based on the identified ingredient information. A generative AI model is used here. The server receives ingredient information as input and uses a generative AI model such as OpenAI to generate nutritionally balanced recipe candidates. The input is ingredient information, and the output is recipe data (e.g., a recipe for "chicken and tomato pasta").

[0111] Step 6:

[0112] The server generates a cooking process video based on the generated recipe. The server retrieves video clips corresponding to each cooking step from the database and connects them using video editing software (e.g., Adobe Premiere Pro) to create a continuous cooking process video. The input is recipe data, and the output is a cooking process video.

[0113] Step 7:

[0114] The server checks the list of all required ingredients based on the generated recipe. The server lists the ingredients included in the recipe and identifies any ingredients that the user does not currently have. The input is the recipe data and the user's ingredient information, and the output is the list of missing ingredients.

[0115] Step 8:

[0116] The server connects the list of ingredients in short supply to an external e-commerce system and provides users with options for ordering more. Specifically, it connects with Amazon Fresh, Rakuten Ichiba, and other systems to provide links and interfaces for purchasing ingredients in short supply. The input is the list of ingredients in short supply, and the output is external links and purchasing options.

[0117] Step 9:

[0118] The server returns the generated recipe and cooking process video to the device. The server then sends this data to the device via the Internet, and the device displays the received data on the application screen. The input is the recipe data and cooking process video data, and the output is a data stream to the device.

[0119] Step 10:

[0120] The user proceeds with cooking based on the displayed information. The user cooks while referring to the recipe and video displayed on the application. If necessary, the cooking process video can be projected onto the wall using smart home appliances. This process allows the user to cook efficiently while keeping their hands free. The input is the recipe and video displayed on the device, and the output is the finished dish.

[0121] (Application example 1)

[0122] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0123] In recent years, there has been a demand for easy preparation of nutritionally balanced meals. There is also a need for systems that can determine the amount of a certain ingredient in the refrigerator, suggest appropriate recipes based on that information, and provide easy-to-understand cooking instructions. However, current systems do not fully integrate these functions, and lack the support users need to easily prepare nutritionally balanced meals. Furthermore, there are few systems that can automatically order missing ingredients all at once. In addition, providing an environment in which both hands can be used freely when actually following the cooking instructions is an important challenge.

[0124] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0125] In this invention, the server includes a means for capturing images of ingredients, a means for transmitting the captured images of ingredients to the server, a means for analyzing the types and quantities of ingredients on the server, a means for generating cooking instructions that take nutritional balance into consideration based on the analysis results, a means for generating a video of the cooking process based on the generated cooking instructions, a means for identifying missing ingredients and ordering additional ingredients in cooperation with an external system, a means for returning the generated cooking instructions and the video of the cooking process to the terminal, a means for displaying the analyzed ingredients and the generated cooking instructions on the smart device, and a means for displaying the video of the cooking process. This allows users to effectively use the ingredients in their refrigerator and easily prepare nutritionally balanced meals. Furthermore, the automatic ordering of missing ingredients and the display and projection of video of the cooking process on the smart device improve convenience during cooking.

[0126] "Means for taking pictures of ingredients" refers to devices or applications that allow users to easily take pictures of ingredients in the refrigerator.

[0127] "Means for transmitting photographed images of ingredients to a server" refers to a mechanism for transmitting photographed image data from a user's device to a server via the Internet.

[0128] "Means for analyzing the types and amounts of ingredients on the server" refers to an algorithm or program that analyzes the received image data and automatically identifies and measures the types and amounts of ingredients contained therein.

[0129] "Means for generating cooking instructions that take nutritional balance into consideration based on the analysis results" refers to an algorithm that automatically generates nutritionally balanced recipes based on analyzed ingredient information.

[0130] "Means for generating a video of the cooking process based on the generated cooking instructions" refers to a function for providing each step of the generated recipe as a video, and refers to a mechanism for combining the necessary video clips and presenting them in a form that is easy for the user to understand.

[0131] "Means of identifying missing ingredients and linking with external systems to order additional ingredients" refers to a system that identifies missing ingredients from the information on ingredients the user has on hand and links with external shopping systems or food delivery systems to place additional orders.

[0132] "Means for returning the generated cooking instructions and cooking process video to the terminal" refers to a mechanism for sending the generated recipe information and cooking process video data to the user's device and displaying them.

[0133] "Means for displaying analyzed ingredients and generated cooking instructions via a smart device" refers to the function for displaying the analysis results and generated recipes on a smart device such as a smartphone or tablet.

[0134] "Means for displaying cooking process videos" refers to a function that plays videos of cooking steps on the user's device to make them visually easy to understand.

[0135] This invention is a system that takes photos of ingredients in the refrigerator, analyzes the image data, generates suitable dish suggestions and recipes, and provides videos of the cooking process. This system combines ingredient management and cooking support, allowing users to easily prepare nutritionally balanced meals.

[0136] First, a dedicated application is installed on the user's device (such as a smartphone or tablet). Using this application, the user takes a photo of the ingredients in the refrigerator. The device reads the photo data, converts it into JSON format, and sends it to a server via the Internet.

[0137] The server analyzes the received photo data. Specifically, it uses an image analysis algorithm to identify the types and amounts of ingredients contained in the photo. This analysis is performed using a generative AI model, which is trained on images of many different ingredients as initial learning data. The analyzed ingredient information is then input into a recipe generation algorithm that takes nutritional balance into account. This algorithm also uses a generative AI model to generate optimal recipe suggestions based on the user's preferences and health condition.

[0138] Based on the generated recipe, the server generates a cooking process video. This process involves retrieving video clips corresponding to each cooking step from the database and combining them appropriately to create a continuous cooking process video. The video includes detailed instructions for each cooking step, a timer function, and audio narration.

[0139] The server then checks the list of all ingredients required based on the generated recipe. If any ingredients are missing, the server connects the list to an external shopping system and provides the user with options for ordering additional ingredients. In this case, the user can easily order additional ingredients by connecting to a shopping app.

[0140] Finally, the server sends the generated recipe and cooking process video back to the user's device and displays it in the application. The user can use the displayed information to cook efficiently. In addition, users with smart home devices can project the cooking process video onto a wall from the application screen. This function frees up both hands, making cooking even more efficient.

[0141] For example, if a user takes a photo of "tomatoes," "chicken," and "cheese" in the refrigerator, the server analyzes these ingredients and generates a recipe for "chicken and tomato pasta." Based on that recipe, the server also creates a video including instructions on how to grill chicken, how to cut tomatoes, how to boil pasta, and so on, and provides it to the user.

[0142] An example prompt is:

[0143] Please analyze the following image to identify the food items and quantities present. Return a JSON object with the list of identified food items and their quantities.

[0144] With the above configuration, the present invention allows users to efficiently plan menus and easily prepare nutritionally balanced meals. In addition, the lack of ingredients ordering function reduces the problem of abandoning a cooking project due to lack of necessary ingredients.

[0145] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0146] Step 1:

[0147] Users use devices such as smartphones and tablets to take pictures of the food items in their refrigerators, which are then saved on the device.

[0148] Step 2:

[0149] The device converts the captured image into JSON format and sends it to a server via the Internet. The input is image data, and the output is JSON format data.

[0150] Step 3:

[0151] The server analyzes the received JSON formatted image data. It uses an image analysis algorithm to identify the types and quantities of ingredients in the image. The input is JSON formatted image data, and the output is a list of the types and quantities of ingredients.

[0152] Step 4:

[0153] The server generates cooking instructions that take nutritional balance into consideration based on the identified ingredient information. Using a generative AI model, it automatically generates optimal recipes that match the user's preferences and health status. The input is ingredient information, and the output is cooking instructions (recipe).

[0154] Step 5:

[0155] Based on the cooking instructions, the server retrieves video clips of each cooking step from the database and creates a continuous cooking process video. The input is the cooking instructions, and the output is the cooking process video.

[0156] Step 6:

[0157] The server checks the list of all necessary ingredients based on the generated recipe. It identifies missing ingredients and connects with an external shopping system to provide the user with options for ordering additional ingredients. The input is the identified ingredient information and the recipe, and the output is the list of missing ingredients and a shopping link.

[0158] Step 7:

[0159] The server generates cooking instructions and video of the cooking process and returns them to the user's terminal. The input is cooking instructions and video of the cooking process, and the output is reply data containing these data.

[0160] Step 8:

[0161] The cooking instructions and cooking process video received by the device are displayed on the application screen. The user can proceed with cooking based on the displayed information. The input is data from the server, and the output is the screen display that the user can see.

[0162] Step 9:

[0163] Users can also use smart home devices to project cooking process videos onto the wall. This function transmits data from the terminal to the smart home device to perform video projection. The input is data from the terminal, and the output is the projected video.

[0164] The above are the specific processing steps of this system.

[0165] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0166] The present invention is a system that takes photos of ingredients in a refrigerator, analyzes the image data to generate suitable dish candidates and recipes, and further provides videos of the cooking process, by combining this with an emotion engine to provide customized support according to the user's emotional state. Detailed embodiments of the system are described below.

[0167] First, the user installs a dedicated application on their smartphone, tablet, or other device. Using this application, the user takes a photo of the ingredients in the refrigerator. Once the photo is taken, the device automatically converts the photo data into JSON format and sends it to a server via the Internet.

[0168] The photo data received by the server is analyzed using an image analysis algorithm to identify the types and amounts of ingredients. The identified ingredient information is then input into a recipe generation algorithm that takes nutritional balance into consideration, and the server generates the best possible dishes and recipes for the user.

[0169] Based on the generated recipe, the server generates a cooking process video. It retrieves video clips corresponding to each cooking step from the database and combines them to create a continuous cooking process video. This video includes audio commentary and a timer function.

[0170] Next, the server checks the list of all ingredients required based on the generated recipe. If any ingredients that the user does not have are identified, the server connects the list to an external shopping app and provides the user with the option to order more. The user can easily purchase the missing ingredients through the shopping app.

[0171] The server sends the generated recipe and cooking process video back to the device. The device receives this data and displays it on the application screen. The user proceeds with the cooking process based on the displayed information. In addition, users of smart home devices can project the cooking process video from the device screen onto the wall.

[0172] A notable feature of this invention is its incorporation of an emotion engine. The emotion engine recognizes the user's facial expressions and vocal tone to measure their emotional state (e.g., joy, sadness, stress). This emotion data is sent to the server and reflected in the criteria for recipe generation. For example, if the user is feeling stressed, the system will prioritize suggestions for dishes that have a relaxing effect or are easy to prepare.

[0173] The cooking process videos are also customized based on the user's emotional data. If the user is feeling anxious, the voice narration in the video will be changed to a more polite and calm tone. The color and font of the screen display can also be adjusted according to the user's emotional state.

[0174] For example, if a user takes a photo of "tomatoes," "chicken," and "cheese" in the refrigerator, the server analyzes the photo and generates a recipe for "chicken and tomato pasta." If the emotion engine recognizes that the user is tired, it will also suggest an easy-to-make "tomato salad." The voice narration for the generated video will also be set to a gentler tone than usual.

[0175] As described above, the present invention is a system that takes into account the user's emotional state, efficiently determines menus, and allows users to prepare nutritionally balanced meals while referring to detailed cooking instructions. Furthermore, the ordering function for missing ingredients reduces the problem of abandoning cooking due to lack of necessary ingredients. The introduction of an emotion engine makes it possible to provide cooking support that is more tailored to each individual user, reducing stress and increasing satisfaction.

[0176] The processing flow will be explained below.

[0177] Step 1:

[0178] The user takes a photo of the ingredients in the refrigerator. The user takes a photo of the ingredients using the camera on their smartphone or tablet, and the image data is saved by a dedicated application.

[0179] Step 2:

[0180] The device converts the captured photo data into JSON format and sends it to the server. The device then sends the photo data to the server via the Internet as an HTTP request.

[0181] Step 3:

[0182] The server analyzes the received photo data. After the server receives the photo data, it launches an image analysis algorithm to identify the type and amount of ingredients.

[0183] Step 4:

[0184] The server generates a recipe based on the analysis results, selecting and generating a nutritionally balanced recipe suitable for the identified ingredients from a recipe database stored on the server.

[0185] Step 5:

[0186] The server generates a cooking process video. Based on the generated recipe, the server combines video clips of each cooking step to create a detailed cooking process video. This video also includes audio commentary and a timer function.

[0187] Step 6:

[0188] The device uses an emotion engine to recognize the user's facial expressions and voice tone, and sends the emotion data to the server. The emotion engine determines the user's emotional state from the user's facial expressions, and the device then sends that data to the server.

[0189] Step 7:

[0190] The server customizes recipes and cooking steps based on the user's emotional data. The server adjusts recipe selection criteria based on the user's emotional state. For example, if the user is feeling stressed, it will prioritize dishes that have a relaxing effect or are easy to make.

[0191] Step 8:

[0192] The server customizes cooking process videos based on emotional data. If the user feels anxious, the voice narration in the video will be changed to a more polite and calm tone. The color and font of the screen display will also be adjusted.

[0193] Step 9:

[0194] The server identifies missing ingredients. The server checks the list of ingredients required for the generated recipe and compares it with the list of ingredients the user already has to identify missing ingredients.

[0195] Step 10:

[0196] The server provides a function to order missing ingredients. The server connects to an external shopping app and provides users with a link or option to order missing ingredients.

[0197] Step 11:

[0198] The server returns the generated recipe and cooking process video to the device, and the server sends the recipe and video data to the user's device as an HTTP response.

[0199] Step 12:

[0200] The device displays the received data to the user, and the device application displays the recipe and cooking process video in an easy-to-understand manner for the user, who can refer to it while cooking.

[0201] Step 13:

[0202] If necessary, the device can project cooking process videos onto the wall via smart home devices, allowing users to check the cooking process while keeping their hands free.

[0203] Through these processing steps, users can efficiently plan their menu and prepare nutritionally balanced meals while referring to detailed cooking instructions. Even if ingredients are in short supply, they can be quickly ordered, reducing the hassle of cooking. Furthermore, by incorporating an emotion engine, the system can provide customized cooking support tailored to each individual user's condition, reducing stress and increasing satisfaction.

[0204] Example 2

[0205] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0206] Conventional cooking support systems lack functionality to improve user satisfaction, as they are not adequately customized to take into account the user's emotional state. Furthermore, they have limited functionality for identifying missing ingredients and linking with external systems to order additional ingredients, making it difficult for users to easily obtain the ingredients they need. Furthermore, generating and displaying cooking procedure videos has also been an issue in some environments.

[0207] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0208] In this invention, the server includes means for taking photos of ingredients, means for converting the photos of ingredients into a data format and sending the data to the server, means for analyzing the types and amounts of ingredients on the server, means for generating a recipe that takes nutritional balance into consideration based on the analysis results, means for generating a video of cooking steps based on the generated recipe, means for identifying missing ingredients and ordering additional ingredients in cooperation with an external system, means for returning the generated recipe and video of cooking steps to the terminal, and means for recognizing the user's emotional state using an emotion engine and customizing the recipe and video of cooking steps based on the recognized emotional state. This makes it possible to propose recipes and provide video of cooking steps that are customized according to the user's emotional state, making it easy to obtain the necessary ingredients and check the cooking steps.

[0209] The "means for taking photos of ingredients" is a mechanism for acquiring images of ingredients in the refrigerator using the camera of the user's device.

[0210] The "means of converting into a data format and sending to a server" refers to a mechanism for converting the image data of photographed ingredients into a standard data format such as JSON and sending it to a server via the Internet.

[0211] "Means for analyzing the type and amount of ingredients on the server" refers to a system that uses an algorithm (e.g., YOLO) to analyze photo data sent to the server and identify the type and amount of ingredients in the photo.

[0212] The "means for generating recipes that take into account nutritional balance" is an algorithm for generating optimal recipes that take into account the user's health and nutritional balance based on analyzed ingredient information.

[0213] The "means for generating video of cooking steps" is a mechanism for providing visual instructions to the user by assembling video clips showing the necessary cooking steps based on the recipe.

[0214] "Means to identify missing ingredients and order additional ingredients by connecting with an external system" refers to a system that checks a list of all necessary ingredients based on the generated recipe, identifies missing ingredients that the user does not have, and connects with an external shopping system, etc., to make it possible to purchase additional ingredients.

[0215] "Means for returning the generated recipe and cooking procedure video to the terminal" refers to a mechanism by which the server sends the generated recipe and video to the user's terminal, allowing the user to easily access them.

[0216] "Means for recognizing a user's emotional state using an emotion engine and customizing recipe and cooking procedure videos based on the recognized emotional state" refers to an engine that analyzes a user's facial expressions and voice tone to identify their emotional state and provides recipes, narration, and display formats that are optimal for that emotional state.

[0217] MODE FOR CARRYING OUT THE INVENTION

[0218] The present invention is a system that takes pictures of ingredients in a refrigerator, analyzes the image data, generates suitable dish candidates and recipes according to the user's emotional state, and provides videos of the cooking process. Detailed embodiments of the system are described below.

[0219] First, the user installs a dedicated application on their smartphone, tablet, or other device. Using this application, the user takes a photo of the food in their refrigerator. Once the photo is taken, the device automatically converts the photo data into JSON format and sends it to a server via the Internet.

[0220] The server analyzes the received photo data using the image analysis algorithm "YOLO (You Only Look Once)." This identifies the ingredients in the photo. The identified ingredient information is then input into "Chef Watson," a recipe generation algorithm that takes nutritional balance into consideration, to generate optimal dish candidates and their recipes.

[0221] Based on the generated recipe, the server retrieves video clips corresponding to the cooking steps using the Vimeo API and combines them in the appropriate order to create a continuous cooking process video, which also includes audio commentary and a timer function.

[0222] Furthermore, the server checks the list of all ingredients required based on the generated recipe. If any ingredients are missing, the server connects to the shopping system using the Amazon API and provides the user with the option to order more. The user can then easily purchase the missing ingredients through the shopping system.

[0223] The server then sends the generated recipe and cooking process video back to the device. The device receives this data and displays it on the application screen. The user can proceed with cooking based on the displayed information. In addition, if the smart home device "Google Nest Hub" is used, the cooking process video can be projected onto the wall.

[0224] A notable feature of this invention is its incorporation of an emotion engine ("Affectiva SDK"). The emotion engine recognizes the user's facial expressions and vocal tone to measure their emotional state (e.g., joy, sadness, stress). This emotion data is sent to the server and reflected in recipe generation and customization of cooking process videos. For example, if the user is feeling stressed, dishes that have a relaxing effect or are easy to make will be prioritized. In addition, the audio narration of the generated video and the color and font of the screen display are also adjusted according to the user's emotional state.

[0225] For example, a user can take a photo of "tomatoes," "chicken," and "cheese" in their refrigerator, and the server will analyze the photos and generate a recipe for "chicken and tomato pasta." If the emotion engine recognizes that the user is tired, it will also suggest an easy-to-make "tomato salad." The generated video will also have a gentle voice narration.

[0226] An example prompt can be entered in the following text format:

[0227] "I have tomatoes and chicken in the fridge. I'm tired, so please suggest some easy meals."

[0228] This system allows users to obtain customized cooking recipes based on their emotional state, easily purchase the necessary ingredients, and enjoy cooking with reduced stress by viewing detailed cooking instruction videos.

[0229] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0230] Program processing steps

[0231] Step 1:

[0232] The user installs a dedicated application on their smartphone or tablet.

[0233] Input: None

[0234] Output: The application is installed on the device.

[0235] Specific operation: The user downloads a dedicated application from the app store and installs it on their device.

[0236] Step 2:

[0237] The user launches the application and takes a photo of the ingredients in the refrigerator.

