system

Abstract

We provide the system. [Solution] A means for users to input information about the dish, A means of transmitting that information to an artificial intelligence, A means by which a generative artificial intelligence analyzes input information and generates correction suggestions, A means of presenting those proposed revisions to the user, A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In home cooking, users often cannot cook according to the recipe. As a result, there is a problem that the taste is unbalanced and a satisfactory result cannot be achieved. Also, forgetting to buy seasonings or making mistakes in procedures are common problems. These problems are particularly likely to cause stress to users with little cooking experience and reduce the enjoyment of home cooking.

Means for Solving the Problems

[0005] This invention proposes a cooking rescue system utilizing generative artificial intelligence to solve such problems. The user inputs the progress of the cooking, the areas where mistakes occurred, and the available seasonings via a terminal, and the generative artificial intelligence analyzes this information. Based on the analysis results, the artificial intelligence provides optimal cooking correction suggestions, allowing the user to adjust the seasoning of the dish. In addition, this system provides the user with visual guidance, making it easy for the user to implement the suggested adjustments and minimize cooking failures.

[0006] "User" refers to an individual who uses the system or a person who prepares a home-cooked meal.

[0007] "Information about the dish" refers to detailed data on the dish name, any problems encountered during the cooking process, and the available seasonings.

[0008] "Generative artificial intelligence" refers to a set of computer programs and algorithms that can analyze input information and generate accurate correction suggestions.

[0009] "Analysis" refers to the process by which generative artificial intelligence interprets information about cooking and identifies problems.

[0010] A "proposal for improvement" refers to specific means or steps to resolve problems in the seasoning or cooking process of a dish.

[0011] A "device" refers to a device used by a user to input information and receive correction suggestions, such as a smartphone or tablet. [Brief explanation of the drawing]

[0012] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3]It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

Embodiments for Carrying Out the Invention

[0013] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described according to the accompanying drawings.

[0014] First, the language used in the following description will be explained.

[0015] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0016] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0017] In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0018] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor and 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), or Bluetooth (registered trademark), and the like.

[0019] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0020] [First Embodiment]

[0021] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0022] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0023] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0024] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0025] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0026] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0027] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0028] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0029] As shown in Figure 2, in the data processing device 12, specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0030] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0031] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0032] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0033] This invention is a system designed to rectify situations where a user has failed to season a dish properly while cooking. Specifically, it uses generative artificial intelligence to suggest the optimal seasoning method. The invention consists of a terminal used by the user, an artificial intelligence program implemented on a server, and data communication means.

[0034] The user enters the name of the dish, the current problem, and a list of seasonings they have on hand via the terminal. For example, if a user notices that beef stew is bland while making it, they would enter information such as "beef stew, bland, salt, sugar, olive oil."

[0035] This information is sent from the terminal to the server. The generative artificial intelligence on the server analyzes the input information and determines what modifications would be effective in improving the taste of the dish. Here, the generative AI refers to a database of dish characteristics and seasoning combinations to generate the optimal suggestion for solving the problem.

[0036] For example, if the AI ​​determines that "adding a little salt and increasing the simmering time by another 5 minutes would enhance the flavor," specific advice is sent from the server to the terminal. This advice is displayed in an easy-to-understand text format, allowing the user to adjust the flavor of the dish accordingly.

[0037] In this way, users can avoid wasting failed dishes and ultimately provide delicious meals. This system aims to solve common seasoning mistakes that occur in home cooking and provide the enjoyment of cooking, and is especially useful for users with little cooking experience.

[0038] The following describes the processing flow.

[0039] Step 1:

[0040] The user uses a terminal to enter the name of the dish, the current seasoning issue, and the condiments they have. For example, they might enter "Curry, Not spicy enough, Salt, Curry powder, Tomato paste."

[0041] Step 2:

[0042] The terminal verifies the entered information and confirms that all necessary information is present. If any information is missing, it prompts the user for additional input. After verification, it sends the information to the server.

[0043] Step 3:

[0044] The server passes the received information to the generating AI module for analysis. The generating AI then starts analyzing the database to provide solutions based on the characteristics of the dishes and the combinations of seasonings.