[0238] Input: Food in the refrigerator

[0239] Output: Photograph of the ingredients

[0240] What happens: The user opens the app and uses the in-app camera to take a picture of the ingredients in the refrigerator.

[0241] Step 3:

[0242] The device converts the captured photo data into JSON format.

[0243] Input: A photo of the food

[0244] Output: Photo data in JSON format

[0245] What happens: The application internally parses the image data and converts the corresponding data into JSON format.

[0246] Step 4:

[0247] The terminal converts the data into JSON format and sends it to the server via the Internet.

[0248] Input: JSON format photo data

[0249] Output: Photo data sent to the server

[0250] What happens: The device uses an internet connection to upload the converted data to the server.

[0251] Step 5:

[0252] The photo data received by the server is analyzed using the image analysis algorithm "YOLO."

[0253] Input: JSON format photo data

[0254] Output: Type and amount of ingredients

[0255] What it does: The server uses the "YOLO" algorithm to analyze the image data sent and identify the types of ingredients in the photo and their quantities.

[0256] Step 6:

[0257] The server uses the "Chef Watson" algorithm based on the analysis results to generate recipes that take nutritional balance into consideration.

[0258] Input: Type and amount of ingredients

[0259] Output: Best food suggestions and recipes

[0260] Specific operation: The server uses "Chef Watson" to generate the optimal recipe based on the identified ingredient information.

[0261] Step 7:

[0262] The server uses an emotional engine ("Affectiva SDK") to recognize the user's emotional state and customize the recipe.

[0263] Input: User's facial expressions and voice tone

[0264] Output: Customized food suggestions and recipes based on sentiment data

[0265] Specific operation: The server uses the emotion engine to analyze the user's emotional state at the time of taking the photo and reflects the results in recipe generation.

[0266] Step 8:

[0267] The server generates a cooking procedure video based on the generated recipe.

[0268] Input: Customized Recipe

[0269] Output: Cooking video with any combination of steps

[0270] Specific operation: The server uses the Vimeo API or similar to obtain video clips corresponding to cooking steps, and then combines them in the appropriate order to generate a continuous video.

[0271] Step 9:

[0272] The server checks the list of ingredients needed, connects with an external shopping system to identify ingredients that are missing, and presents options for ordering more.

[0273] Input: Generated recipe and user's ingredients

[0274] Output: List of missing ingredients and options to order more

[0275] Specific operation: The server identifies the ingredients that are in short supply using the Amazon API or similar and gives the user the option to purchase more.

[0276] Step 10:

[0277] The server sends the final recipe and cooking instruction video to the device.

[0278] Input: Customized recipes and cooking instructions videos

[0279] Output: Recipe and video data sent to the device

[0280] Specific operation: The server sends the generated recipes and videos to the device, making them viewable in the application.

[0281] Step 11:

[0282] The data received by the device is displayed on the application screen, and if necessary, a video is projected onto the wall using a smart home device.

[0283] Input: Received recipe and cooking instructions video

[0284] Output: Recipes and videos displayed on the application screen or projected onto smart home devices

[0285] How it works: Users can view recipes and videos in the app and can also use smart home devices to project cooking instruction videos onto the wall.

[0286] This will enable the system to propose recipes and provide cooking procedure videos customized to the user's emotional state, allowing them to easily obtain the necessary ingredients and check the cooking procedures.

[0287] (Application example 2)

[0288] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0289] In today's society, preparing nutritiously balanced meals at home despite leading busy daily lives is a major challenge for many people. Users, especially those living in hectic schedules and stressful environments, often find it difficult to find the time and energy to cook. As a result, many resort to eating out or unhealthy eating habits. Furthermore, when ingredients are scarce, it can be a hassle to go shopping. Furthermore, the lack of appropriate recipe suggestions based on emotional state makes it difficult to improve user satisfaction.

[0290] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for analyzing the user's emotional state and proposing customized recipes and cooking methods according to the emotional state, means for generating recipes that take nutritional balance into consideration based on the analysis results, and means for generating videos of cooking steps based on the generated recipes. This enables personalized cooking suggestions that take into account the user's emotional state and stress level. As a result, the user can have a more relaxed and enjoyable cooking experience and be supported in maintaining a healthy diet. Furthermore, even if necessary ingredients are in short supply, additional ingredients can be quickly ordered in cooperation with an external app, reducing the effort required to obtain ingredients.

[0291] The "means for taking photos of ingredients" is a function for acquiring image data of ingredients in the refrigerator using an electronic device owned by the user.

[0292] The "means for transmitting the photographed foodstuffs to the server" is a communication function for transferring the acquired foodstuffs image data from the electronic device to the server.

[0293] The "means for analyzing the type and amount of ingredients on the server" refers to an algorithm and program for analyzing the image data of ingredients uploaded to the server and identifying the type and amount of ingredients.

[0294] The "means for generating recipes that take nutritional balance into consideration" is a function that generates nutritionally balanced cooking recipes based on analyzed ingredient data.

[0295] The "means for generating videos of cooking processes" is a function that creates videos showing cooking steps and methods based on the generated recipe.

[0296] "Means for identifying missing ingredients and ordering additional ingredients in cooperation with an external application" is a function that identifies ingredients that the user does not have and orders the missing ingredients through an external shopping application.

[0297] "Means for analyzing the user's emotional state and suggesting recipes and cooking methods according to the emotional state" refers to a function that analyzes the user's emotions from their facial expressions and voice, and customizes cooking suggestions and cooking methods based on the analysis results.

[0298] The "means for returning the generated recipe and cooking process video to the terminal" is a function for sending the recipe and cooking method video generated by the server to the user's electronic device.

[0299] The "means for setting video narration and display according to emotional state" is a function for changing the tone of video narration and the color of display based on the user's emotional state.

[0300] This invention is a system that uses an application installed on a user's device (such as a smartphone or tablet) to take photos of ingredients in a refrigerator, and then sends the data to a server for analysis. Specifically, the invention is implemented in the following steps.

[0301] First, the user uses their device to take a photo of the food in their refrigerator. The captured image data is converted into JSON format by an application on the device and sent to a server over the Internet, using standard Internet protocols.

[0302] The server then uses AI image recognition technology, such as Google Cloud Vision API, to analyze the received image data. This identifies the types and amounts of ingredients. The analysis results are then input into a recipe generation algorithm that takes nutritional balance into account, generating the best possible dishes and recipes for the user.

[0303] Based on the generated recipe, the server generates a cooking process video using the FFmpeg library, taking video clips corresponding to each cooking step and combining them to create a continuous cooking process video, which also includes audio commentary and a timer function.

[0304] In addition, the server uses the Emotion API from Microsoft Azure Cognitive Services to analyze the user's emotional state. The system analyzes the user's facial expressions and vocal tone to identify their emotional state. This emotional data is reflected in the criteria for recipe generation; if the user is feeling stressed, it will prioritize dishes that are relaxing or easy to make. The generated cooking process videos are also customized based on the emotional data. The voice narration changes depending on the user's emotional state, and the color and font of the screen display are also adjusted.

[0305] For example, if a user takes a photo of "tomatoes," "chicken," and "cheese" in the refrigerator, the server analyzes the photo and generates a recipe for "chicken and tomato pasta." If the emotion engine recognizes that the user is tired, it will also suggest an easy-to-make "tomato salad." The voice narration for the video is set to a gentler tone than usual to help users relax.

[0306] If any ingredients are identified as being in short supply, the server will share the list with an external shopping app and provide the user with the option to order more. The user can then easily purchase the missing ingredients through the shopping app.

[0307] In this way, a system is realized that provides a stress-free cooking experience that suits the user's emotions and supports a healthy diet.

[0308] Example prompt sentence:

[0309] "Use an emotion engine to determine the user's emotional state and generate prompts for a food delivery app that suggests relaxing recipes."

[0310] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0311] Step 1:

[0312] The user uses a device (smartphone or tablet) to take a photo of the ingredients in the refrigerator. The input is image data captured by the camera, and the output is an image file saved on the device. The device takes the photo in response to the user's operation and saves the image data.

[0313] Step 2:

[0314] The device converts the photographed food photo data into JSON format and sends it to the server via the Internet. The input is an image file, and the output is JSON format data sent to the server. The device converts the image data into a JSON object and sends it to the server via an HTTP request.

[0315] Step 3:

[0316] The server analyzes the received JSON-formatted photo data and identifies the type and quantity of ingredients. The input is JSON-formatted image data, and the output is the analyzed ingredient information. The server uses the Google Cloud Vision API to analyze the image and identify the type and quantity of ingredients.

[0317] Step 4:

[0318] The server generates a nutritionally balanced recipe based on the analyzed ingredient information. The input is ingredient information, and the output is the generated recipe. The server uses an internal algorithm to generate a nutritionally balanced recipe from the input ingredient information.

[0319] Step 5:

[0320] The server analyzes facial expressions and voice tones to determine the user's emotional state. The input is the user's face photo and voice data, and the output is the user's emotional state data. The server identifies the emotional state using the Emotion API of Microsoft Azure Cognitive Services.

[0321] Step 6:

[0322] The server considers the emotional state data and proposes customized recipes and cooking methods. The input is the emotional state data and ingredient information, and the output is a customized recipe proposal. The server generates recipes and cooking methods that reflect the results of the emotion analysis.

[0323] Step 7:

[0324] The server generates a cooking process video based on the generated recipe. The input is recipe information, and the output is a cooking process video. The server uses FFmpeg to combine video clips corresponding to each cooking step to generate a continuous cooking process video.

[0325] Step 8:

[0326] The server returns the generated cooking process video to the terminal. The input is the cooking process video, and the output is the video data sent to the user terminal. The server sends the generated video data to the terminal as an HTTP response.

[0327] Step 9:

[0328] The server identifies missing ingredients and orders additional ingredients by connecting with an external shopping app. The input is a list of ingredients required for the recipe, and the output is the order information. The server sends the list of missing ingredients via the external shopping app's API and processes the order.

[0329] Step 10:

[0330] The device displays the cooking process video received. The input is the received video data, and the output is the video played on the device screen. The device plays the received video data to provide the user with visual cooking support.

[0331] Step 11:

[0332] The device changes the tone of the video narration and the color of the screen display according to the user's emotional state. The input is emotional state data and video data, and the output is a customized video display. The device adjusts the narration and screen settings to match the user's emotional state, providing a more relaxing experience.

[0333] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0334] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0335] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0336] [Second embodiment]

[0337] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0338] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0339] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0340] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0341] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0342] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0343] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0344] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0345] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0346] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0347] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0348] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0349] The present invention is a system that takes photos of ingredients in a refrigerator, analyzes the image data, generates suitable dish options and recipes, and provides videos of the cooking process. Detailed embodiments of the system are described below.

[0350] First, a dedicated application is installed on a user's smartphone, tablet, or other device. The user uses this application to take a photo of the food in the refrigerator. Once the photo is taken, the device automatically converts the photo data into JSON format and sends it to a server via the Internet.

[0351] The photo data received by the server is first analyzed using an image analysis algorithm. During this process, the server identifies the types and amounts of ingredients. The identified ingredient information is then input into a recipe generation algorithm that takes nutritional balance into consideration. This allows the server to generate the best possible recipes for the user.

[0352] Based on the generated recipe, the server generates a video of the cooking process. The server retrieves video clips corresponding to each cooking step from the database and combines them to create a continuous video of the cooking process. This video includes detailed instructions for each cooking step, a timer function, and audio narration.

[0353] Next, the server checks the list of all necessary ingredients based on the generated recipe. If any ingredients that the user does not have are identified, the server connects the list to an external shopping app and provides the user with the option to order additional ingredients. The user can easily purchase the missing ingredients through the shopping app.

[0354] Finally, the server sends the generated recipe and cooking process video back to the device. The device receives this data and displays it on the application screen. The user then proceeds with the cooking process based on the displayed information. Furthermore, users of smart home devices can project the cooking process video from the device screen onto the wall. This function frees up the user's hands and allows for more efficient cooking.

[0355] For example, if a user takes a photo of "tomatoes," "chicken," and "cheese" in the refrigerator, the server analyzes this information and generates a recipe for "chicken and tomato pasta." Based on the recipe, the server then creates a video including instructions on how to bake chicken, how to cut tomatoes, how to boil pasta, and so on, providing detailed step-by-step instructions to help the user with the cooking process.

[0356] With the above configuration, the present invention is a system that allows users to efficiently plan menus and easily prepare nutritionally balanced meals. In addition, the system's ability to order ingredients in short supply reduces the risk of abandoning a cooking project due to lack of necessary ingredients.

[0357] The processing flow will be explained below.

[0358] Step 1:

[0359] The user takes a photo of the ingredients in the refrigerator. The user takes a photo of the ingredients using the camera on their smartphone or tablet, and the image data is saved by a dedicated application.

[0360] Step 2:

[0361] The device converts the captured photo data into JSON format and sends it to the server. The device then sends the photo data to the server via the Internet as an HTTP request.

[0362] Step 3:

[0363] The server analyzes the received photo data. After the server receives the photo data, it launches an image analysis algorithm to identify the type and amount of ingredients.

[0364] Step 4:

[0365] The server generates a recipe based on the analysis results, selecting and generating a nutritionally balanced recipe suitable for the identified ingredients from a recipe database stored on the server.

[0366] Step 5:

[0367] The server generates a cooking process video. Based on the generated recipe, the server combines video clips of each cooking step to create a detailed cooking process video. This video also includes audio commentary and a timer function.

[0368] Step 6:

[0369] The server identifies missing ingredients. The server checks the list of ingredients required for the generated recipe and compares it with the list of ingredients the user already has to identify missing ingredients.

[0370] Step 7:

[0371] The server provides a function to order missing ingredients. The server connects to an external shopping app and provides users with a link or option to order missing ingredients.

[0372] Step 8:

[0373] The server returns the generated recipe and cooking process video to the device, and the server sends the recipe and video data to the user's device as an HTTP response.

[0374] Step 9:

[0375] The device displays the received data to the user, and the device application displays the recipe and cooking process video in an easy-to-understand manner for the user, who can refer to it while cooking.

[0376] Step 10:

[0377] If necessary, the device can project cooking process videos onto the wall via smart home devices, allowing users to check the cooking steps while keeping their hands free.

[0378] By using the above process steps, users can efficiently plan their menu and prepare nutritionally balanced meals while referring to detailed cooking instructions. Even if ingredients are in short supply, they can be quickly ordered, reducing the hassle of cooking.

[0379] Example 1

[0380] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0381] With conventional cooking assistance systems, it was difficult for users to effectively use the ingredients in their refrigerator and efficiently prepare nutritionally balanced meals. Furthermore, if an ingredient was insufficient, it was time-consuming to purchase additional ingredients, which was inconvenient for users. Furthermore, the cooking steps could be difficult to understand, which also created an issue of cooking not proceeding smoothly.

[0382] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0383] In this invention, the server includes a means for taking photos of ingredients, a means for converting the photos of ingredients into a data format and sending them to the server, a means for analyzing the types and quantities of ingredients on the server, a means for generating a nutritionally balanced recipe based on the analysis results, a means for generating a video of the cooking process based on the generated recipe, a means for identifying missing ingredients and ordering additional ingredients in cooperation with an external e-commerce system, and a means for returning the generated recipe and cooking process video to the terminal. This allows the user to easily prepare a nutritionally balanced meal using ingredients in the refrigerator and efficiently purchase missing ingredients. Furthermore, the video providing detailed instructions on the cooking process allows the user to smoothly proceed with the cooking process.

[0384] "Means for taking photos of ingredients" refers to equipment or functions for capturing digital images of ingredients in the refrigerator.

[0385] "Means for converting photographs of ingredients into a data format and sending it to a server" refers to devices or functions for converting acquired photos of ingredients into structured data such as JSON format and sending that data to a server via the Internet.

[0386] "Means for analyzing the type and amount of ingredients on the server" refers to image recognition algorithms or software that analyzes image data received by the server and identifies the specific type and amount of ingredients.

[0387] "Means for generating recipes that take nutritional balance into consideration based on analysis results" refers to algorithms and software for generating optimal recipes that take nutritional balance into consideration based on identified ingredient information.

[0388] "Means for generating cooking process videos based on the generated recipe" refers to equipment or software that acquires and edits video clips to create a continuous cooking process video in order to provide cooking steps corresponding to the generated recipe in video format.

[0389] "Means of identifying missing ingredients and linking with an external e-commerce system to order additional ingredients" refers to a function that identifies ingredients that the user does not have and links with the e-commerce platform to complete the purchase process for the missing ingredients.

[0390] "Means for returning the generated recipe and cooking process video to the terminal" refers to the function for sending the recipe and cooking process video generated by the server to the user's terminal and displaying that information.

[0391] "Smart home appliances" refer to electronic devices that are connected to a network and provide various functions through voice control, touch control, etc.

[0392] A "structured data format" is a data format that organizes information in a specified format and makes it easy for machines to process. JSON format is a common example.

[0393] MODE FOR CARRYING OUT THE INVENTION

[0394] The present invention is a system that takes photos of ingredients in a refrigerator, analyzes the image data, generates suitable dish candidates and recipes, and provides videos of the cooking process. Specific embodiments of the system are described below.

[0395] First, a dedicated application is installed on a user's smartphone, tablet, or other device. The user uses this application to take photos of the ingredients in the refrigerator. For example, the user opens the refrigerator and takes photos of "tomatoes," "chicken," and "cheese." Once the photos are taken, the device automatically converts the photo data into JSON format and sends it to a server via the Internet.

[0396] The photo data received by the server is first analyzed using an image analysis algorithm. This analysis uses common image recognition software. Specifically, the server identifies the type and quantity of ingredients. For example, specific data such as "3 tomatoes," "500g of chicken," and "200g of cheese" is extracted.

[0397] Based on the identified ingredient information, the server uses a recipe generation algorithm that takes nutritional balance into account to generate optimal dish candidates and recipes. This process utilizes a generative AI model. Specifically, models such as OpenAI are used. This algorithm generates recipes taking into account the user's usage history and the freshness of the ingredients. For example, a recipe for "chicken and tomato pasta" is generated.

[0398] Based on the generated recipe, the server generates a video of the cooking process. The server retrieves video clips corresponding to each cooking step from the database and combines them to create a continuous cooking process video. This process is performed using video editing software such as Adobe Premiere Pro. The video includes detailed explanations of each cooking step, a timer function, and audio narration.

[0399] Next, the server checks the list of all ingredients required based on the generated recipe. If any ingredients that the user does not have are identified, the server connects the list to an external e-commerce system and provides the user with options for ordering additional ingredients. For example, it uses online shopping platforms such as Amazon Fresh or Rakuten Ichiba. This allows the user to easily purchase the missing ingredients.

[0400] Finally, the server sends the generated recipe and cooking process video back to the device. The device receives this data and displays it on the application screen. The user then proceeds with the cooking process based on the displayed information. Furthermore, users of smart home appliances can project the cooking process video from the device screen onto the wall. This function frees up the user's hands and makes cooking more efficient.

[0401] For example, if a user takes a photo of "tomatoes," "chicken," and "cheese" in the refrigerator, the server analyzes this information and generates a recipe for "chicken and tomato pasta." Based on the recipe, the server then creates a video that includes instructions on how to bake chicken, how to cut tomatoes, how to boil pasta, and other steps, providing detailed step-by-step instructions that will help the user as they cook.

[0402] The following are examples of specific prompt sentences:

[0403] Take a photo of "tomatoes," "chicken," and "cheese" in your refrigerator and generate a recipe and cooking video using these ingredients.

[0404] With the above configuration, the present invention is a system that allows users to efficiently plan menus and easily prepare nutritionally balanced meals. In addition, the system's ability to order ingredients in short supply reduces the risk of abandoning a cooking project due to lack of necessary ingredients.

[0405] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0406] Step 1:

[0407] The user takes a photo of the ingredients in the refrigerator using a smartphone or tablet. The user then launches the application and takes a photo of an ingredient, such as "tomato," "chicken," or "cheese." The input is the photo of the ingredient, and the output is a digital image file.

[0408] Step 2:

[0409] The device automatically converts photos taken into JSON format. Specifically, an image analysis module in the application reads the photos and converts them into a format that can be processed as digital data. The input is a digital image file, and the output is JSON-formatted data.

[0410] Step 3:

[0411] The device sends the converted JSON-formatted photo data to the server via the Internet. This operation is performed through a communication module in the application. The input is JSON-formatted data, and the output is a data stream sent to the server.

[0412] Step 4:

[0413] The server analyzes the received photo data. This analysis is performed using an image recognition algorithm. The server uses image recognition tools such as Amazon Rekognition or Google Cloud Vision to identify the type and quantity of ingredients. The input is JSON-formatted photo data, and the output is ingredient information (e.g., "3 tomatoes," "500g chicken," and "200g cheese").

[0414] Step 5:

[0415] The server generates a recipe that takes nutritional balance into consideration based on the identified ingredient information. A generative AI model is used here. The server receives ingredient information as input and uses a generative AI model such as OpenAI to generate nutritionally balanced recipe candidates. The input is ingredient information, and the output is recipe data (e.g., a recipe for "chicken and tomato pasta").

[0416] Step 6:

[0417] The server generates a cooking process video based on the generated recipe. The server retrieves video clips corresponding to each cooking step from the database and connects them using video editing software (e.g., Adobe Premiere Pro) to create a continuous cooking process video. The input is recipe data, and the output is a cooking process video.

[0418] Step 7:

[0419] The server checks the list of all required ingredients based on the generated recipe. The server lists the ingredients included in the recipe and identifies any ingredients that the user does not currently have. The input is the recipe data and the user's ingredient information, and the output is the list of missing ingredients.

[0420] Step 8:

[0421] The server connects the list of ingredients in short supply to an external e-commerce system and provides users with options for ordering more. Specifically, it connects with Amazon Fresh, Rakuten Ichiba, and other systems to provide links and interfaces for purchasing ingredients in short supply. The input is the list of ingredients in short supply, and the output is external links and purchasing options.

[0422] Step 9:

[0423] The server returns the generated recipe and cooking process video to the device. The server then sends this data to the device via the Internet, and the device displays the received data on the application screen. The input is the recipe data and cooking process video data, and the output is a data stream to the device.

[0424] Step 10:

[0425] The user proceeds with cooking based on the displayed information. The user cooks while referring to the recipe and video displayed on the application. If necessary, the cooking process video can be projected onto the wall using smart home appliances. This process allows the user to cook efficiently while keeping their hands free. The input is the recipe and video displayed on the device, and the output is the finished dish.

[0426] (Application example 1)

[0427] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0428] In recent years, there has been a demand for easy preparation of nutritionally balanced meals. There is also a need for systems that can determine the amount of a certain ingredient in the refrigerator, suggest appropriate recipes based on that information, and provide easy-to-understand cooking instructions. However, current systems do not fully integrate these functions, and lack the support users need to easily prepare nutritionally balanced meals. Furthermore, there are few systems that can automatically order missing ingredients all at once. In addition, providing an environment in which both hands can be used freely when actually following the cooking instructions is an important challenge.

[0429] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0430] In this invention, the server includes a means for capturing images of ingredients, a means for transmitting the captured images of ingredients to the server, a means for analyzing the types and quantities of ingredients on the server, a means for generating cooking instructions that take nutritional balance into consideration based on the analysis results, a means for generating a video of the cooking process based on the generated cooking instructions, a means for identifying missing ingredients and ordering additional ingredients in cooperation with an external system, a means for returning the generated cooking instructions and the video of the cooking process to the terminal, a means for displaying the analyzed ingredients and the generated cooking instructions on the smart device, and a means for displaying the video of the cooking process. This allows users to effectively use the ingredients in their refrigerator and easily prepare nutritionally balanced meals. Furthermore, the automatic ordering of missing ingredients and the display and projection of video of the cooking process on the smart device improve convenience during cooking.

[0431] "Means for taking pictures of ingredients" refers to devices or applications that allow users to easily take pictures of ingredients in the refrigerator.

[0432] "Means for transmitting photographed images of ingredients to a server" refers to a mechanism for transmitting photographed image data from a user's device to a server via the Internet.

[0433] "Means for analyzing the types and amounts of ingredients on the server" refers to an algorithm or program that analyzes the received image data and automatically identifies and measures the types and amounts of ingredients contained therein.

[0434] "Means for generating cooking instructions that take nutritional balance into consideration based on the analysis results" refers to an algorithm that automatically generates nutritionally balanced recipes based on analyzed ingredient information.

[0435] "Means for generating a video of the cooking process based on the generated cooking instructions" refers to a function for providing each step of the generated recipe as a video, and refers to a mechanism for combining the necessary video clips and presenting them in a form that is easy for the user to understand.

[0436] "Means of identifying missing ingredients and linking with external systems to order additional ingredients" refers to a system that identifies missing ingredients from the information on ingredients the user has on hand and links with external shopping systems or food delivery systems to place additional orders.

[0437] "Means for returning the generated cooking instructions and cooking process video to the terminal" refers to a mechanism for sending the generated recipe information and cooking process video data to the user's device and displaying them.

[0438] "Means for displaying analyzed ingredients and generated cooking instructions via a smart device" refers to the function for displaying the analysis results and generated recipes on a smart device such as a smartphone or tablet.

[0439] "Means for displaying cooking process videos" refers to a function that plays videos of cooking steps on the user's device to make them visually easy to understand.

[0440] This invention is a system that takes photos of ingredients in the refrigerator, analyzes the image data, generates suitable dish suggestions and recipes, and provides videos of the cooking process. This system combines ingredient management and cooking support, allowing users to easily prepare nutritionally balanced meals.

[0441] First, a dedicated application is installed on the user's device (such as a smartphone or tablet). Using this application, the user takes a photo of the ingredients in the refrigerator. The device reads the photo data, converts it into JSON format, and sends it to a server via the Internet.

[0442] The server analyzes the received photo data. Specifically, it uses an image analysis algorithm to identify the types and amounts of ingredients contained in the photo. This analysis is performed using a generative AI model, which is trained on images of many different ingredients as initial learning data. The analyzed ingredient information is then input into a recipe generation algorithm that takes nutritional balance into account. This algorithm also uses a generative AI model to generate optimal recipe suggestions based on the user's preferences and health condition.

[0443] Based on the generated recipe, the server generates a cooking process video. This process involves retrieving video clips corresponding to each cooking step from the database and combining them appropriately to create a continuous cooking process video. The video includes detailed instructions for each cooking step, a timer function, and audio narration.

[0444] The server then checks the list of all ingredients required based on the generated recipe. If any ingredients are missing, the server connects the list to an external shopping system and provides the user with options for ordering additional ingredients. In this case, the user can easily order additional ingredients by connecting to a shopping app.

[0445] Finally, the server sends the generated recipe and cooking process video back to the user's device and displays it in the application. The user can use the displayed information to cook efficiently. In addition, users with smart home devices can project the cooking process video onto a wall from the application screen. This function frees up both hands, making cooking even more efficient.

[0446] For example, if a user takes a photo of "tomatoes," "chicken," and "cheese" in the refrigerator, the server analyzes these ingredients and generates a recipe for "chicken and tomato pasta." Based on that recipe, the server also creates a video including instructions on how to grill chicken, how to cut tomatoes, how to boil pasta, and so on, and provides it to the user.

[0447] An example prompt is:

[0448] Please analyze the following image to identify the food items and quantities present. Return a JSON object with the list of identified food items and their quantities.

[0449] With the above configuration, the present invention allows users to efficiently plan menus and easily prepare nutritionally balanced meals. In addition, the lack of ingredients ordering function reduces the problem of abandoning a cooking project due to lack of necessary ingredients.

[0450] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0451] Step 1:

[0452] Users use devices such as smartphones and tablets to take pictures of the food items in their refrigerators, which are then saved on the device.

[0453] Step 2:

[0454] The device converts the captured image into JSON format and sends it to a server via the Internet. The input is image data, and the output is JSON format data.

[0455] Step 3:

[0456] The server analyzes the received JSON formatted image data. It uses an image analysis algorithm to identify the types and quantities of ingredients in the image. The input is JSON formatted image data, and the output is a list of the types and quantities of ingredients.

[0457] Step 4:

[0458] The server generates cooking instructions that take nutritional balance into consideration based on the identified ingredient information. Using a generative AI model, it automatically generates optimal recipes that match the user's preferences and health status. The input is ingredient information, and the output is cooking instructions (recipe).

[0459] Step 5:

[0460] Based on the cooking instructions, the server retrieves video clips of each cooking step from the database and creates a continuous cooking process video. The input is the cooking instructions, and the output is the cooking process video.

[0461] Step 6:

[0462] The server checks the list of all necessary ingredients based on the generated recipe. It identifies missing ingredients and connects with an external shopping system to provide the user with options for ordering additional ingredients. The input is the identified ingredient information and the recipe, and the output is the list of missing ingredients and a shopping link.

[0463] Step 7:

[0464] The server generates cooking instructions and video of the cooking process and returns them to the user's terminal. The input is cooking instructions and video of the cooking process, and the output is reply data containing these data.

[0465] Step 8:

[0466] The cooking instructions and cooking process video received by the device are displayed on the application screen. The user can proceed with cooking based on the displayed information. The input is data from the server, and the output is the screen display that the user can see.

[0467] Step 9:

[0468] Users can also use smart home devices to project cooking process videos onto the wall. This function transmits data from the terminal to the smart home device to perform video projection. The input is data from the terminal, and the output is the projected video.

[0469] The above are the specific processing steps of this system.

[0470] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0471] The present invention is a system that takes photos of ingredients in a refrigerator, analyzes the image data to generate suitable dish candidates and recipes, and further provides videos of the cooking process, by combining this with an emotion engine to provide customized support according to the user's emotional state. Detailed embodiments of the system are described below.

[0472] First, the user installs a dedicated application on their smartphone, tablet, or other device. Using this application, the user takes a photo of the ingredients in the refrigerator. Once the photo is taken, the device automatically converts the photo data into JSON format and sends it to a server via the Internet.

[0473] The photo data received by the server is analyzed using an image analysis algorithm to identify the types and amounts of ingredients. The identified ingredient information is then input into a recipe generation algorithm that takes nutritional balance into consideration, and the server generates the best possible dishes and recipes for the user.

[0474] Based on the generated recipe, the server generates a cooking process video. It retrieves video clips corresponding to each cooking step from the database and combines them to create a continuous cooking process video. This video includes audio commentary and a timer function.

[0475] Next, the server checks the list of all ingredients required based on the generated recipe. If any ingredients that the user does not have are identified, the server connects the list to an external shopping app and provides the user with the option to order more. The user can easily purchase the missing ingredients through the shopping app.

[0476] The server sends the generated recipe and cooking process video back to the device. The device receives this data and displays it on the application screen. The user proceeds with the cooking process based on the displayed information. In addition, users of smart home devices can project the cooking process video from the device screen onto the wall.

[0477] A notable feature of this invention is its incorporation of an emotion engine. The emotion engine recognizes the user's facial expressions and vocal tone to measure their emotional state (e.g., joy, sadness, stress). This emotion data is sent to the server and reflected in the criteria for recipe generation. For example, if the user is feeling stressed, the system will prioritize suggestions for dishes that have a relaxing effect or are easy to prepare.

[0478] The cooking process videos are also customized based on the user's emotional data. If the user is feeling anxious, the voice narration in the video will be changed to a more polite and calm tone. The color and font of the screen display can also be adjusted according to the user's emotional state.

[0479] For example, if a user takes a photo of "tomatoes," "chicken," and "cheese" in the refrigerator, the server analyzes the photo and generates a recipe for "chicken and tomato pasta." If the emotion engine recognizes that the user is tired, it will also suggest an easy-to-make "tomato salad." The voice narration for the generated video will also be set to a gentler tone than usual.

[0480] As described above, the present invention is a system that takes into account the user's emotional state, efficiently determines menus, and allows users to prepare nutritionally balanced meals while referring to detailed cooking instructions. Furthermore, the ordering function for missing ingredients reduces the problem of abandoning cooking due to lack of necessary ingredients. The introduction of an emotion engine makes it possible to provide cooking support that is more tailored to each individual user, reducing stress and increasing satisfaction.

[0481] The processing flow will be explained below.

[0482] Step 1:

[0483] The user takes a photo of the ingredients in the refrigerator. The user takes a photo of the ingredients using the camera on their smartphone or tablet, and the image data is saved by a dedicated application.

[0484] Step 2:

[0485] The device converts the captured photo data into JSON format and sends it to the server. The device then sends the photo data to the server via the Internet as an HTTP request.

[0486] Step 3:

[0487] The server analyzes the received photo data. After the server receives the photo data, it launches an image analysis algorithm to identify the type and amount of ingredients.

[0488] Step 4:

[0489] The server generates a recipe based on the analysis results, selecting and generating a nutritionally balanced recipe suitable for the identified ingredients from a recipe database stored on the server.

[0490] Step 5:

[0491] The server generates a cooking process video. Based on the generated recipe, the server combines video clips of each cooking step to create a detailed cooking process video. This video also includes audio commentary and a timer function.

[0492] Step 6:

[0493] The device uses an emotion engine to recognize the user's facial expressions and voice tone, and sends the emotion data to the server. The emotion engine determines the user's emotional state from the user's facial expressions, and the device then sends that data to the server.

[0494] Step 7:

[0495] The server customizes recipes and cooking steps based on the user's emotional data. The server adjusts recipe selection criteria based on the user's emotional state. For example, if the user is feeling stressed, it will prioritize dishes that have a relaxing effect or are easy to make.

[0496] Step 8:

[0497] The server customizes cooking process videos based on emotional data. If the user feels anxious, the voice narration in the video will be changed to a more polite and calm tone. The color and font of the screen display will also be adjusted.

[0498] Step 9:

[0499] The server identifies missing ingredients. The server checks the list of ingredients required for the generated recipe and compares it with the list of ingredients the user already has to identify missing ingredients.

[0500] Step 10:

[0501] The server provides a function to order missing ingredients. The server connects to an external shopping app and provides users with a link or option to order missing ingredients.

[0502] Step 11:

[0503] The server returns the generated recipe and cooking process video to the device, and the server sends the recipe and video data to the user's device as an HTTP response.

[0504] Step 12:

[0505] The device displays the received data to the user, and the device application displays the recipe and cooking process video in an easy-to-understand manner for the user, who can refer to it while cooking.

[0506] Step 13:

[0507] If necessary, the device can project cooking process videos onto the wall via smart home devices, allowing users to check the cooking process while keeping their hands free.

[0508] Through these processing steps, users can efficiently plan their menu and prepare nutritionally balanced meals while referring to detailed cooking instructions. Even if ingredients are in short supply, they can be quickly ordered, reducing the hassle of cooking. Furthermore, by incorporating an emotion engine, the system can provide customized cooking support tailored to each individual user's condition, reducing stress and increasing satisfaction.

[0509] Example 2

[0510] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0511] Conventional cooking support systems lack functionality to improve user satisfaction, as they are not adequately customized to take into account the user's emotional state. Furthermore, they have limited functionality for identifying missing ingredients and linking with external systems to order additional ingredients, making it difficult for users to easily obtain the ingredients they need. Furthermore, generating and displaying cooking procedure videos has also been an issue in some environments.

[0512] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0513] In this invention, the server includes means for taking photos of ingredients, means for converting the photos of ingredients into a data format and sending the data to the server, means for analyzing the types and amounts of ingredients on the server, means for generating a recipe that takes nutritional balance into consideration based on the analysis results, means for generating a video of cooking steps based on the generated recipe, means for identifying missing ingredients and ordering additional ingredients in cooperation with an external system, means for returning the generated recipe and video of cooking steps to the terminal, and means for recognizing the user's emotional state using an emotion engine and customizing the recipe and video of cooking steps based on the recognized emotional state. This makes it possible to propose recipes and provide video of cooking steps that are customized according to the user's emotional state, making it easy to obtain the necessary ingredients and check the cooking steps.

[0514] The "means for taking photos of ingredients" is a mechanism for acquiring images of ingredients in the refrigerator using the camera of the user's device.

[0515] The "means of converting into a data format and sending to a server" refers to a mechanism for converting the image data of photographed ingredients into a standard data format such as JSON and sending it to a server via the Internet.

[0516] "Means for analyzing the type and amount of ingredients on the server" refers to a system that uses an algorithm (e.g., YOLO) to analyze photo data sent to the server and identify the type and amount of ingredients in the photo.

[0517] The "means for generating recipes that take into account nutritional balance" is an algorithm for generating optimal recipes that take into account the user's health and nutritional balance based on analyzed ingredient information.

[0518] The "means for generating video of cooking steps" is a mechanism for providing visual instructions to the user by assembling video clips showing the necessary cooking steps based on the recipe.

[0519] "Means to identify missing ingredients and order additional ingredients by connecting with an external system" refers to a system that checks a list of all necessary ingredients based on the generated recipe, identifies missing ingredients that the user does not have, and connects with an external shopping system, etc., to make it possible to purchase additional ingredients.

[0520] "Means for returning the generated recipe and cooking procedure video to the terminal" refers to a mechanism by which the server sends the generated recipe and video to the user's terminal, allowing the user to easily access them.

[0521] "Means for recognizing a user's emotional state using an emotion engine and customizing recipe and cooking procedure videos based on the recognized emotional state" refers to an engine that analyzes a user's facial expressions and voice tone to identify their emotional state and provides recipes, narration, and display formats that are optimal for that emotional state.

[0522] MODE FOR CARRYING OUT THE INVENTION

[0523] The present invention is a system that takes pictures of ingredients in a refrigerator, analyzes the image data, generates suitable dish candidates and recipes according to the user's emotional state, and provides videos of the cooking process. Detailed embodiments of the system are described below.

[0524] First, the user installs a dedicated application on their smartphone, tablet, or other device. Using this application, the user takes a photo of the food in their refrigerator. Once the photo is taken, the device automatically converts the photo data into JSON format and sends it to a server via the Internet.

[0525] The server analyzes the received photo data using the image analysis algorithm "YOLO (You Only Look Once)." This identifies the ingredients in the photo. The identified ingredient information is then input into "Chef Watson," a recipe generation algorithm that takes nutritional balance into consideration, to generate optimal dish candidates and their recipes.

[0526] Based on the generated recipe, the server retrieves video clips corresponding to the cooking steps using the Vimeo API and combines them in the appropriate order to create a continuous cooking process video, which also includes audio commentary and a timer function.

[0527] Furthermore, the server checks the list of all ingredients required based on the generated recipe. If any ingredients are missing, the server connects to the shopping system using the Amazon API and provides the user with the option to order more. The user can then easily purchase the missing ingredients through the shopping system.

[0528] The server then sends the generated recipe and cooking process video back to the device. The device receives this data and displays it on the application screen. The user can proceed with cooking based on the displayed information. In addition, if the smart home device "Google Nest Hub" is used, the cooking process video can be projected onto the wall.