[0045] Step 4:

[0046] The generating AI uses the analysis results to create optimal solutions to the input problem. For example, it might create specific suggestions such as, "To increase the spiciness, add 1 teaspoon of curry powder and a small amount of tomato paste."

[0047] Step 5:

[0048] The server sends the generated revisions to the terminal. The data sent is converted to a text format that is easy for the user to understand.

[0049] Step 6:

[0050] The device displays the proposed fixes to the user. The display is presented in a visually easy-to-understand format, and additional explanations and implementation steps are shown if necessary.

[0051] Step 7:

[0052] The user adjusts the dish according to the displayed suggestions. For example, they can add curry powder, taste it again, and make further adjustments as needed to achieve the ideal flavor.

[0053] (Example 1)

[0054] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0055] For beginners, seasoning and cooking methods are often challenging aspects of home and personal cooking. In particular, when trying new recipes, seasoning mistakes are common, leading to wasted ingredients. Effective methods are needed to avoid such situations and reduce cooking failures.

[0056] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0057] In this invention, the server includes means for the user to input data related to cooking, means for transmitting that data to a central processing unit, and means for a generative artificial intelligence implemented in the central processing unit to analyze the input data and generate suggestions for correction. This allows the user to quickly receive appropriate advice regarding problems during cooking, and to effectively avoid cooking failures.

[0058] A "user" is an individual or entity that inputs cooking-related data and receives advice from the system before carrying out cooking.

[0059] "Cooking-related data" includes data such as the name of the dish, current cooking problems, and information about the seasonings and ingredients available.

[0060] A "central processing unit" is a server or computer system installed to receive and analyze data sent from users.

[0061] "Generative artificial intelligence" refers to an artificial intelligence model used to analyze cooking-related data and generate suggestions for improvements.

[0062] "Analysis" is the process of identifying problems and areas for improvement based on input data related to cooking, and proposing the optimal cooking method.

[0063] A "suggested fix" is specific advice or recommended action generated to solve a cooking problem the user is facing.

[0064] "Visual presentation" refers to displaying the generated correction suggestions on a display device in a way that is easy for the user to understand.

[0065] This invention is a system for users to solve cooking-related problems, and was developed in particular to correct mistakes in seasoning and cooking methods during cooking. The system consists of multiple components, including a terminal, a server, and a generative AI model.

[0066] The user uses a terminal to input information about the cooking process. Specifically, they input the name of the dish, the problem they are currently experiencing, and a list of seasonings they have on hand. For example, if a user is cooking pasta and finds the taste unsatisfactory, they might input information such as "pasta, average taste, garlic, Parmesan cheese, black pepper." This information is then sent from the terminal to the server.

[0067] The server uses a generative AI model to analyze the received information. This generative AI refers to a database of food characteristics and seasoning combinations, and devises the optimal seasoning method based on the input information. The generative AI model analyzes the input data and generates suggestions for modifications to solve the problem.

[0068] Finally, the seasoning methods and modifications suggested by the generating AI are sent to the terminal by the server. The terminal visually displays the suggestions to the user. For example, if the AI ​​determines that "adding a little garlic and sprinkling Parmesan cheese will enhance the flavor," that advice will be displayed to the user.

[0069] This allows users to effectively resolve cooking problems by following specific advice received from the system. The system aims to improve the user's cooking experience by increasing cooking efficiency and reducing failures.

[0070] Example of a prompt:

[0071] Dish name: Pasta

[0072] Current problem: The taste is unremarkable.

[0073] Ingredients I have: Garlic, Parmesan cheese, black pepper

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

[0075] Step 1:

[0076] The terminal receives cooking information from the user as input. The user enters the name of the dish, the current problem, and the seasonings they have into the terminal's input interface. This inputs specific cooking data into the terminal (e.g., "pasta, average taste, garlic, Parmesan cheese, black pepper").

[0077] Step 2:

[0078] The terminal formats the entered cooking information and prepares it for data transmission. The data is formatted in text format and sent to the server via the network. The terminal's output becomes the data to be sent to the server.

[0079] Step 3:

[0080] The server receives data from the terminal as input and activates the generative AI model. The generative AI model refers to a database of cooking characteristics and performs analysis based on the input cooking data. Specifically, it identifies problems and devises solutions. As a result of the analysis, proposed modifications are generated.