[0529] A notable feature of this invention is its incorporation of an emotion engine ("Affectiva SDK"). The emotion engine recognizes the user's facial expressions and vocal tone to measure their emotional state (e.g., joy, sadness, stress). This emotion data is sent to the server and reflected in recipe generation and customization of cooking process videos. For example, if the user is feeling stressed, dishes that have a relaxing effect or are easy to make will be prioritized. In addition, the audio narration of the generated video and the color and font of the screen display are also adjusted according to the user's emotional state.

[0530] For example, a user can take a photo of "tomatoes," "chicken," and "cheese" in their refrigerator, and the server will analyze the photos and generate a recipe for "chicken and tomato pasta." If the emotion engine recognizes that the user is tired, it will also suggest an easy-to-make "tomato salad." The generated video will also have a gentle voice narration.

[0531] An example prompt can be entered in the following text format:

[0532] "I have tomatoes and chicken in the fridge. I'm tired, so please suggest some easy meals."

[0533] This system allows users to obtain customized cooking recipes based on their emotional state, easily purchase the necessary ingredients, and enjoy cooking with reduced stress by viewing detailed cooking instruction videos.

[0534] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0535] Program processing steps

[0536] Step 1:

[0537] The user installs a dedicated application on their smartphone or tablet.

[0538] Input: None

[0539] Output: The application is installed on the device.

[0540] Specific operation: The user downloads a dedicated application from the app store and installs it on their device.

[0541] Step 2:

[0542] The user launches the application and takes a photo of the ingredients in the refrigerator.

[0543] Input: Food in the refrigerator

[0544] Output: Photograph of the ingredients

[0545] What happens: The user opens the app and uses the in-app camera to take a picture of the ingredients in the refrigerator.

[0546] Step 3:

[0547] The device converts the captured photo data into JSON format.

[0548] Input: A photo of the food

[0549] Output: Photo data in JSON format

[0550] What happens: The application internally parses the image data and converts the corresponding data into JSON format.

[0551] Step 4:

[0552] The terminal converts the data into JSON format and sends it to the server via the Internet.

[0553] Input: JSON format photo data

[0554] Output: Photo data sent to the server

[0555] What happens: The device uses an internet connection to upload the converted data to the server.

[0556] Step 5:

[0557] The photo data received by the server is analyzed using the image analysis algorithm "YOLO."

[0558] Input: JSON format photo data

[0559] Output: Type and amount of ingredients

[0560] What it does: The server uses the "YOLO" algorithm to analyze the image data sent and identify the types of ingredients in the photo and their quantities.

[0561] Step 6:

[0562] The server uses the "Chef Watson" algorithm based on the analysis results to generate recipes that take nutritional balance into consideration.

[0563] Input: Type and amount of ingredients

[0564] Output: Best food suggestions and recipes

[0565] Specific operation: The server uses "Chef Watson" to generate the optimal recipe based on the identified ingredient information.

[0566] Step 7:

[0567] The server uses an emotional engine ("Affectiva SDK") to recognize the user's emotional state and customize the recipe.

[0568] Input: User's facial expressions and voice tone

[0569] Output: Customized food suggestions and recipes based on sentiment data

[0570] Specific operation: The server uses the emotion engine to analyze the user's emotional state at the time of taking the photo and reflects the results in recipe generation.

[0571] Step 8:

[0572] The server generates a cooking procedure video based on the generated recipe.

[0573] Input: Customized Recipe

[0574] Output: Cooking video with any combination of steps

[0575] Specific operation: The server uses the Vimeo API or similar to obtain video clips corresponding to cooking steps, and then combines them in the appropriate order to generate a continuous video.

[0576] Step 9:

[0577] The server checks the list of ingredients needed, connects with an external shopping system to identify ingredients that are missing, and presents options for ordering more.

[0578] Input: Generated recipe and user's ingredients

[0579] Output: List of missing ingredients and options to order more

[0580] Specific operation: The server identifies the ingredients that are in short supply using the Amazon API or similar and gives the user the option to purchase more.

[0581] Step 10:

[0582] The server sends the final recipe and cooking instruction video to the device.

[0583] Input: Customized recipes and cooking instructions videos

[0584] Output: Recipe and video data sent to the device

[0585] Specific operation: The server sends the generated recipes and videos to the device, making them viewable in the application.

[0586] Step 11:

[0587] The data received by the device is displayed on the application screen, and if necessary, a video is projected onto the wall using a smart home device.

[0588] Input: Received recipe and cooking instructions video

[0589] Output: Recipes and videos displayed on the application screen or projected onto smart home devices

[0590] How it works: Users can view recipes and videos in the app and can also use smart home devices to project cooking instruction videos onto the wall.

[0591] This will enable the system to propose recipes and provide cooking procedure videos customized to the user's emotional state, allowing them to easily obtain the necessary ingredients and check the cooking procedures.

[0592] (Application example 2)

[0593] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0594] In today's society, preparing nutritiously balanced meals at home despite leading busy daily lives is a major challenge for many people. Users, especially those living in hectic schedules and stressful environments, often find it difficult to find the time and energy to cook. As a result, many resort to eating out or unhealthy eating habits. Furthermore, when ingredients are scarce, it can be a hassle to go shopping. Furthermore, the lack of appropriate recipe suggestions based on emotional state makes it difficult to improve user satisfaction.

[0595] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for analyzing the user's emotional state and proposing customized recipes and cooking methods according to the emotional state, means for generating recipes that take nutritional balance into consideration based on the analysis results, and means for generating videos of cooking steps based on the generated recipes. This enables personalized cooking suggestions that take into account the user's emotional state and stress level. As a result, the user can have a more relaxed and enjoyable cooking experience and be supported in maintaining a healthy diet. Furthermore, even if necessary ingredients are in short supply, additional ingredients can be quickly ordered in cooperation with an external app, reducing the effort required to obtain ingredients.

[0596] The "means for taking photos of ingredients" is a function for acquiring image data of ingredients in the refrigerator using an electronic device owned by the user.

[0597] The "means for transmitting the photographed foodstuffs to the server" is a communication function for transferring the acquired foodstuffs image data from the electronic device to the server.

[0598] The "means for analyzing the type and amount of ingredients on the server" refers to an algorithm and program for analyzing the image data of ingredients uploaded to the server and identifying the type and amount of ingredients.

[0599] The "means for generating recipes that take nutritional balance into consideration" is a function that generates nutritionally balanced cooking recipes based on analyzed ingredient data.

[0600] The "means for generating videos of cooking processes" is a function that creates videos showing cooking steps and methods based on the generated recipe.

[0601] "Means for identifying missing ingredients and ordering additional ingredients in cooperation with an external application" is a function that identifies ingredients that the user does not have and orders the missing ingredients through an external shopping application.

[0602] "Means for analyzing the user's emotional state and suggesting recipes and cooking methods according to the emotional state" refers to a function that analyzes the user's emotions from their facial expressions and voice, and customizes cooking suggestions and cooking methods based on the analysis results.

[0603] The "means for returning the generated recipe and cooking process video to the terminal" is a function for sending the recipe and cooking method video generated by the server to the user's electronic device.

[0604] The "means for setting video narration and display according to emotional state" is a function for changing the tone of video narration and the color of display based on the user's emotional state.

[0605] This invention is a system that uses an application installed on a user's device (such as a smartphone or tablet) to take photos of ingredients in a refrigerator, and then sends the data to a server for analysis. Specifically, the invention is implemented in the following steps.

[0606] First, the user uses their device to take a photo of the food in their refrigerator. The captured image data is converted into JSON format by an application on the device and sent to a server over the Internet, using standard Internet protocols.

[0607] The server then uses AI image recognition technology, such as Google Cloud Vision API, to analyze the received image data. This identifies the types and amounts of ingredients. The analysis results are then input into a recipe generation algorithm that takes nutritional balance into account, generating the best possible dishes and recipes for the user.

[0608] Based on the generated recipe, the server generates a cooking process video using the FFmpeg library, taking video clips corresponding to each cooking step and combining them to create a continuous cooking process video, which also includes audio commentary and a timer function.

[0609] In addition, the server uses the Emotion API from Microsoft Azure Cognitive Services to analyze the user's emotional state. The system analyzes the user's facial expressions and vocal tone to identify their emotional state. This emotional data is reflected in the criteria for recipe generation; if the user is feeling stressed, it will prioritize dishes that are relaxing or easy to make. The generated cooking process videos are also customized based on the emotional data. The voice narration changes depending on the user's emotional state, and the color and font of the screen display are also adjusted.

[0610] For example, if a user takes a photo of "tomatoes," "chicken," and "cheese" in the refrigerator, the server analyzes the photo and generates a recipe for "chicken and tomato pasta." If the emotion engine recognizes that the user is tired, it will also suggest an easy-to-make "tomato salad." The voice narration for the video is set to a gentler tone than usual to help users relax.

[0611] If any ingredients are identified as being in short supply, the server will share the list with an external shopping app and provide the user with the option to order more. The user can then easily purchase the missing ingredients through the shopping app.

[0612] In this way, a system is realized that provides a stress-free cooking experience that suits the user's emotions and supports a healthy diet.

[0613] Example prompt sentence:

[0614] "Use an emotion engine to determine the user's emotional state and generate prompts for a food delivery app that suggests relaxing recipes."

[0615] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0616] Step 1:

[0617] The user uses a device (smartphone or tablet) to take a photo of the ingredients in the refrigerator. The input is image data captured by the camera, and the output is an image file saved on the device. The device takes the photo in response to the user's operation and saves the image data.

[0618] Step 2:

[0619] The device converts the photographed food photo data into JSON format and sends it to the server via the Internet. The input is an image file, and the output is JSON format data sent to the server. The device converts the image data into a JSON object and sends it to the server via an HTTP request.

[0620] Step 3:

[0621] The server analyzes the received JSON-formatted photo data and identifies the type and quantity of ingredients. The input is JSON-formatted image data, and the output is the analyzed ingredient information. The server uses the Google Cloud Vision API to analyze the image and identify the type and quantity of ingredients.

[0622] Step 4:

[0623] The server generates a nutritionally balanced recipe based on the analyzed ingredient information. The input is ingredient information, and the output is the generated recipe. The server uses an internal algorithm to generate a nutritionally balanced recipe from the input ingredient information.

[0624] Step 5:

[0625] The server analyzes facial expressions and voice tones to determine the user's emotional state. The input is the user's face photo and voice data, and the output is the user's emotional state data. The server identifies the emotional state using the Emotion API of Microsoft Azure Cognitive Services.

[0626] Step 6:

[0627] The server considers the emotional state data and proposes customized recipes and cooking methods. The input is the emotional state data and ingredient information, and the output is a customized recipe proposal. The server generates recipes and cooking methods that reflect the results of the emotion analysis.

[0628] Step 7:

[0629] The server generates a cooking process video based on the generated recipe. The input is recipe information, and the output is a cooking process video. The server uses FFmpeg to combine video clips corresponding to each cooking step to generate a continuous cooking process video.

[0630] Step 8:

[0631] The server returns the generated cooking process video to the terminal. The input is the cooking process video, and the output is the video data sent to the user terminal. The server sends the generated video data to the terminal as an HTTP response.

[0632] Step 9:

[0633] The server identifies missing ingredients and orders additional ingredients by connecting with an external shopping app. The input is a list of ingredients required for the recipe, and the output is the order information. The server sends the list of missing ingredients via the external shopping app's API and processes the order.

[0634] Step 10:

[0635] The device displays the cooking process video received. The input is the received video data, and the output is the video played on the device screen. The device plays the received video data to provide the user with visual cooking support.

[0636] Step 11:

[0637] The device changes the tone of the video narration and the color of the screen display according to the user's emotional state. The input is emotional state data and video data, and the output is a customized video display. The device adjusts the narration and screen settings to match the user's emotional state, providing a more relaxing experience.

[0638] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0639] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0640] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0641] [Third embodiment]

[0642] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0643] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0644] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0645] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0646] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0647] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0648] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0649] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0650] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0651] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0652] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0653] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0654] The present invention is a system that takes photos of ingredients in a refrigerator, analyzes the image data, generates suitable dish options and recipes, and provides videos of the cooking process. Detailed embodiments of the system are described below.

[0655] First, a dedicated application is installed on a user's smartphone, tablet, or other device. The user uses this application to take a photo of the food in the refrigerator. Once the photo is taken, the device automatically converts the photo data into JSON format and sends it to a server via the Internet.

[0656] The photo data received by the server is first analyzed using an image analysis algorithm. During this process, the server identifies the types and amounts of ingredients. The identified ingredient information is then input into a recipe generation algorithm that takes nutritional balance into consideration. This allows the server to generate the best possible recipes for the user.

[0657] Based on the generated recipe, the server generates a video of the cooking process. The server retrieves video clips corresponding to each cooking step from the database and combines them to create a continuous video of the cooking process. This video includes detailed instructions for each cooking step, a timer function, and audio narration.

[0658] Next, the server checks the list of all necessary ingredients based on the generated recipe. If any ingredients that the user does not have are identified, the server connects the list to an external shopping app and provides the user with the option to order additional ingredients. The user can easily purchase the missing ingredients through the shopping app.

[0659] Finally, the server sends the generated recipe and cooking process video back to the device. The device receives this data and displays it on the application screen. The user then proceeds with the cooking process based on the displayed information. Furthermore, users of smart home devices can project the cooking process video from the device screen onto the wall. This function frees up the user's hands and allows for more efficient cooking.

[0660] For example, if a user takes a photo of "tomatoes," "chicken," and "cheese" in the refrigerator, the server analyzes this information and generates a recipe for "chicken and tomato pasta." Based on the recipe, the server then creates a video including instructions on how to bake chicken, how to cut tomatoes, how to boil pasta, and so on, providing detailed step-by-step instructions to help the user with the cooking process.

[0661] With the above configuration, the present invention is a system that allows users to efficiently plan menus and easily prepare nutritionally balanced meals. In addition, the system's ability to order ingredients in short supply reduces the risk of abandoning a cooking project due to lack of necessary ingredients.

[0662] The processing flow will be explained below.

[0663] Step 1:

[0664] The user takes a photo of the ingredients in the refrigerator. The user takes a photo of the ingredients using the camera on their smartphone or tablet, and the image data is saved by a dedicated application.

[0665] Step 2:

[0666] The device converts the captured photo data into JSON format and sends it to the server. The device then sends the photo data to the server via the Internet as an HTTP request.

[0667] Step 3:

[0668] The server analyzes the received photo data. After the server receives the photo data, it launches an image analysis algorithm to identify the type and amount of ingredients.

[0669] Step 4:

[0670] The server generates a recipe based on the analysis results, selecting and generating a nutritionally balanced recipe suitable for the identified ingredients from a recipe database stored on the server.

[0671] Step 5:

[0672] The server generates a cooking process video. Based on the generated recipe, the server combines video clips of each cooking step to create a detailed cooking process video. This video also includes audio commentary and a timer function.

[0673] Step 6:

[0674] The server identifies missing ingredients. The server checks the list of ingredients required for the generated recipe and compares it with the list of ingredients the user already has to identify missing ingredients.

[0675] Step 7:

[0676] The server provides a function to order missing ingredients. The server connects to an external shopping app and provides users with a link or option to order missing ingredients.

[0677] Step 8:

[0678] The server returns the generated recipe and cooking process video to the device, and the server sends the recipe and video data to the user's device as an HTTP response.

[0679] Step 9:

[0680] The device displays the received data to the user, and the device application displays the recipe and cooking process video in an easy-to-understand manner for the user, who can refer to it while cooking.

[0681] Step 10:

[0682] If necessary, the device can project cooking process videos onto the wall via smart home devices, allowing users to check the cooking steps while keeping their hands free.

[0683] By using the above process steps, users can efficiently plan their menu and prepare nutritionally balanced meals while referring to detailed cooking instructions. Even if ingredients are in short supply, they can be quickly ordered, reducing the hassle of cooking.

[0684] Example 1

[0685] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0686] With conventional cooking assistance systems, it was difficult for users to effectively use the ingredients in their refrigerator and efficiently prepare nutritionally balanced meals. Furthermore, if an ingredient was insufficient, it was time-consuming to purchase additional ingredients, which was inconvenient for users. Furthermore, the cooking steps could be difficult to understand, which also created an issue of cooking not proceeding smoothly.

[0687] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0688] In this invention, the server includes a means for taking photos of ingredients, a means for converting the photos of ingredients into a data format and sending them to the server, a means for analyzing the types and quantities of ingredients on the server, a means for generating a nutritionally balanced recipe based on the analysis results, a means for generating a video of the cooking process based on the generated recipe, a means for identifying missing ingredients and ordering additional ingredients in cooperation with an external e-commerce system, and a means for returning the generated recipe and cooking process video to the terminal. This allows the user to easily prepare a nutritionally balanced meal using ingredients in the refrigerator and efficiently purchase missing ingredients. Furthermore, the video providing detailed instructions on the cooking process allows the user to smoothly proceed with the cooking process.

[0689] "Means for taking photos of ingredients" refers to equipment or functions for capturing digital images of ingredients in the refrigerator.

[0690] "Means for converting photographs of ingredients into a data format and sending it to a server" refers to devices or functions for converting acquired photos of ingredients into structured data such as JSON format and sending that data to a server via the Internet.

[0691] "Means for analyzing the type and amount of ingredients on the server" refers to image recognition algorithms or software that analyzes image data received by the server and identifies the specific type and amount of ingredients.

[0692] "Means for generating recipes that take nutritional balance into consideration based on analysis results" refers to algorithms and software for generating optimal recipes that take nutritional balance into consideration based on identified ingredient information.

[0693] "Means for generating cooking process videos based on the generated recipe" refers to equipment or software that acquires and edits video clips to create a continuous cooking process video in order to provide cooking steps corresponding to the generated recipe in video format.

[0694] "Means of identifying missing ingredients and linking with an external e-commerce system to order additional ingredients" refers to a function that identifies ingredients that the user does not have and links with the e-commerce platform to complete the purchase process for the missing ingredients.

[0695] "Means for returning the generated recipe and cooking process video to the terminal" refers to the function for sending the recipe and cooking process video generated by the server to the user's terminal and displaying that information.

[0696] "Smart home appliances" refer to electronic devices that are connected to a network and provide various functions through voice control, touch control, etc.

[0697] A "structured data format" is a data format that organizes information in a specified format and makes it easy for machines to process. JSON format is a common example.

[0698] MODE FOR CARRYING OUT THE INVENTION

[0699] The present invention is a system that takes photos of ingredients in a refrigerator, analyzes the image data, generates suitable dish candidates and recipes, and provides videos of the cooking process. Specific embodiments of the system are described below.

[0700] First, a dedicated application is installed on a user's smartphone, tablet, or other device. The user uses this application to take photos of the ingredients in the refrigerator. For example, the user opens the refrigerator and takes photos of "tomatoes," "chicken," and "cheese." Once the photos are taken, the device automatically converts the photo data into JSON format and sends it to a server via the Internet.

[0701] The photo data received by the server is first analyzed using an image analysis algorithm. This analysis uses common image recognition software. Specifically, the server identifies the type and quantity of ingredients. For example, specific data such as "3 tomatoes," "500g of chicken," and "200g of cheese" is extracted.

[0702] Based on the identified ingredient information, the server uses a recipe generation algorithm that takes nutritional balance into account to generate optimal dish candidates and recipes. This process utilizes a generative AI model. Specifically, models such as OpenAI are used. This algorithm generates recipes taking into account the user's usage history and the freshness of the ingredients. For example, a recipe for "chicken and tomato pasta" is generated.

[0703] Based on the generated recipe, the server generates a video of the cooking process. The server retrieves video clips corresponding to each cooking step from the database and combines them to create a continuous cooking process video. This process is performed using video editing software such as Adobe Premiere Pro. The video includes detailed explanations of each cooking step, a timer function, and audio narration.

[0704] Next, the server checks the list of all ingredients required based on the generated recipe. If any ingredients that the user does not have are identified, the server connects the list to an external e-commerce system and provides the user with options for ordering additional ingredients. For example, it uses online shopping platforms such as Amazon Fresh or Rakuten Ichiba. This allows the user to easily purchase the missing ingredients.

[0705] Finally, the server sends the generated recipe and cooking process video back to the device. The device receives this data and displays it on the application screen. The user then proceeds with the cooking process based on the displayed information. Furthermore, users of smart home appliances can project the cooking process video from the device screen onto the wall. This function frees up the user's hands and makes cooking more efficient.

[0706] For example, if a user takes a photo of "tomatoes," "chicken," and "cheese" in the refrigerator, the server analyzes this information and generates a recipe for "chicken and tomato pasta." Based on the recipe, the server then creates a video that includes instructions on how to bake chicken, how to cut tomatoes, how to boil pasta, and other steps, providing detailed step-by-step instructions that will help the user as they cook.

[0707] The following are examples of specific prompt sentences:

[0708] Take a photo of "tomatoes," "chicken," and "cheese" in your refrigerator and generate a recipe and cooking video using these ingredients.

[0709] With the above configuration, the present invention is a system that allows users to efficiently plan menus and easily prepare nutritionally balanced meals. In addition, the system's ability to order ingredients in short supply reduces the risk of abandoning a cooking project due to lack of necessary ingredients.

[0710] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0711] Step 1:

[0712] The user takes a photo of the ingredients in the refrigerator using a smartphone or tablet. The user then launches the application and takes a photo of an ingredient, such as "tomato," "chicken," or "cheese." The input is the photo of the ingredient, and the output is a digital image file.

[0713] Step 2:

[0714] The device automatically converts photos taken into JSON format. Specifically, an image analysis module in the application reads the photos and converts them into a format that can be processed as digital data. The input is a digital image file, and the output is JSON-formatted data.

[0715] Step 3:

[0716] The device sends the converted JSON-formatted photo data to the server via the Internet. This operation is performed through a communication module in the application. The input is JSON-formatted data, and the output is a data stream sent to the server.

[0717] Step 4:

[0718] The server analyzes the received photo data. This analysis is performed using an image recognition algorithm. The server uses image recognition tools such as Amazon Rekognition or Google Cloud Vision to identify the type and quantity of ingredients. The input is JSON-formatted photo data, and the output is ingredient information (e.g., "3 tomatoes," "500g chicken," and "200g cheese").

[0719] Step 5:

[0720] The server generates a recipe that takes nutritional balance into consideration based on the identified ingredient information. A generative AI model is used here. The server receives ingredient information as input and uses a generative AI model such as OpenAI to generate nutritionally balanced recipe candidates. The input is ingredient information, and the output is recipe data (e.g., a recipe for "chicken and tomato pasta").

[0721] Step 6:

[0722] The server generates a cooking process video based on the generated recipe. The server retrieves video clips corresponding to each cooking step from the database and connects them using video editing software (e.g., Adobe Premiere Pro) to create a continuous cooking process video. The input is recipe data, and the output is a cooking process video.

[0723] Step 7:

[0724] The server checks the list of all required ingredients based on the generated recipe. The server lists the ingredients included in the recipe and identifies any ingredients that the user does not currently have. The input is the recipe data and the user's ingredient information, and the output is the list of missing ingredients.

[0725] Step 8:

[0726] The server connects the list of ingredients in short supply to an external e-commerce system and provides users with options for ordering more. Specifically, it connects with Amazon Fresh, Rakuten Ichiba, and other systems to provide links and interfaces for purchasing ingredients in short supply. The input is the list of ingredients in short supply, and the output is external links and purchasing options.

[0727] Step 9:

[0728] The server returns the generated recipe and cooking process video to the device. The server then sends this data to the device via the Internet, and the device displays the received data on the application screen. The input is the recipe data and cooking process video data, and the output is a data stream to the device.

[0729] Step 10:

[0730] The user proceeds with cooking based on the displayed information. The user cooks while referring to the recipe and video displayed on the application. If necessary, the cooking process video can be projected onto the wall using smart home appliances. This process allows the user to cook efficiently while keeping their hands free. The input is the recipe and video displayed on the device, and the output is the finished dish.

[0731] (Application example 1)

[0732] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0733] In recent years, there has been a demand for easy preparation of nutritionally balanced meals. There is also a need for systems that can determine the amount of a certain ingredient in the refrigerator, suggest appropriate recipes based on that information, and provide easy-to-understand cooking instructions. However, current systems do not fully integrate these functions, and lack the support users need to easily prepare nutritionally balanced meals. Furthermore, there are few systems that can automatically order missing ingredients all at once. In addition, providing an environment in which both hands can be used freely when actually following the cooking instructions is an important challenge.

[0734] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0735] In this invention, the server includes a means for capturing images of ingredients, a means for transmitting the captured images of ingredients to the server, a means for analyzing the types and quantities of ingredients on the server, a means for generating cooking instructions that take nutritional balance into consideration based on the analysis results, a means for generating a video of the cooking process based on the generated cooking instructions, a means for identifying missing ingredients and ordering additional ingredients in cooperation with an external system, a means for returning the generated cooking instructions and the video of the cooking process to the terminal, a means for displaying the analyzed ingredients and the generated cooking instructions on the smart device, and a means for displaying the video of the cooking process. This allows users to effectively use the ingredients in their refrigerator and easily prepare nutritionally balanced meals. Furthermore, the automatic ordering of missing ingredients and the display and projection of video of the cooking process on the smart device improve convenience during cooking.

[0736] "Means for taking pictures of ingredients" refers to devices or applications that allow users to easily take pictures of ingredients in the refrigerator.

[0737] "Means for transmitting photographed images of ingredients to a server" refers to a mechanism for transmitting photographed image data from a user's device to a server via the Internet.

[0738] "Means for analyzing the types and amounts of ingredients on the server" refers to an algorithm or program that analyzes the received image data and automatically identifies and measures the types and amounts of ingredients contained therein.

[0739] "Means for generating cooking instructions that take nutritional balance into consideration based on the analysis results" refers to an algorithm that automatically generates nutritionally balanced recipes based on analyzed ingredient information.

[0740] "Means for generating a video of the cooking process based on the generated cooking instructions" refers to a function for providing each step of the generated recipe as a video, and refers to a mechanism for combining the necessary video clips and presenting them in a form that is easy for the user to understand.

[0741] "Means of identifying missing ingredients and linking with external systems to order additional ingredients" refers to a system that identifies missing ingredients from the information on ingredients the user has on hand and links with external shopping systems or food delivery systems to place additional orders.

[0742] "Means for returning the generated cooking instructions and cooking process video to the terminal" refers to a mechanism for sending the generated recipe information and cooking process video data to the user's device and displaying them.

[0743] "Means for displaying analyzed ingredients and generated cooking instructions via a smart device" refers to the function for displaying the analysis results and generated recipes on a smart device such as a smartphone or tablet.

[0744] "Means for displaying cooking process videos" refers to a function that plays videos of cooking steps on the user's device to make them visually easy to understand.

[0745] This invention is a system that takes photos of ingredients in the refrigerator, analyzes the image data, generates suitable dish suggestions and recipes, and provides videos of the cooking process. This system combines ingredient management and cooking support, allowing users to easily prepare nutritionally balanced meals.

[0746] First, a dedicated application is installed on the user's device (such as a smartphone or tablet). Using this application, the user takes a photo of the ingredients in the refrigerator. The device reads the photo data, converts it into JSON format, and sends it to a server via the Internet.

[0747] The server analyzes the received photo data. Specifically, it uses an image analysis algorithm to identify the types and amounts of ingredients contained in the photo. This analysis is performed using a generative AI model, which is trained on images of many different ingredients as initial learning data. The analyzed ingredient information is then input into a recipe generation algorithm that takes nutritional balance into account. This algorithm also uses a generative AI model to generate optimal recipe suggestions based on the user's preferences and health condition.

[0748] Based on the generated recipe, the server generates a cooking process video. This process involves retrieving video clips corresponding to each cooking step from the database and combining them appropriately to create a continuous cooking process video. The video includes detailed instructions for each cooking step, a timer function, and audio narration.

[0749] The server then checks the list of all ingredients required based on the generated recipe. If any ingredients are missing, the server connects the list to an external shopping system and provides the user with options for ordering additional ingredients. In this case, the user can easily order additional ingredients by connecting to a shopping app.

[0750] Finally, the server sends the generated recipe and cooking process video back to the user's device and displays it in the application. The user can use the displayed information to cook efficiently. In addition, users with smart home devices can project the cooking process video onto a wall from the application screen. This function frees up both hands, making cooking even more efficient.

[0751] For example, if a user takes a photo of "tomatoes," "chicken," and "cheese" in the refrigerator, the server analyzes these ingredients and generates a recipe for "chicken and tomato pasta." Based on that recipe, the server also creates a video including instructions on how to grill chicken, how to cut tomatoes, how to boil pasta, and so on, and provides it to the user.

[0752] An example prompt is:

[0753] Please analyze the following image to identify the food items and quantities present. Return a JSON object with the list of identified food items and their quantities.

[0754] With the above configuration, the present invention allows users to efficiently plan menus and easily prepare nutritionally balanced meals. In addition, the lack of ingredients ordering function reduces the problem of abandoning a cooking project due to lack of necessary ingredients.

[0755] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0756] Step 1:

[0757] Users use devices such as smartphones and tablets to take pictures of the food items in their refrigerators, which are then saved on the device.

[0758] Step 2:

[0759] The device converts the captured image into JSON format and sends it to a server via the Internet. The input is image data, and the output is JSON format data.

[0760] Step 3:

[0761] The server analyzes the received JSON formatted image data. It uses an image analysis algorithm to identify the types and quantities of ingredients in the image. The input is JSON formatted image data, and the output is a list of the types and quantities of ingredients.

[0762] Step 4:

[0763] The server generates cooking instructions that take nutritional balance into consideration based on the identified ingredient information. Using a generative AI model, it automatically generates optimal recipes that match the user's preferences and health status. The input is ingredient information, and the output is cooking instructions (recipe).

[0764] Step 5:

[0765] Based on the cooking instructions, the server retrieves video clips of each cooking step from the database and creates a continuous cooking process video. The input is the cooking instructions, and the output is the cooking process video.

[0766] Step 6:

[0767] The server checks the list of all necessary ingredients based on the generated recipe. It identifies missing ingredients and connects with an external shopping system to provide the user with options for ordering additional ingredients. The input is the identified ingredient information and the recipe, and the output is the list of missing ingredients and a shopping link.

[0768] Step 7:

[0769] The server generates cooking instructions and video of the cooking process and returns them to the user's terminal. The input is cooking instructions and video of the cooking process, and the output is reply data containing these data.

[0770] Step 8:

[0771] The cooking instructions and cooking process video received by the device are displayed on the application screen. The user can proceed with cooking based on the displayed information. The input is data from the server, and the output is the screen display that the user can see.

[0772] Step 9:

[0773] Users can also use smart home devices to project cooking process videos onto the wall. This function transmits data from the terminal to the smart home device to perform video projection. The input is data from the terminal, and the output is the projected video.

[0774] The above are the specific processing steps of this system.

[0775] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0776] The present invention is a system that takes photos of ingredients in a refrigerator, analyzes the image data to generate suitable dish candidates and recipes, and further provides videos of the cooking process, by combining this with an emotion engine to provide customized support according to the user's emotional state. Detailed embodiments of the system are described below.

[0777] First, the user installs a dedicated application on their smartphone, tablet, or other device. Using this application, the user takes a photo of the ingredients in the refrigerator. Once the photo is taken, the device automatically converts the photo data into JSON format and sends it to a server via the Internet.

[0778] The photo data received by the server is analyzed using an image analysis algorithm to identify the types and amounts of ingredients. The identified ingredient information is then input into a recipe generation algorithm that takes nutritional balance into consideration, and the server generates the best possible dishes and recipes for the user.

[0779] Based on the generated recipe, the server generates a cooking process video. It retrieves video clips corresponding to each cooking step from the database and combines them to create a continuous cooking process video. This video includes audio commentary and a timer function.

[0780] Next, the server checks the list of all ingredients required based on the generated recipe. If any ingredients that the user does not have are identified, the server connects the list to an external shopping app and provides the user with the option to order more. The user can easily purchase the missing ingredients through the shopping app.

[0781] The server sends the generated recipe and cooking process video back to the device. The device receives this data and displays it on the application screen. The user proceeds with the cooking process based on the displayed information. In addition, users of smart home devices can project the cooking process video from the device screen onto the wall.

[0782] A notable feature of this invention is its incorporation of an emotion engine. The emotion engine recognizes the user's facial expressions and vocal tone to measure their emotional state (e.g., joy, sadness, stress). This emotion data is sent to the server and reflected in the criteria for recipe generation. For example, if the user is feeling stressed, the system will prioritize suggestions for dishes that have a relaxing effect or are easy to prepare.

[0783] The cooking process videos are also customized based on the user's emotional data. If the user is feeling anxious, the voice narration in the video will be changed to a more polite and calm tone. The color and font of the screen display can also be adjusted according to the user's emotional state.

[0784] For example, if a user takes a photo of "tomatoes," "chicken," and "cheese" in the refrigerator, the server analyzes the photo and generates a recipe for "chicken and tomato pasta." If the emotion engine recognizes that the user is tired, it will also suggest an easy-to-make "tomato salad." The voice narration for the generated video will also be set to a gentler tone than usual.

[0785] As described above, the present invention is a system that takes into account the user's emotional state, efficiently determines menus, and allows users to prepare nutritionally balanced meals while referring to detailed cooking instructions. Furthermore, the ordering function for missing ingredients reduces the problem of abandoning cooking due to lack of necessary ingredients. The introduction of an emotion engine makes it possible to provide cooking support that is more tailored to each individual user, reducing stress and increasing satisfaction.

[0786] The processing flow will be explained below.

[0787] Step 1:

[0788] The user takes a photo of the ingredients in the refrigerator. The user takes a photo of the ingredients using the camera on their smartphone or tablet, and the image data is saved by a dedicated application.

[0789] Step 2:

[0790] The device converts the captured photo data into JSON format and sends it to the server. The device then sends the photo data to the server via the Internet as an HTTP request.

[0791] Step 3:

[0792] The server analyzes the received photo data. After the server receives the photo data, it launches an image analysis algorithm to identify the type and amount of ingredients.

[0793] Step 4:

[0794] The server generates a recipe based on the analysis results, selecting and generating a nutritionally balanced recipe suitable for the identified ingredients from a recipe database stored on the server.

[0795] Step 5:

[0796] The server generates a cooking process video. Based on the generated recipe, the server combines video clips of each cooking step to create a detailed cooking process video. This video also includes audio commentary and a timer function.

[0797] Step 6:

[0798] The device uses an emotion engine to recognize the user's facial expressions and voice tone, and sends the emotion data to the server. The emotion engine determines the user's emotional state from the user's facial expressions, and the device then sends that data to the server.

[0799] Step 7:

[0800] The server customizes recipes and cooking steps based on the user's emotional data. The server adjusts recipe selection criteria based on the user's emotional state. For example, if the user is feeling stressed, it will prioritize dishes that have a relaxing effect or are easy to make.

[0801] Step 8:

[0802] The server customizes cooking process videos based on emotional data. If the user feels anxious, the voice narration in the video will be changed to a more polite and calm tone. The color and font of the screen display will also be adjusted.

[0803] Step 9:

[0804] The server identifies missing ingredients. The server checks the list of ingredients required for the generated recipe and compares it with the list of ingredients the user already has to identify missing ingredients.

[0805] Step 10:

[0806] The server provides a function to order missing ingredients. The server connects to an external shopping app and provides users with a link or option to order missing ingredients.

[0807] Step 11:

[0808] The server returns the generated recipe and cooking process video to the device, and the server sends the recipe and video data to the user's device as an HTTP response.

[0809] Step 12:

[0810] The device displays the received data to the user, and the device application displays the recipe and cooking process video in an easy-to-understand manner for the user, who can refer to it while cooking.

[0811] Step 13:

[0812] If necessary, the device can project cooking process videos onto the wall via smart home devices, allowing users to check the cooking process while keeping their hands free.

[0813] Through these processing steps, users can efficiently plan their menu and prepare nutritionally balanced meals while referring to detailed cooking instructions. Even if ingredients are in short supply, they can be quickly ordered, reducing the hassle of cooking. Furthermore, by incorporating an emotion engine, the system can provide customized cooking support tailored to each individual user's condition, reducing stress and increasing satisfaction.

[0814] Example 2

[0815] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0816] Conventional cooking support systems lack functionality to improve user satisfaction, as they are not adequately customized to take into account the user's emotional state. Furthermore, they have limited functionality for identifying missing ingredients and linking with external systems to order additional ingredients, making it difficult for users to easily obtain the ingredients they need. Furthermore, generating and displaying cooking procedure videos has also been an issue in some environments.

[0817] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0818] In this invention, the server includes means for taking photos of ingredients, means for converting the photos of ingredients into a data format and sending the data to the server, means for analyzing the types and amounts of ingredients on the server, means for generating a recipe that takes nutritional balance into consideration based on the analysis results, means for generating a video of cooking steps based on the generated recipe, means for identifying missing ingredients and ordering additional ingredients in cooperation with an external system, means for returning the generated recipe and video of cooking steps to the terminal, and means for recognizing the user's emotional state using an emotion engine and customizing the recipe and video of cooking steps based on the recognized emotional state. This makes it possible to propose recipes and provide video of cooking steps that are customized according to the user's emotional state, making it easy to obtain the necessary ingredients and check the cooking steps.

[0819] The "means for taking photos of ingredients" is a mechanism for acquiring images of ingredients in the refrigerator using the camera of the user's device.

[0820] The "means of converting into a data format and sending to a server" refers to a mechanism for converting the image data of photographed ingredients into a standard data format such as JSON and sending it to a server via the Internet.

[0821] "Means for analyzing the type and amount of ingredients on the server" refers to a system that uses an algorithm (e.g., YOLO) to analyze photo data sent to the server and identify the type and amount of ingredients in the photo.

[0822] The "means for generating recipes that take into account nutritional balance" is an algorithm for generating optimal recipes that take into account the user's health and nutritional balance based on analyzed ingredient information.

[0823] The "means for generating video of cooking steps" is a mechanism for providing visual instructions to the user by assembling video clips showing the necessary cooking steps based on the recipe.

[0824] "Means to identify missing ingredients and order additional ingredients by connecting with an external system" refers to a system that checks a list of all necessary ingredients based on the generated recipe, identifies missing ingredients that the user does not have, and connects with an external shopping system, etc., to make it possible to purchase additional ingredients.

[0825] "Means for returning the generated recipe and cooking procedure video to the terminal" refers to a mechanism by which the server sends the generated recipe and video to the user's terminal, allowing the user to easily access them.

[0826] "Means for recognizing a user's emotional state using an emotion engine and customizing recipe and cooking procedure videos based on the recognized emotional state" refers to an engine that analyzes a user's facial expressions and voice tone to identify their emotional state and provides recipes, narration, and display formats that are optimal for that emotional state.

[0827] MODE FOR CARRYING OUT THE INVENTION

[0828] The present invention is a system that takes pictures of ingredients in a refrigerator, analyzes the image data, generates suitable dish candidates and recipes according to the user's emotional state, and provides videos of the cooking process. Detailed embodiments of the system are described below.

[0829] First, the user installs a dedicated application on their smartphone, tablet, or other device. Using this application, the user takes a photo of the food in their refrigerator. Once the photo is taken, the device automatically converts the photo data into JSON format and sends it to a server via the Internet.