[0081] Step 4:

[0082] The server receives the suggested modifications generated by the generative AI model as output and sends that information to the terminal. The output from the server is specific advice for improving the dish (e.g., "Add a little garlic and sprinkle with Parmesan cheese"). The server converts this advice into a format that is easy for the user to understand.

[0083] Step 5:

[0084] The terminal receives suggested corrections from the server as input and presents them visually to the user. The user reviews the advice displayed on the terminal and adjusts the cooking process accordingly. Based on the information provided, the user can correct any seasoning mistakes by performing the necessary actions during the cooking process. The terminal's output is an advertisement for the user.

[0085] (Application Example 1)

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

[0087] When customers cook in a physical store, those with little cooking experience often make mistakes with seasoning. In such cases, there is a need to provide efficient and appropriate means to correct seasoning mistakes and guide the dish to its optimal state. Furthermore, when customers bring various ingredients and use limited seasonings, flexible and quick solutions are necessary.

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

[0089] In this invention, the server includes means for the user to input information about the dish, means for transmitting that information to the processing unit, and means for the processing unit to analyze the input information and generate suggested modifications. This makes it possible to suggest appropriate seasonings based on the ingredients and seasonings brought by the customer when they cook in a physical store, thereby improving the customer's cooking experience.

[0090] A "user" is an individual who uses the system to input information about cooking and receive seasoning suggestions.

[0091] "Means of inputting information" refers to an interface in which users input data such as the type of dish, problems, and available seasonings into the terminal.

[0092] "Means of transmitting to a processing unit" refers to communication methods that transmit information entered by the user to a server or AI platform in the cloud via a network.

[0093] A "processing device" is a computer equipped with a database and algorithms for analyzing input information received from a user and generating optimal correction suggestions.

[0094] "Methods for generating revised proposals" refers to a method that uses a generative AI model to create specific suggestions for improving the taste of a dish based on input information.

[0095] "Means of presenting to the user" refers to an interface that displays the proposed modifications generated by the processing unit on the user's terminal.

[0096] A "physical store" is a real place where customers actually visit and learn or practice cooking.

[0097] A "customer" is an individual who visits a physical store and receives support for a culinary experience.

[0098] "Means of supporting seasoning" refers to a system that provides optimal seasoning advice based on the seasonings and ingredients that customers can use when cooking.

[0099] This system utilizes the user's smart device application to support the in-store cooking experience. Users input details such as the name of the dish, any issues they are experiencing, and the types of seasonings they possess via their device. For example, this might include information like "chicken curry, bland flavor, tomato paste, coriander, garlic salt." This information is then transmitted from the device to a server in the cloud.

[0100] The server uses Google® Cloud AI and OpenAI® APIs to analyze this information and processes the input data using a specific generative AI model. The AI ​​model generates suggestions for optimal seasoning by referring to a database of dish characteristics and seasoning combinations. The generated suggestions are presented on the device in an easy-to-understand format, allowing the user to adjust the taste of the dish based on them.

[0101] For example, if a user is making chicken curry and notices that it's bland, they would enter the aforementioned prompt into the app. In response, the server would suggest, "Adding a little tomato paste and simmering for 10 minutes will enhance the flavor." Through this process, the user can gain clues to achieve the ideal taste in their dish.

[0102] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0103] Step 1:

[0104] The user enters the dish name, current problem, and available seasonings into the terminal. This information includes the specific dish name and problem. The input data is structured in a format such as "chicken curry, bland taste, tomato paste, coriander, garlic salt." The terminal correctly receives this information and packages it into a format that can be transmitted.

[0105] Step 2:

[0106] The terminal sends the processed user input information to a server in the cloud. The input data is transferred to the server via a secure channel using TCP / IP. The input content is received on the server side as structured data and prepared for analysis.

[0107] Step 3:

[0108] The server performs analysis based on the received information. This is where generative AI models incorporating Google Cloud AI and OpenAI APIs come into play. The server references information about dishes and seasonings in the database and generates suggested modifications for optimal seasoning based on the input data. This analysis is performed using database searches and algorithms to quickly respond to user inquiries.