[0830] The server analyzes the received photo data using the image analysis algorithm "YOLO (You Only Look Once)." This identifies the ingredients in the photo. The identified ingredient information is then input into "Chef Watson," a recipe generation algorithm that takes nutritional balance into consideration, to generate optimal dish candidates and their recipes.

[0831] Based on the generated recipe, the server retrieves video clips corresponding to the cooking steps using the Vimeo API and combines them in the appropriate order to create a continuous cooking process video, which also includes audio commentary and a timer function.

[0832] Furthermore, the server checks the list of all ingredients required based on the generated recipe. If any ingredients are missing, the server connects to the shopping system using the Amazon API and provides the user with the option to order more. The user can then easily purchase the missing ingredients through the shopping system.

[0833] The server then sends the generated recipe and cooking process video back to the device. The device receives this data and displays it on the application screen. The user can proceed with cooking based on the displayed information. In addition, if the smart home device "Google Nest Hub" is used, the cooking process video can be projected onto the wall.

[0834] A notable feature of this invention is its incorporation of an emotion engine ("Affectiva SDK"). The emotion engine recognizes the user's facial expressions and vocal tone to measure their emotional state (e.g., joy, sadness, stress). This emotion data is sent to the server and reflected in recipe generation and customization of cooking process videos. For example, if the user is feeling stressed, dishes that have a relaxing effect or are easy to make will be prioritized. In addition, the audio narration of the generated video and the color and font of the screen display are also adjusted according to the user's emotional state.

[0835] For example, a user can take a photo of "tomatoes," "chicken," and "cheese" in their refrigerator, and the server will analyze the photos and generate a recipe for "chicken and tomato pasta." If the emotion engine recognizes that the user is tired, it will also suggest an easy-to-make "tomato salad." The generated video will also have a gentle voice narration.

[0836] An example prompt can be entered in the following text format:

[0837] "I have tomatoes and chicken in the fridge. I'm tired, so please suggest some easy meals."

[0838] This system allows users to obtain customized cooking recipes based on their emotional state, easily purchase the necessary ingredients, and enjoy cooking with reduced stress by viewing detailed cooking instruction videos.

[0839] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0840] Program processing steps

[0841] Step 1:

[0842] The user installs a dedicated application on their smartphone or tablet.

[0843] Input: None

[0844] Output: The application is installed on the device.

[0845] Specific operation: The user downloads a dedicated application from the app store and installs it on their device.

[0846] Step 2:

[0847] The user launches the application and takes a photo of the ingredients in the refrigerator.

[0848] Input: Food in the refrigerator

[0849] Output: Photograph of the ingredients

[0850] What happens: The user opens the app and uses the in-app camera to take a picture of the ingredients in the refrigerator.

[0851] Step 3:

[0852] The device converts the captured photo data into JSON format.

[0853] Input: A photo of the food

[0854] Output: Photo data in JSON format

[0855] What happens: The application internally parses the image data and converts the corresponding data into JSON format.

[0856] Step 4:

[0857] The terminal converts the data into JSON format and sends it to the server via the Internet.

[0858] Input: JSON format photo data

[0859] Output: Photo data sent to the server

[0860] What happens: The device uses an internet connection to upload the converted data to the server.

[0861] Step 5:

[0862] The photo data received by the server is analyzed using the image analysis algorithm "YOLO."

[0863] Input: JSON format photo data

[0864] Output: Type and amount of ingredients

[0865] What it does: The server uses the "YOLO" algorithm to analyze the image data sent and identify the types of ingredients in the photo and their quantities.

[0866] Step 6:

[0867] The server uses the "Chef Watson" algorithm based on the analysis results to generate recipes that take nutritional balance into consideration.

[0868] Input: Type and amount of ingredients

[0869] Output: Best food suggestions and recipes

[0870] Specific operation: The server uses "Chef Watson" to generate the optimal recipe based on the identified ingredient information.

[0871] Step 7:

[0872] The server uses an emotional engine ("Affectiva SDK") to recognize the user's emotional state and customize the recipe.

[0873] Input: User's facial expressions and voice tone

[0874] Output: Customized food suggestions and recipes based on sentiment data

[0875] Specific operation: The server uses the emotion engine to analyze the user's emotional state at the time of taking the photo and reflects the results in recipe generation.

[0876] Step 8:

[0877] The server generates a cooking procedure video based on the generated recipe.

[0878] Input: Customized Recipe

[0879] Output: Cooking video with any combination of steps

[0880] Specific operation: The server uses the Vimeo API or similar to obtain video clips corresponding to cooking steps, and then combines them in the appropriate order to generate a continuous video.

[0881] Step 9:

[0882] The server checks the list of ingredients needed, connects with an external shopping system to identify ingredients that are missing, and presents options for ordering more.

[0883] Input: Generated recipe and user's ingredients

[0884] Output: List of missing ingredients and options to order more

[0885] Specific operation: The server identifies the ingredients that are in short supply using the Amazon API or similar and gives the user the option to purchase more.

[0886] Step 10:

[0887] The server sends the final recipe and cooking instruction video to the device.

[0888] Input: Customized recipes and cooking instructions videos

[0889] Output: Recipe and video data sent to the device

[0890] Specific operation: The server sends the generated recipes and videos to the device, making them viewable in the application.

[0891] Step 11:

[0892] The data received by the device is displayed on the application screen, and if necessary, a video is projected onto the wall using a smart home device.

[0893] Input: Received recipe and cooking instructions video

[0894] Output: Recipes and videos displayed on the application screen or projected onto smart home devices

[0895] How it works: Users can view recipes and videos in the app and can also use smart home devices to project cooking instruction videos onto the wall.

[0896] This will enable the system to propose recipes and provide cooking procedure videos customized to the user's emotional state, allowing them to easily obtain the necessary ingredients and check the cooking procedures.

[0897] (Application example 2)

[0898] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0899] In today's society, preparing nutritiously balanced meals at home despite leading busy daily lives is a major challenge for many people. Users, especially those living in hectic schedules and stressful environments, often find it difficult to find the time and energy to cook. As a result, many resort to eating out or unhealthy eating habits. Furthermore, when ingredients are scarce, it can be a hassle to go shopping. Furthermore, the lack of appropriate recipe suggestions based on emotional state makes it difficult to improve user satisfaction.

[0900] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for analyzing the user's emotional state and proposing customized recipes and cooking methods according to the emotional state, means for generating recipes that take nutritional balance into consideration based on the analysis results, and means for generating videos of cooking steps based on the generated recipes. This enables personalized cooking suggestions that take into account the user's emotional state and stress level. As a result, the user can have a more relaxed and enjoyable cooking experience and be supported in maintaining a healthy diet. Furthermore, even if necessary ingredients are in short supply, additional ingredients can be quickly ordered in cooperation with an external app, reducing the effort required to obtain ingredients.

[0901] The "means for taking photos of ingredients" is a function for acquiring image data of ingredients in the refrigerator using an electronic device owned by the user.

[0902] The "means for transmitting the photographed foodstuffs to the server" is a communication function for transferring the acquired foodstuffs image data from the electronic device to the server.

[0903] The "means for analyzing the type and amount of ingredients on the server" refers to an algorithm and program for analyzing the image data of ingredients uploaded to the server and identifying the type and amount of ingredients.

[0904] The "means for generating recipes that take nutritional balance into consideration" is a function that generates nutritionally balanced cooking recipes based on analyzed ingredient data.

[0905] The "means for generating videos of cooking processes" is a function that creates videos showing cooking steps and methods based on the generated recipe.

[0906] "Means for identifying missing ingredients and ordering additional ingredients in cooperation with an external application" is a function that identifies ingredients that the user does not have and orders the missing ingredients through an external shopping application.

[0907] "Means for analyzing the user's emotional state and suggesting recipes and cooking methods according to the emotional state" refers to a function that analyzes the user's emotions from their facial expressions and voice, and customizes cooking suggestions and cooking methods based on the analysis results.

[0908] The "means for returning the generated recipe and cooking process video to the terminal" is a function for sending the recipe and cooking method video generated by the server to the user's electronic device.

[0909] The "means for setting video narration and display according to emotional state" is a function for changing the tone of video narration and the color of display based on the user's emotional state.

[0910] This invention is a system that uses an application installed on a user's device (such as a smartphone or tablet) to take photos of ingredients in a refrigerator, and then sends the data to a server for analysis. Specifically, the invention is implemented in the following steps.

[0911] First, the user uses their device to take a photo of the food in their refrigerator. The captured image data is converted into JSON format by an application on the device and sent to a server over the Internet, using standard Internet protocols.

[0912] The server then uses AI image recognition technology, such as Google Cloud Vision API, to analyze the received image data. This identifies the types and amounts of ingredients. The analysis results are then input into a recipe generation algorithm that takes nutritional balance into account, generating the best possible dishes and recipes for the user.

[0913] Based on the generated recipe, the server generates a cooking process video using the FFmpeg library, taking video clips corresponding to each cooking step and combining them to create a continuous cooking process video, which also includes audio commentary and a timer function.

[0914] In addition, the server uses the Emotion API from Microsoft Azure Cognitive Services to analyze the user's emotional state. The system analyzes the user's facial expressions and vocal tone to identify their emotional state. This emotional data is reflected in the criteria for recipe generation; if the user is feeling stressed, it will prioritize dishes that are relaxing or easy to make. The generated cooking process videos are also customized based on the emotional data. The voice narration changes depending on the user's emotional state, and the color and font of the screen display are also adjusted.

[0915] For example, if a user takes a photo of "tomatoes," "chicken," and "cheese" in the refrigerator, the server analyzes the photo and generates a recipe for "chicken and tomato pasta." If the emotion engine recognizes that the user is tired, it will also suggest an easy-to-make "tomato salad." The voice narration for the video is set to a gentler tone than usual to help users relax.

[0916] If any ingredients are identified as being in short supply, the server will share the list with an external shopping app and provide the user with the option to order more. The user can then easily purchase the missing ingredients through the shopping app.

[0917] In this way, a system is realized that provides a stress-free cooking experience that suits the user's emotions and supports a healthy diet.

[0918] Example prompt sentence:

[0919] "Use an emotion engine to determine the user's emotional state and generate prompts for a food delivery app that suggests relaxing recipes."

[0920] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0921] Step 1:

[0922] The user uses a device (smartphone or tablet) to take a photo of the ingredients in the refrigerator. The input is image data captured by the camera, and the output is an image file saved on the device. The device takes the photo in response to the user's operation and saves the image data.

[0923] Step 2:

[0924] The device converts the photographed food photo data into JSON format and sends it to the server via the Internet. The input is an image file, and the output is JSON format data sent to the server. The device converts the image data into a JSON object and sends it to the server via an HTTP request.

[0925] Step 3:

[0926] The server analyzes the received JSON-formatted photo data and identifies the type and quantity of ingredients. The input is JSON-formatted image data, and the output is the analyzed ingredient information. The server uses the Google Cloud Vision API to analyze the image and identify the type and quantity of ingredients.

[0927] Step 4:

[0928] The server generates a nutritionally balanced recipe based on the analyzed ingredient information. The input is ingredient information, and the output is the generated recipe. The server uses an internal algorithm to generate a nutritionally balanced recipe from the input ingredient information.

[0929] Step 5:

[0930] The server analyzes facial expressions and voice tones to determine the user's emotional state. The input is the user's face photo and voice data, and the output is the user's emotional state data. The server identifies the emotional state using the Emotion API of Microsoft Azure Cognitive Services.

[0931] Step 6:

[0932] The server considers the emotional state data and proposes customized recipes and cooking methods. The input is the emotional state data and ingredient information, and the output is a customized recipe proposal. The server generates recipes and cooking methods that reflect the results of the emotion analysis.

[0933] Step 7:

[0934] The server generates a cooking process video based on the generated recipe. The input is recipe information, and the output is a cooking process video. The server uses FFmpeg to combine video clips corresponding to each cooking step to generate a continuous cooking process video.

[0935] Step 8:

[0936] The server returns the generated cooking process video to the terminal. The input is the cooking process video, and the output is the video data sent to the user terminal. The server sends the generated video data to the terminal as an HTTP response.

[0937] Step 9:

[0938] The server identifies missing ingredients and orders additional ingredients by connecting with an external shopping app. The input is a list of ingredients required for the recipe, and the output is the order information. The server sends the list of missing ingredients via the external shopping app's API and processes the order.

[0939] Step 10:

[0940] The device displays the cooking process video received. The input is the received video data, and the output is the video played on the device screen. The device plays the received video data to provide the user with visual cooking support.

[0941] Step 11:

[0942] The device changes the tone of the video narration and the color of the screen display according to the user's emotional state. The input is emotional state data and video data, and the output is a customized video display. The device adjusts the narration and screen settings to match the user's emotional state, providing a more relaxing experience.

[0943] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0944] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0945] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[0946] [Fourth embodiment]

[0947] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0948] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0949] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0950] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0951] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0952] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0953] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0954] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0955] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0956] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0957] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0958] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0959] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[0960] The present invention is a system that takes photos of ingredients in a refrigerator, analyzes the image data, generates suitable dish options and recipes, and provides videos of the cooking process. Detailed embodiments of the system are described below.

[0961] First, a dedicated application is installed on a user's smartphone, tablet, or other device. The user uses this application to take a photo of the food in the refrigerator. Once the photo is taken, the device automatically converts the photo data into JSON format and sends it to a server via the Internet.

[0962] The photo data received by the server is first analyzed using an image analysis algorithm. During this process, the server identifies the types and amounts of ingredients. The identified ingredient information is then input into a recipe generation algorithm that takes nutritional balance into consideration. This allows the server to generate the best possible recipes for the user.

[0963] Based on the generated recipe, the server generates a video of the cooking process. The server retrieves video clips corresponding to each cooking step from the database and combines them to create a continuous video of the cooking process. This video includes detailed instructions for each cooking step, a timer function, and audio narration.

[0964] Next, the server checks the list of all necessary ingredients based on the generated recipe. If any ingredients that the user does not have are identified, the server connects the list to an external shopping app and provides the user with the option to order additional ingredients. The user can easily purchase the missing ingredients through the shopping app.

[0965] Finally, the server sends the generated recipe and cooking process video back to the device. The device receives this data and displays it on the application screen. The user then proceeds with the cooking process based on the displayed information. Furthermore, users of smart home devices can project the cooking process video from the device screen onto the wall. This function frees up the user's hands and allows for more efficient cooking.

[0966] For example, if a user takes a photo of "tomatoes," "chicken," and "cheese" in the refrigerator, the server analyzes this information and generates a recipe for "chicken and tomato pasta." Based on the recipe, the server then creates a video including instructions on how to bake chicken, how to cut tomatoes, how to boil pasta, and so on, providing detailed step-by-step instructions to help the user with the cooking process.

[0967] With the above configuration, the present invention is a system that allows users to efficiently plan menus and easily prepare nutritionally balanced meals. In addition, the system's ability to order ingredients in short supply reduces the risk of abandoning a cooking project due to lack of necessary ingredients.

[0968] The processing flow will be explained below.

[0969] Step 1:

[0970] The user takes a photo of the ingredients in the refrigerator. The user takes a photo of the ingredients using the camera on their smartphone or tablet, and the image data is saved by a dedicated application.

[0971] Step 2:

[0972] The device converts the captured photo data into JSON format and sends it to the server. The device then sends the photo data to the server via the Internet as an HTTP request.

[0973] Step 3:

[0974] The server analyzes the received photo data. After the server receives the photo data, it launches an image analysis algorithm to identify the type and amount of ingredients.

[0975] Step 4:

[0976] The server generates a recipe based on the analysis results, selecting and generating a nutritionally balanced recipe suitable for the identified ingredients from a recipe database stored on the server.

[0977] Step 5:

[0978] The server generates a cooking process video. Based on the generated recipe, the server combines video clips of each cooking step to create a detailed cooking process video. This video also includes audio commentary and a timer function.

[0979] Step 6:

[0980] The server identifies missing ingredients. The server checks the list of ingredients required for the generated recipe and compares it with the list of ingredients the user already has to identify missing ingredients.

[0981] Step 7:

[0982] The server provides a function to order missing ingredients. The server connects to an external shopping app and provides users with a link or option to order missing ingredients.

[0983] Step 8:

[0984] The server returns the generated recipe and cooking process video to the device, and the server sends the recipe and video data to the user's device as an HTTP response.

[0985] Step 9:

[0986] The device displays the received data to the user, and the device application displays the recipe and cooking process video in an easy-to-understand manner for the user, who can refer to it while cooking.

[0987] Step 10:

[0988] If necessary, the device can project cooking process videos onto the wall via smart home devices, allowing users to check the cooking steps while keeping their hands free.

[0989] By using the above process steps, users can efficiently plan their menu and prepare nutritionally balanced meals while referring to detailed cooking instructions. Even if ingredients are in short supply, they can be quickly ordered, reducing the hassle of cooking.

[0990] Example 1

[0991] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[0992] With conventional cooking assistance systems, it was difficult for users to effectively use the ingredients in their refrigerator and efficiently prepare nutritionally balanced meals. Furthermore, if an ingredient was insufficient, it was time-consuming to purchase additional ingredients, which was inconvenient for users. Furthermore, the cooking steps could be difficult to understand, which also created an issue of cooking not proceeding smoothly.

[0993] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0994] In this invention, the server includes a means for taking photos of ingredients, a means for converting the photos of ingredients into a data format and sending them to the server, a means for analyzing the types and quantities of ingredients on the server, a means for generating a nutritionally balanced recipe based on the analysis results, a means for generating a video of the cooking process based on the generated recipe, a means for identifying missing ingredients and ordering additional ingredients in cooperation with an external e-commerce system, and a means for returning the generated recipe and cooking process video to the terminal. This allows the user to easily prepare a nutritionally balanced meal using ingredients in the refrigerator and efficiently purchase missing ingredients. Furthermore, the video providing detailed instructions on the cooking process allows the user to smoothly proceed with the cooking process.

[0995] "Means for taking photos of ingredients" refers to equipment or functions for capturing digital images of ingredients in the refrigerator.

[0996] "Means for converting photographs of ingredients into a data format and sending it to a server" refers to devices or functions for converting acquired photos of ingredients into structured data such as JSON format and sending that data to a server via the Internet.

[0997] "Means for analyzing the type and amount of ingredients on the server" refers to image recognition algorithms or software that analyzes image data received by the server and identifies the specific type and amount of ingredients.

[0998] "Means for generating recipes that take nutritional balance into consideration based on analysis results" refers to algorithms and software for generating optimal recipes that take nutritional balance into consideration based on identified ingredient information.

[0999] "Means for generating cooking process videos based on the generated recipe" refers to equipment or software that acquires and edits video clips to create a continuous cooking process video in order to provide cooking steps corresponding to the generated recipe in video format.

[1000] "Means of identifying missing ingredients and linking with an external e-commerce system to order additional ingredients" refers to a function that identifies ingredients that the user does not have and links with the e-commerce platform to complete the purchase process for the missing ingredients.

[1001] "Means for returning the generated recipe and cooking process video to the terminal" refers to the function for sending the recipe and cooking process video generated by the server to the user's terminal and displaying that information.

[1002] "Smart home appliances" refer to electronic devices that are connected to a network and provide various functions through voice control, touch control, etc.

[1003] A "structured data format" is a data format that organizes information in a specified format and makes it easy for machines to process. JSON format is a common example.

[1004] MODE FOR CARRYING OUT THE INVENTION

[1005] The present invention is a system that takes photos of ingredients in a refrigerator, analyzes the image data, generates suitable dish candidates and recipes, and provides videos of the cooking process. Specific embodiments of the system are described below.