[0109] Step 4:

[0110] The server sends the generated revised instructions back to the user's terminal. This output data includes easy-to-understand adjustments such as increasing or decreasing seasonings and adjusting cooking times. The output data is converted into a text format for display on the user's terminal.

[0111] Step 5:

[0112] The user cooks based on the suggested modifications displayed on the device. For example, following the suggestion, "Adding a little tomato paste and simmering for 10 minutes will enhance the flavor," the user adjusts the seasonings and performs additional steps to improve the dish's quality. This allows the user to bring a failed dish closer to the ideal taste.

[0113] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0114] This invention is a system that not only helps users recover from seasoning mistakes during cooking, but also provides a better user experience by taking into account the user's emotions. The system not only suggests the optimal cooking correction plan based on the user's input information and possessed seasoning data, but also uses an emotion engine to provide feedback that takes the user's psychological state into account.

[0115] The user first inputs the state of the dish, the current problem, and a list of available seasonings into the terminal. Meanwhile, the emotion engine receives data including the user's facial expressions, voice, and text input to determine the user's emotional state. This information is sent to the server, where the generative artificial intelligence and emotion engine on the server begin working together to analyze it.

[0116] The server first generates suggested modifications based on the input cooking information using artificial intelligence. Simultaneously, an emotion engine adjusts the suggestions to take into account the user's emotional state. For example, if the emotion engine detects that the user is feeling down, the feedback will include words of encouragement and be delivered in a gentle tone. Specifically, it might say something like, "Add a little salt and let it simmer slowly. It'll be good when the aroma starts to develop!"

[0117] The terminal displays suggestions and adjustment feedback sent from the server. Users can improve their cooking by making adjustments while receiving emotionally-responsive feedback along with the suggestions. This system aims to provide optimal support to users with different moods, and will be especially helpful for users who are unfamiliar with cooking.

[0118] The following describes the processing flow.

[0119] Step 1:

[0120] The user enters the name of the dish they are currently cooking, the seasoning problem they are facing, and a list of seasonings they possess into the device. In addition, emotional data is obtained by collecting facial expressions and voice through the device's camera or microphone.

[0121] Step 2:

[0122] The terminal sends the entered food information and emotion data to the server. Necessary security protocols are used during transmission to ensure the data is transferred securely.

[0123] Step 3:

[0124] The server receives cooking information from a generating artificial intelligence and analyzes the problem. The analysis takes into account the type of dish, the combination of seasonings, and the usual balance of flavors.

[0125] Step 4:

[0126] The emotion engine processes the received emotion data and determines the user's emotional state. For example, it identifies when the user is feeling stressed.

[0127] Step 5:

[0128] Based on the analysis of the dish, the server's AI generates suggested improvements. This process incorporates the analysis results of the emotion engine, providing feedback tailored to the user's emotional state. For example, it might offer feedback such as, "Adding a little salt would be good. Don't rush, just continue cooking and it will taste delicious!"

[0129] Step 6:

[0130] The server sends back the generated revisions and emotionally appropriate feedback to the terminal.

[0131] Step 7:

[0132] The device displays the received correction suggestions and feedback to the user. Based on the feedback, the user can make corrections to the cooking process and actually improve the taste of the dish.

[0133] (Example 2)

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

[0135] When cooking, users sometimes struggle to find appropriate solutions if they make a mistake with the seasoning. Furthermore, while a user's psychological state often influences the quality of the dish and their overall experience, conventional systems have struggled to provide support that takes user emotions into account.

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

[0137] In this invention, the server includes means for the user to input the state of the dish, problems, and seasoning information; means for collecting emotional data through the user's facial expressions, voice, and text input; and means for analyzing the input information using a generative AI model and an emotion engine to generate and adjust correction suggestions. This makes it possible to provide optimal cooking correction suggestions while taking the user's psychological state into consideration, and to enjoy an improved cooking experience.

[0138] A "user" refers to a person who uses the system to receive suggestions regarding the adjustment of their cooking.

[0139] "The state of the dish" refers to information indicating the progress and quality of the dish during cooking.

[0140] "Problems" refer to specific challenges or malfunctions that users encounter while cooking.