[1006] First, a dedicated application is installed on a user's smartphone, tablet, or other device. The user uses this application to take photos of the ingredients in the refrigerator. For example, the user opens the refrigerator and takes photos of "tomatoes," "chicken," and "cheese." Once the photos are taken, the device automatically converts the photo data into JSON format and sends it to a server via the Internet.

[1007] The photo data received by the server is first analyzed using an image analysis algorithm. This analysis uses common image recognition software. Specifically, the server identifies the type and quantity of ingredients. For example, specific data such as "3 tomatoes," "500g of chicken," and "200g of cheese" is extracted.

[1008] Based on the identified ingredient information, the server uses a recipe generation algorithm that takes nutritional balance into account to generate optimal dish candidates and recipes. This process utilizes a generative AI model. Specifically, models such as OpenAI are used. This algorithm generates recipes taking into account the user's usage history and the freshness of the ingredients. For example, a recipe for "chicken and tomato pasta" is generated.

[1009] Based on the generated recipe, the server generates a video of the cooking process. The server retrieves video clips corresponding to each cooking step from the database and combines them to create a continuous cooking process video. This process is performed using video editing software such as Adobe Premiere Pro. The video includes detailed explanations of each cooking step, a timer function, and audio narration.

[1010] Next, the server checks the list of all ingredients required based on the generated recipe. If any ingredients that the user does not have are identified, the server connects the list to an external e-commerce system and provides the user with options for ordering additional ingredients. For example, it uses online shopping platforms such as Amazon Fresh or Rakuten Ichiba. This allows the user to easily purchase the missing ingredients.

[1011] Finally, the server sends the generated recipe and cooking process video back to the device. The device receives this data and displays it on the application screen. The user then proceeds with the cooking process based on the displayed information. Furthermore, users of smart home appliances can project the cooking process video from the device screen onto the wall. This function frees up the user's hands and makes cooking more efficient.

[1012] For example, if a user takes a photo of "tomatoes," "chicken," and "cheese" in the refrigerator, the server analyzes this information and generates a recipe for "chicken and tomato pasta." Based on the recipe, the server then creates a video that includes instructions on how to bake chicken, how to cut tomatoes, how to boil pasta, and other steps, providing detailed step-by-step instructions that will help the user as they cook.

[1013] The following are examples of specific prompt sentences:

[1014] Take a photo of "tomatoes," "chicken," and "cheese" in your refrigerator and generate a recipe and cooking video using these ingredients.

[1015] With the above configuration, the present invention is a system that allows users to efficiently plan menus and easily prepare nutritionally balanced meals. In addition, the system's ability to order ingredients in short supply reduces the risk of abandoning a cooking project due to lack of necessary ingredients.

[1016] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1017] Step 1:

[1018] The user takes a photo of the ingredients in the refrigerator using a smartphone or tablet. The user then launches the application and takes a photo of an ingredient, such as "tomato," "chicken," or "cheese." The input is the photo of the ingredient, and the output is a digital image file.

[1019] Step 2:

[1020] The device automatically converts photos taken into JSON format. Specifically, an image analysis module in the application reads the photos and converts them into a format that can be processed as digital data. The input is a digital image file, and the output is JSON-formatted data.

[1021] Step 3:

[1022] The device sends the converted JSON-formatted photo data to the server via the Internet. This operation is performed through a communication module in the application. The input is JSON-formatted data, and the output is a data stream sent to the server.

[1023] Step 4:

[1024] The server analyzes the received photo data. This analysis is performed using an image recognition algorithm. The server uses image recognition tools such as Amazon Rekognition or Google Cloud Vision to identify the type and quantity of ingredients. The input is JSON-formatted photo data, and the output is ingredient information (e.g., "3 tomatoes," "500g chicken," and "200g cheese").

[1025] Step 5:

[1026] The server generates a recipe that takes nutritional balance into consideration based on the identified ingredient information. A generative AI model is used here. The server receives ingredient information as input and uses a generative AI model such as OpenAI to generate nutritionally balanced recipe candidates. The input is ingredient information, and the output is recipe data (e.g., a recipe for "chicken and tomato pasta").

[1027] Step 6:

[1028] The server generates a cooking process video based on the generated recipe. The server retrieves video clips corresponding to each cooking step from the database and connects them using video editing software (e.g., Adobe Premiere Pro) to create a continuous cooking process video. The input is recipe data, and the output is a cooking process video.

[1029] Step 7:

[1030] The server checks the list of all required ingredients based on the generated recipe. The server lists the ingredients included in the recipe and identifies any ingredients that the user does not currently have. The input is the recipe data and the user's ingredient information, and the output is the list of missing ingredients.

[1031] Step 8:

[1032] The server connects the list of ingredients in short supply to an external e-commerce system and provides users with options for ordering more. Specifically, it connects with Amazon Fresh, Rakuten Ichiba, and other systems to provide links and interfaces for purchasing ingredients in short supply. The input is the list of ingredients in short supply, and the output is external links and purchasing options.

[1033] Step 9:

[1034] The server returns the generated recipe and cooking process video to the device. The server then sends this data to the device via the Internet, and the device displays the received data on the application screen. The input is the recipe data and cooking process video data, and the output is a data stream to the device.

[1035] Step 10:

[1036] The user proceeds with cooking based on the displayed information. The user cooks while referring to the recipe and video displayed on the application. If necessary, the cooking process video can be projected onto the wall using smart home appliances. This process allows the user to cook efficiently while keeping their hands free. The input is the recipe and video displayed on the device, and the output is the finished dish.

[1037] (Application example 1)

[1038] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1039] In recent years, there has been a demand for easy preparation of nutritionally balanced meals. There is also a need for systems that can determine the amount of a certain ingredient in the refrigerator, suggest appropriate recipes based on that information, and provide easy-to-understand cooking instructions. However, current systems do not fully integrate these functions, and lack the support users need to easily prepare nutritionally balanced meals. Furthermore, there are few systems that can automatically order missing ingredients all at once. In addition, providing an environment in which both hands can be used freely when actually following the cooking instructions is an important challenge.

[1040] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1041] In this invention, the server includes a means for capturing images of ingredients, a means for transmitting the captured images of ingredients to the server, a means for analyzing the types and quantities of ingredients on the server, a means for generating cooking instructions that take nutritional balance into consideration based on the analysis results, a means for generating a video of the cooking process based on the generated cooking instructions, a means for identifying missing ingredients and ordering additional ingredients in cooperation with an external system, a means for returning the generated cooking instructions and the video of the cooking process to the terminal, a means for displaying the analyzed ingredients and the generated cooking instructions on the smart device, and a means for displaying the video of the cooking process. This allows users to effectively use the ingredients in their refrigerator and easily prepare nutritionally balanced meals. Furthermore, the automatic ordering of missing ingredients and the display and projection of video of the cooking process on the smart device improve convenience during cooking.

[1042] "Means for taking pictures of ingredients" refers to devices or applications that allow users to easily take pictures of ingredients in the refrigerator.

[1043] "Means for transmitting photographed images of ingredients to a server" refers to a mechanism for transmitting photographed image data from a user's device to a server via the Internet.

[1044] "Means for analyzing the types and amounts of ingredients on the server" refers to an algorithm or program that analyzes the received image data and automatically identifies and measures the types and amounts of ingredients contained therein.

[1045] "Means for generating cooking instructions that take nutritional balance into consideration based on the analysis results" refers to an algorithm that automatically generates nutritionally balanced recipes based on analyzed ingredient information.

[1046] "Means for generating a video of the cooking process based on the generated cooking instructions" refers to a function for providing each step of the generated recipe as a video, and refers to a mechanism for combining the necessary video clips and presenting them in a form that is easy for the user to understand.

[1047] "Means of identifying missing ingredients and linking with external systems to order additional ingredients" refers to a system that identifies missing ingredients from the information on ingredients the user has on hand and links with external shopping systems or food delivery systems to place additional orders.

[1048] "Means for returning the generated cooking instructions and cooking process video to the terminal" refers to a mechanism for sending the generated recipe information and cooking process video data to the user's device and displaying them.

[1049] "Means for displaying analyzed ingredients and generated cooking instructions via a smart device" refers to the function for displaying the analysis results and generated recipes on a smart device such as a smartphone or tablet.

[1050] "Means for displaying cooking process videos" refers to a function that plays videos of cooking steps on the user's device to make them visually easy to understand.

[1051] This invention is a system that takes photos of ingredients in the refrigerator, analyzes the image data, generates suitable dish suggestions and recipes, and provides videos of the cooking process. This system combines ingredient management and cooking support, allowing users to easily prepare nutritionally balanced meals.

[1052] First, a dedicated application is installed on the user's device (such as a smartphone or tablet). Using this application, the user takes a photo of the ingredients in the refrigerator. The device reads the photo data, converts it into JSON format, and sends it to a server via the Internet.

[1053] The server analyzes the received photo data. Specifically, it uses an image analysis algorithm to identify the types and amounts of ingredients contained in the photo. This analysis is performed using a generative AI model, which is trained on images of many different ingredients as initial learning data. The analyzed ingredient information is then input into a recipe generation algorithm that takes nutritional balance into account. This algorithm also uses a generative AI model to generate optimal recipe suggestions based on the user's preferences and health condition.

[1054] Based on the generated recipe, the server generates a cooking process video. This process involves retrieving video clips corresponding to each cooking step from the database and combining them appropriately to create a continuous cooking process video. The video includes detailed instructions for each cooking step, a timer function, and audio narration.

[1055] The server then checks the list of all ingredients required based on the generated recipe. If any ingredients are missing, the server connects the list to an external shopping system and provides the user with options for ordering additional ingredients. In this case, the user can easily order additional ingredients by connecting to a shopping app.

[1056] Finally, the server sends the generated recipe and cooking process video back to the user's device and displays it in the application. The user can use the displayed information to cook efficiently. In addition, users with smart home devices can project the cooking process video onto a wall from the application screen. This function frees up both hands, making cooking even more efficient.

[1057] For example, if a user takes a photo of "tomatoes," "chicken," and "cheese" in the refrigerator, the server analyzes these ingredients and generates a recipe for "chicken and tomato pasta." Based on that recipe, the server also creates a video including instructions on how to grill chicken, how to cut tomatoes, how to boil pasta, and so on, and provides it to the user.

[1058] An example prompt is:

[1059] Please analyze the following image to identify the food items and quantities present. Return a JSON object with the list of identified food items and their quantities.

[1060] With the above configuration, the present invention allows users to efficiently plan menus and easily prepare nutritionally balanced meals. In addition, the lack of ingredients ordering function reduces the problem of abandoning a cooking project due to lack of necessary ingredients.

[1061] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1062] Step 1:

[1063] Users use devices such as smartphones and tablets to take pictures of the food items in their refrigerators, which are then saved on the device.

[1064] Step 2:

[1065] The device converts the captured image into JSON format and sends it to a server via the Internet. The input is image data, and the output is JSON format data.

[1066] Step 3:

[1067] The server analyzes the received JSON formatted image data. It uses an image analysis algorithm to identify the types and quantities of ingredients in the image. The input is JSON formatted image data, and the output is a list of the types and quantities of ingredients.

[1068] Step 4:

[1069] The server generates cooking instructions that take nutritional balance into consideration based on the identified ingredient information. Using a generative AI model, it automatically generates optimal recipes that match the user's preferences and health status. The input is ingredient information, and the output is cooking instructions (recipe).

[1070] Step 5:

[1071] Based on the cooking instructions, the server retrieves video clips of each cooking step from the database and creates a continuous cooking process video. The input is the cooking instructions, and the output is the cooking process video.

[1072] Step 6:

[1073] The server checks the list of all necessary ingredients based on the generated recipe. It identifies missing ingredients and connects with an external shopping system to provide the user with options for ordering additional ingredients. The input is the identified ingredient information and the recipe, and the output is the list of missing ingredients and a shopping link.

[1074] Step 7:

[1075] The server generates cooking instructions and video of the cooking process and returns them to the user's terminal. The input is cooking instructions and video of the cooking process, and the output is reply data containing these data.

[1076] Step 8:

[1077] The cooking instructions and cooking process video received by the device are displayed on the application screen. The user can proceed with cooking based on the displayed information. The input is data from the server, and the output is the screen display that the user can see.

[1078] Step 9:

[1079] Users can also use smart home devices to project cooking process videos onto the wall. This function transmits data from the terminal to the smart home device to perform video projection. The input is data from the terminal, and the output is the projected video.

[1080] The above are the specific processing steps of this system.

[1081] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1082] The present invention is a system that takes photos of ingredients in a refrigerator, analyzes the image data to generate suitable dish candidates and recipes, and further provides videos of the cooking process, by combining this with an emotion engine to provide customized support according to the user's emotional state. Detailed embodiments of the system are described below.

[1083] First, the user installs a dedicated application on their smartphone, tablet, or other device. Using this application, the user takes a photo of the ingredients in the refrigerator. Once the photo is taken, the device automatically converts the photo data into JSON format and sends it to a server via the Internet.

[1084] The photo data received by the server is analyzed using an image analysis algorithm to identify the types and amounts of ingredients. The identified ingredient information is then input into a recipe generation algorithm that takes nutritional balance into consideration, and the server generates the best possible dishes and recipes for the user.

[1085] Based on the generated recipe, the server generates a cooking process video. It retrieves video clips corresponding to each cooking step from the database and combines them to create a continuous cooking process video. This video includes audio commentary and a timer function.

[1086] Next, the server checks the list of all ingredients required based on the generated recipe. If any ingredients that the user does not have are identified, the server connects the list to an external shopping app and provides the user with the option to order more. The user can easily purchase the missing ingredients through the shopping app.

[1087] The server sends the generated recipe and cooking process video back to the device. The device receives this data and displays it on the application screen. The user proceeds with the cooking process based on the displayed information. In addition, users of smart home devices can project the cooking process video from the device screen onto the wall.

[1088] A notable feature of this invention is its incorporation of an emotion engine. The emotion engine recognizes the user's facial expressions and vocal tone to measure their emotional state (e.g., joy, sadness, stress). This emotion data is sent to the server and reflected in the criteria for recipe generation. For example, if the user is feeling stressed, the system will prioritize suggestions for dishes that have a relaxing effect or are easy to prepare.

[1089] The cooking process videos are also customized based on the user's emotional data. If the user is feeling anxious, the voice narration in the video will be changed to a more polite and calm tone. The color and font of the screen display can also be adjusted according to the user's emotional state.

[1090] For example, if a user takes a photo of "tomatoes," "chicken," and "cheese" in the refrigerator, the server analyzes the photo and generates a recipe for "chicken and tomato pasta." If the emotion engine recognizes that the user is tired, it will also suggest an easy-to-make "tomato salad." The voice narration for the generated video will also be set to a gentler tone than usual.

[1091] As described above, the present invention is a system that takes into account the user's emotional state, efficiently determines menus, and allows users to prepare nutritionally balanced meals while referring to detailed cooking instructions. Furthermore, the ordering function for missing ingredients reduces the problem of abandoning cooking due to lack of necessary ingredients. The introduction of an emotion engine makes it possible to provide cooking support that is more tailored to each individual user, reducing stress and increasing satisfaction.

[1092] The processing flow will be explained below.

[1093] Step 1:

[1094] The user takes a photo of the ingredients in the refrigerator. The user takes a photo of the ingredients using the camera on their smartphone or tablet, and the image data is saved by a dedicated application.

[1095] Step 2:

[1096] The device converts the captured photo data into JSON format and sends it to the server. The device then sends the photo data to the server via the Internet as an HTTP request.

[1097] Step 3:

[1098] The server analyzes the received photo data. After the server receives the photo data, it launches an image analysis algorithm to identify the type and amount of ingredients.

[1099] Step 4:

[1100] The server generates a recipe based on the analysis results, selecting and generating a nutritionally balanced recipe suitable for the identified ingredients from a recipe database stored on the server.

[1101] Step 5:

[1102] The server generates a cooking process video. Based on the generated recipe, the server combines video clips of each cooking step to create a detailed cooking process video. This video also includes audio commentary and a timer function.

[1103] Step 6:

[1104] The device uses an emotion engine to recognize the user's facial expressions and voice tone, and sends the emotion data to the server. The emotion engine determines the user's emotional state from the user's facial expressions, and the device then sends that data to the server.

[1105] Step 7:

[1106] The server customizes recipes and cooking steps based on the user's emotional data. The server adjusts recipe selection criteria based on the user's emotional state. For example, if the user is feeling stressed, it will prioritize dishes that have a relaxing effect or are easy to make.

[1107] Step 8:

[1108] The server customizes cooking process videos based on emotional data. If the user feels anxious, the voice narration in the video will be changed to a more polite and calm tone. The color and font of the screen display will also be adjusted.

[1109] Step 9:

[1110] The server identifies missing ingredients. The server checks the list of ingredients required for the generated recipe and compares it with the list of ingredients the user already has to identify missing ingredients.

[1111] Step 10:

[1112] The server provides a function to order missing ingredients. The server connects to an external shopping app and provides users with a link or option to order missing ingredients.

[1113] Step 11:

[1114] The server returns the generated recipe and cooking process video to the device, and the server sends the recipe and video data to the user's device as an HTTP response.

[1115] Step 12:

[1116] The device displays the received data to the user, and the device application displays the recipe and cooking process video in an easy-to-understand manner for the user, who can refer to it while cooking.

[1117] Step 13:

[1118] If necessary, the device can project cooking process videos onto the wall via smart home devices, allowing users to check the cooking process while keeping their hands free.

[1119] Through these processing steps, users can efficiently plan their menu and prepare nutritionally balanced meals while referring to detailed cooking instructions. Even if ingredients are in short supply, they can be quickly ordered, reducing the hassle of cooking. Furthermore, by incorporating an emotion engine, the system can provide customized cooking support tailored to each individual user's condition, reducing stress and increasing satisfaction.

[1120] Example 2

[1121] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1122] Conventional cooking support systems lack functionality to improve user satisfaction, as they are not adequately customized to take into account the user's emotional state. Furthermore, they have limited functionality for identifying missing ingredients and linking with external systems to order additional ingredients, making it difficult for users to easily obtain the ingredients they need. Furthermore, generating and displaying cooking procedure videos has also been an issue in some environments.

[1123] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1124] In this invention, the server includes means for taking photos of ingredients, means for converting the photos of ingredients into a data format and sending the data to the server, means for analyzing the types and amounts of ingredients on the server, means for generating a recipe that takes nutritional balance into consideration based on the analysis results, means for generating a video of cooking steps based on the generated recipe, means for identifying missing ingredients and ordering additional ingredients in cooperation with an external system, means for returning the generated recipe and video of cooking steps to the terminal, and means for recognizing the user's emotional state using an emotion engine and customizing the recipe and video of cooking steps based on the recognized emotional state. This makes it possible to propose recipes and provide video of cooking steps that are customized according to the user's emotional state, making it easy to obtain the necessary ingredients and check the cooking steps.

[1125] The "means for taking photos of ingredients" is a mechanism for acquiring images of ingredients in the refrigerator using the camera of the user's device.

[1126] The "means of converting into a data format and sending to a server" refers to a mechanism for converting the image data of photographed ingredients into a standard data format such as JSON and sending it to a server via the Internet.

[1127] "Means for analyzing the type and amount of ingredients on the server" refers to a system that uses an algorithm (e.g., YOLO) to analyze photo data sent to the server and identify the type and amount of ingredients in the photo.

[1128] The "means for generating recipes that take into account nutritional balance" is an algorithm for generating optimal recipes that take into account the user's health and nutritional balance based on analyzed ingredient information.

[1129] The "means for generating video of cooking steps" is a mechanism for providing visual instructions to the user by assembling video clips showing the necessary cooking steps based on the recipe.

[1130] "Means to identify missing ingredients and order additional ingredients by connecting with an external system" refers to a system that checks a list of all necessary ingredients based on the generated recipe, identifies missing ingredients that the user does not have, and connects with an external shopping system, etc., to make it possible to purchase additional ingredients.

[1131] "Means for returning the generated recipe and cooking procedure video to the terminal" refers to a mechanism by which the server sends the generated recipe and video to the user's terminal, allowing the user to easily access them.

[1132] "Means for recognizing a user's emotional state using an emotion engine and customizing recipe and cooking procedure videos based on the recognized emotional state" refers to an engine that analyzes a user's facial expressions and voice tone to identify their emotional state and provides recipes, narration, and display formats that are optimal for that emotional state.

[1133] MODE FOR CARRYING OUT THE INVENTION

[1134] The present invention is a system that takes pictures of ingredients in a refrigerator, analyzes the image data, generates suitable dish candidates and recipes according to the user's emotional state, and provides videos of the cooking process. Detailed embodiments of the system are described below.

[1135] First, the user installs a dedicated application on their smartphone, tablet, or other device. Using this application, the user takes a photo of the food in their refrigerator. Once the photo is taken, the device automatically converts the photo data into JSON format and sends it to a server via the Internet.

[1136] The server analyzes the received photo data using the image analysis algorithm "YOLO (You Only Look Once)." This identifies the ingredients in the photo. The identified ingredient information is then input into "Chef Watson," a recipe generation algorithm that takes nutritional balance into consideration, to generate optimal dish candidates and their recipes.

[1137] Based on the generated recipe, the server retrieves video clips corresponding to the cooking steps using the Vimeo API and combines them in the appropriate order to create a continuous cooking process video, which also includes audio commentary and a timer function.

[1138] Furthermore, the server checks the list of all ingredients required based on the generated recipe. If any ingredients are missing, the server connects to the shopping system using the Amazon API and provides the user with the option to order more. The user can then easily purchase the missing ingredients through the shopping system.

[1139] The server then sends the generated recipe and cooking process video back to the device. The device receives this data and displays it on the application screen. The user can proceed with cooking based on the displayed information. In addition, if the smart home device "Google Nest Hub" is used, the cooking process video can be projected onto the wall.

[1140] A notable feature of this invention is its incorporation of an emotion engine ("Affectiva SDK"). The emotion engine recognizes the user's facial expressions and vocal tone to measure their emotional state (e.g., joy, sadness, stress). This emotion data is sent to the server and reflected in recipe generation and customization of cooking process videos. For example, if the user is feeling stressed, dishes that have a relaxing effect or are easy to make will be prioritized. In addition, the audio narration of the generated video and the color and font of the screen display are also adjusted according to the user's emotional state.

[1141] For example, a user can take a photo of "tomatoes," "chicken," and "cheese" in their refrigerator, and the server will analyze the photos and generate a recipe for "chicken and tomato pasta." If the emotion engine recognizes that the user is tired, it will also suggest an easy-to-make "tomato salad." The generated video will also have a gentle voice narration.

[1142] An example prompt can be entered in the following text format:

[1143] "I have tomatoes and chicken in the fridge. I'm tired, so please suggest some easy meals."

[1144] This system allows users to obtain customized cooking recipes based on their emotional state, easily purchase the necessary ingredients, and enjoy cooking with reduced stress by viewing detailed cooking instruction videos.

[1145] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1146] Program processing steps

[1147] Step 1:

[1148] The user installs a dedicated application on their smartphone or tablet.

[1149] Input: None

[1150] Output: The application is installed on the device.

[1151] Specific operation: The user downloads a dedicated application from the app store and installs it on their device.

[1152] Step 2:

[1153] The user launches the application and takes a photo of the ingredients in the refrigerator.

[1154] Input: Food in the refrigerator

[1155] Output: Photograph of the ingredients

[1156] What happens: The user opens the app and uses the in-app camera to take a picture of the ingredients in the refrigerator.

[1157] Step 3:

[1158] The device converts the captured photo data into JSON format.

[1159] Input: A photo of the food

[1160] Output: Photo data in JSON format

[1161] What happens: The application internally parses the image data and converts the corresponding data into JSON format.

[1162] Step 4:

[1163] The terminal converts the data into JSON format and sends it to the server via the Internet.

[1164] Input: JSON format photo data

[1165] Output: Photo data sent to the server

[1166] What happens: The device uses an internet connection to upload the converted data to the server.

[1167] Step 5:

[1168] The photo data received by the server is analyzed using the image analysis algorithm "YOLO."

[1169] Input: JSON format photo data

[1170] Output: Type and amount of ingredients

[1171] What it does: The server uses the "YOLO" algorithm to analyze the image data sent and identify the types of ingredients in the photo and their quantities.

[1172] Step 6:

[1173] The server uses the "Chef Watson" algorithm based on the analysis results to generate recipes that take nutritional balance into consideration.

[1174] Input: Type and amount of ingredients

[1175] Output: Best food suggestions and recipes

[1176] Specific operation: The server uses "Chef Watson" to generate the optimal recipe based on the identified ingredient information.

[1177] Step 7:

[1178] The server uses an emotional engine ("Affectiva SDK") to recognize the user's emotional state and customize the recipe.

[1179] Input: User's facial expressions and voice tone

[1180] Output: Customized food suggestions and recipes based on sentiment data

[1181] Specific operation: The server uses the emotion engine to analyze the user's emotional state at the time of taking the photo and reflects the results in recipe generation.

[1182] Step 8:

[1183] The server generates a cooking procedure video based on the generated recipe.

[1184] Input: Customized Recipe

[1185] Output: Cooking video with any combination of steps

[1186] Specific operation: The server uses the Vimeo API or similar to obtain video clips corresponding to cooking steps, and then combines them in the appropriate order to generate a continuous video.

[1187] Step 9:

[1188] The server checks the list of ingredients needed, connects with an external shopping system to identify ingredients that are missing, and presents options for ordering more.

[1189] Input: Generated recipe and user's ingredients

[1190] Output: List of missing ingredients and options to order more

[1191] Specific operation: The server identifies the ingredients that are in short supply using the Amazon API or similar and gives the user the option to purchase more.

[1192] Step 10:

[1193] The server sends the final recipe and cooking instruction video to the device.

[1194] Input: Customized recipes and cooking instructions videos

[1195] Output: Recipe and video data sent to the device

[1196] Specific operation: The server sends the generated recipes and videos to the device, making them viewable in the application.

[1197] Step 11:

[1198] The data received by the device is displayed on the application screen, and if necessary, a video is projected onto the wall using a smart home device.

[1199] Input: Received recipe and cooking instructions video

[1200] Output: Recipes and videos displayed on the application screen or projected onto smart home devices

[1201] How it works: Users can view recipes and videos in the app and can also use smart home devices to project cooking instruction videos onto the wall.

[1202] This will enable the system to propose recipes and provide cooking procedure videos customized to the user's emotional state, allowing them to easily obtain the necessary ingredients and check the cooking procedures.

[1203] (Application example 2)

[1204] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1205] In today's society, preparing nutritiously balanced meals at home despite leading busy daily lives is a major challenge for many people. Users, especially those living in hectic schedules and stressful environments, often find it difficult to find the time and energy to cook. As a result, many resort to eating out or unhealthy eating habits. Furthermore, when ingredients are scarce, it can be a hassle to go shopping. Furthermore, the lack of appropriate recipe suggestions based on emotional state makes it difficult to improve user satisfaction.

[1206] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for analyzing the user's emotional state and proposing customized recipes and cooking methods according to the emotional state, means for generating recipes that take nutritional balance into consideration based on the analysis results, and means for generating videos of cooking steps based on the generated recipes. This enables personalized cooking suggestions that take into account the user's emotional state and stress level. As a result, the user can have a more relaxed and enjoyable cooking experience and be supported in maintaining a healthy diet. Furthermore, even if necessary ingredients are in short supply, additional ingredients can be quickly ordered in cooperation with an external app, reducing the effort required to obtain ingredients.

[1207] The "means for taking photos of ingredients" is a function for acquiring image data of ingredients in the refrigerator using an electronic device owned by the user.

[1208] The "means for transmitting the photographed foodstuffs to the server" is a communication function for transferring the acquired foodstuffs image data from the electronic device to the server.

[1209] The "means for analyzing the type and amount of ingredients on the server" refers to an algorithm and program for analyzing the image data of ingredients uploaded to the server and identifying the type and amount of ingredients.

[1210] The "means for generating recipes that take nutritional balance into consideration" is a function that generates nutritionally balanced cooking recipes based on analyzed ingredient data.

[1211] The "means for generating videos of cooking processes" is a function that creates videos showing cooking steps and methods based on the generated recipe.

[1212] "Means for identifying missing ingredients and ordering additional ingredients in cooperation with an external application" is a function that identifies ingredients that the user does not have and orders the missing ingredients through an external shopping application.

[1213] "Means for analyzing the user's emotional state and suggesting recipes and cooking methods according to the emotional state" refers to a function that analyzes the user's emotions from their facial expressions and voice, and customizes cooking suggestions and cooking methods based on the analysis results.

[1214] The "means for returning the generated recipe and cooking process video to the terminal" is a function for sending the recipe and cooking method video generated by the server to the user's electronic device.

[1215] The "means for setting video narration and display according to emotional state" is a function for changing the tone of video narration and the color of display based on the user's emotional state.

[1216] This invention is a system that uses an application installed on a user's device (such as a smartphone or tablet) to take photos of ingredients in a refrigerator, and then sends the data to a server for analysis. Specifically, the invention is implemented in the following steps.

[1217] First, the user uses their device to take a photo of the food in their refrigerator. The captured image data is converted into JSON format by an application on the device and sent to a server over the Internet, using standard Internet protocols.

[1218] The server then uses AI image recognition technology, such as Google Cloud Vision API, to analyze the received image data. This identifies the types and amounts of ingredients. The analysis results are then input into a recipe generation algorithm that takes nutritional balance into account, generating the best possible dishes and recipes for the user.

[1219] Based on the generated recipe, the server generates a cooking process video using the FFmpeg library, taking video clips corresponding to each cooking step and combining them to create a continuous cooking process video, which also includes audio commentary and a timer function.

[1220] In addition, the server uses the Emotion API from Microsoft Azure Cognitive Services to analyze the user's emotional state. The system analyzes the user's facial expressions and vocal tone to identify their emotional state. This emotional data is reflected in the criteria for recipe generation; if the user is feeling stressed, it will prioritize dishes that are relaxing or easy to make. The generated cooking process videos are also customized based on the emotional data. The voice narration changes depending on the user's emotional state, and the color and font of the screen display are also adjusted.

[1221] For example, if a user takes a photo of "tomatoes," "chicken," and "cheese" in the refrigerator, the server analyzes the photo and generates a recipe for "chicken and tomato pasta." If the emotion engine recognizes that the user is tired, it will also suggest an easy-to-make "tomato salad." The voice narration for the video is set to a gentler tone than usual to help users relax.

[1222] If any ingredients are identified as being in short supply, the server will share the list with an external shopping app and provide the user with the option to order more. The user can then easily purchase the missing ingredients through the shopping app.

[1223] In this way, a system is realized that provides a stress-free cooking experience that suits the user's emotions and supports a healthy diet.

[1224] Example prompt sentence:

[1225] "Use an emotion engine to determine the user's emotional state and generate prompts for a food delivery app that suggests relaxing recipes."

[1226] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1227] Step 1:

[1228] The user uses a device (smartphone or tablet) to take a photo of the ingredients in the refrigerator. The input is image data captured by the camera, and the output is an image file saved on the device. The device takes the photo in response to the user's operation and saves the image data.

[1229] Step 2:

[1230] The device converts the photographed food photo data into JSON format and sends it to the server via the Internet. The input is an image file, and the output is JSON format data sent to the server. The device converts the image data into a JSON object and sends it to the server via an HTTP request.

[1231] Step 3:

[1232] The server analyzes the received JSON-formatted photo data and identifies the type and quantity of ingredients. The input is JSON-formatted image data, and the output is the analyzed ingredient information. The server uses the Google Cloud Vision API to analyze the image and identify the type and quantity of ingredients.

[1233] Step 4:

[1234] The server generates a nutritionally balanced recipe based on the analyzed ingredient information. The input is ingredient information, and the output is the generated recipe. The server uses an internal algorithm to generate a nutritionally balanced recipe from the input ingredient information.

[1235] Step 5:

[1236] The server analyzes facial expressions and voice tones to determine the user's emotional state. The input is the user's face photo and voice data, and the output is the user's emotional state data. The server identifies the emotional state using the Emotion API of Microsoft Azure Cognitive Services.

[1237] Step 6:

[1238] The server considers the emotional state data and proposes customized recipes and cooking methods. The input is the emotional state data and ingredient information, and the output is a customized recipe proposal. The server generates recipes and cooking methods that reflect the results of the emotion analysis.

[1239] Step 7:

[1240] The server generates a cooking process video based on the generated recipe. The input is recipe information, and the output is a cooking process video. The server uses FFmpeg to combine video clips corresponding to each cooking step to generate a continuous cooking process video.

[1241] Step 8:

[1242] The server returns the generated cooking process video to the terminal. The input is the cooking process video, and the output is the video data sent to the user terminal. The server sends the generated video data to the terminal as an HTTP response.

[1243] Step 9:

[1244] The server identifies missing ingredients and orders additional ingredients by connecting with an external shopping app. The input is a list of ingredients required for the recipe, and the output is the order information. The server sends the list of missing ingredients via the external shopping app's API and processes the order.

[1245] Step 10:

[1246] The device displays the cooking process video received. The input is the received video data, and the output is the video played on the device screen. The device plays the received video data to provide the user with visual cooking support.

[1247] Step 11:

[1248] The device changes the tone of the video narration and the color of the screen display according to the user's emotional state. The input is emotional state data and video data, and the output is a customized video display. The device adjusts the narration and screen settings to match the user's emotional state, providing a more relaxing experience.

[1249] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1250] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1251] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1252] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1253] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1254] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1255] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1256] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1257] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1258] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1259] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1260] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1261] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1262] 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.

[1263] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1264] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1265] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1266] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1267] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1268] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1269] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1270] The following is further disclosed regarding the above embodiment.

[1271] (Claim 1)

[1272] A means of taking photos of ingredients,

[1273] A means for transmitting the photograph of the food ingredient to a server;

[1274] A means of analyzing the type and amount of ingredients on the server,

[1275] A means for generating recipes that take nutritional balance into consideration based on the analysis results;

[1276] A means for generating a video of the cooking process based on the generated recipe;

[1277] A way to identify missing ingredients and order additional ingredients by connecting with an external app.

[1278] The system includes a means for returning the generated recipe and cooking process video to the terminal.

[1279] (Claim 2)

[1280] The system of claim 1, further comprising: means for projecting the generated cooking process video onto a wall through a smart home device.

[1281] (Claim 3)

[1282] The system of claim 1, further comprising means for transmitting photographic data of the photographed ingredients to a server in JSON format.

[1283] "Example 1"

[1284] (Claim 1)

[1285] A means of taking photos of ingredients,

[1286] A means for converting the photographed food product photos into a data format and transmitting the data to a server;

[1287] A means of analyzing the type and amount of ingredients on the server,

[1288] A means for generating recipes that take nutritional balance into consideration based on the analysis results;

[1289] A means for generating a video of the cooking process based on the generated recipe;

[1290] A means to identify missing ingredients and order additional ingredients by connecting with an external e-commerce system;

[1291] The system includes a means for returning the generated recipe and cooking process video to the terminal.

[1292] (Claim 2)

[1293] The system of claim 1, further comprising: means for projecting the generated cooking process video onto a wall through a smart home appliance.

[1294] (Claim 3)

[1295] The system of claim 1, further comprising means for transmitting photographic data of the photographed ingredients to the server in a structured data format.

[1296] "Application Example 1"

[1297] (Claim 1)

[1298] a means for taking an image of the food ingredient;

[1299] means for transmitting the photographed image of the ingredient to a server;

[1300] A means of analyzing the type and amount of ingredients on the server,

[1301] A means for generating cooking instructions that take nutritional balance into consideration based on the analysis results;

[1302] a means for generating a video of the cooking process based on the generated cooking instructions;

[1303] A means to identify missing ingredients and order additional ingredients by connecting with external systems;

[1304] A means for returning the generated cooking instructions and cooking process video to the terminal;

[1305] a means for displaying the analyzed ingredients and the generated cooking instructions through a smart device;

[1306] The system includes a means for displaying cooking process videos.

[1307] (Claim 2)

[1308] 10. The system of claim 1, further comprising: means for projecting cooking process video onto a wall through the smart device.

[1309] (Claim 3)

[1310] 10. The system of claim 1, further comprising means for transmitting the photographed image data of the ingredient to the server in a structured format.

[1311] "Example 2: Combining Emotion Engines"

[1312] (Claim 1)

[1313] A means of taking photos of ingredients,

[1314] A means for converting the photographed food product photos into a data format and transmitting the data to a server;

[1315] A means of analyzing the type and amount of ingredients on the server,

[1316] A means for generating recipes that take nutritional balance into consideration based on the analysis results;

[1317] A means for generating a video of a cooking procedure based on the generated recipe;

[1318] A means to identify ingredients that are in short supply and order additional ingredients by linking with external systems;

[1319] A means for returning the generated recipe and cooking procedure video to the terminal;

[1320] The system includes means for recognizing a user's emotional state using an emotion engine and customizing recipe and cooking instruction videos based on the recognized emotional state.

[1321] (Claim 2)

[1322] 10. The system of claim 1, further comprising means for projecting the generated cooking instruction video onto a wall through a smart home device.

[1323] (Claim 3)

[1324] 10. The system according to claim 1, further comprising means for transmitting photographic data of the photographed ingredients to a server in a data format.

[1325] "Application example 2 when combining emotion engines"

[1326] (Claim 1)

[1327] A means of taking photos of ingredients,

[1328] A means for transmitting the photograph of the food ingredient to a server;

[1329] A means of analyzing the type and amount of ingredients on the server,

[1330] A means for generating recipes that take nutritional balance into consideration based on the analysis results;

[1331] A means for generating a video of the cooking process based on the generated recipe;

[1332] A way to identify missing ingredients and order additional ingredients by connecting with an external app.

[1333] A means for analyzing the emotional state of a user and suggesting customized recipes and cooking methods according to the emotional state;

[1334] A means for returning the generated recipe and cooking process video to the terminal;

[1335] A means for setting video narration and display settings according to emotional state;

[1336] A system including:

[1337] (Claim 2)

[1338] The system of claim 1, further comprising: means for projecting the generated cooking process video onto a wall through a smart home device.

[1339] (Claim 3)

[1340] The system of claim 1, further comprising means for transmitting photographic data of the photographed ingredients to a server in JSON format. [Explanation of symbols]

[1341] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of taking photos of ingredients, A means for transmitting the photograph of the food ingredient to a server; A means of analyzing the type and amount of ingredients on the server, A means for generating recipes that take nutritional balance into consideration based on the analysis results; A means for generating a video of the cooking process based on the generated recipe; A way to identify missing ingredients and order additional ingredients by connecting with an external app. The system includes a means for returning the generated recipe and cooking process video to the terminal.

2. The system of claim 1 , further comprising: means for projecting the generated cooking process video onto a wall through a smart home device.

3. The system according to claim 1 , further comprising means for transmitting photographed foodstuff photo data to a server in JSON format.

Images