[0141] "Seasoning information" refers to data about the types and quantities of seasonings that the user possesses.

[0142] A "server" refers to a central computing resource that receives and processes user input information.

[0143] A "generative AI model" refers to an algorithm that generates optimal suggestions based on input data.

[0144] An "emotion engine" refers to a system that analyzes a user's emotional state and generates corresponding feedback.

[0145] "Proposed improvements" refers to information that specifically outlines suggestions and advice for addressing problems in the cooking process.

[0146] "Emotional data" refers to information about the user's psychological state, and includes data obtained from facial expressions, voice, and text input.

[0147] "Adjustment" refers to the process of incorporating sentiment data into the generated correction suggestions and presenting them to the user in a way that is individually tailored to their needs.

[0148] This invention is a system that assists users in improving their seasoning skills by providing optimal cooking correction suggestions and feedback when they make mistakes in seasoning during cooking. The system consists of a terminal and a server, and a key feature is that it generates feedback that takes the user's emotions into consideration.

[0149] First, the user uses a device to input information about the state of the dish, any current problems, and a list of seasonings they possess. At this stage, the device collects the user's facial expressions and voice through its camera and microphone, and also acquires emotional data through text input. Both the collected information and emotional data are then sent to the server.

[0150] On the server, a generative AI model and an emotion engine are used to analyze the provided data and generate the optimal cooking improvement suggestions for the user. The generative AI model has the ability to analyze the data entered using prompt sentences, producing accurate and effective suggestions. For example, if the user enters "It's bland," the prompt sentence used will be "The pasta you are currently cooking is bland. You have salt, pepper, soy sauce, and basil. Please generate the optimal cooking improvement suggestion." The suggestions obtained by this generative AI model are then adjusted by the emotion engine according to the user's emotional state. The emotion engine uses collected facial expressions, voice, and text information to determine the user's psychological state and adjusts the suggestions to include encouragement and kindness.

[0151] The revised suggestions, once adjusted, are presented to the user via the device. This allows users to receive not only suggestions to correct seasoning mistakes but also emotionally resonant feedback, resulting in a more satisfying cooking experience.

[0152] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0153] Step 1:

[0154] The user enters the dish's condition, any problems, and a list of seasonings into the terminal. This information is then entered into the system. Specifically, the user enters information using text fields on the interface and saves the data to the terminal by pressing the submit button.

[0155] Step 2:

[0156] The device collects the user's facial expressions and voice using a camera and microphone to acquire emotional data. The input here is camera images and audio data, which are used for emotion analysis. Specifically, the camera recognizes the user's face and records changes in facial expressions as digital data, the microphone captures the voice, and text-to-speech recognition software analyzes the emotions. The analysis result outputs the current emotional state.

[0157] Step 3:

[0158] The collected cooking information and emotional data are sent from the terminal to the server. This transmission takes place via an internet connection, and the data is received on the server side. The input data includes the state and problems of the dish, a list of seasonings, and the emotional state.

[0159] Step 4:

[0160] The server generates correction suggestions using a generative AI model based on the received data. In this process, the server identifies the problem with the cooking from the input information and creates a prompt message to input into the AI ​​model. Specifically, it uses a prompt such as, "The dish currently being cooked is bland. The available seasonings are salt, pepper, soy sauce, and basil. Please generate the best cooking improvement plan," and the AI ​​model outputs the optimal improvement solution.

[0161] Step 5:

[0162] The server's emotion engine analyzes the user's emotion data and adjusts the generated correction suggestions according to the user's emotional state. If the server determines that the user is "depressed," it adds words of encouragement or kindness to the suggestions. The output of this process is an adjusted correction suggestion.

[0163] Step 6:

[0164] The server-adjusted correction suggestions are sent to the terminal and presented to the user. The terminal outputs the data received from the server to its display interface, presenting the advice in a way that the user can easily understand.

[0165] Step 7:

[0166] The user modifies the dish based on the suggestions displayed on the device. For example, if the user receives the suggestion to "add a little salt and simmer it slowly," they actually add the seasonings in the kitchen and monitor the process. The output of this step is the modified dish.

[0167] (Application Example 2)

[0168] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0169] It is necessary to not only prevent seasoning mistakes during cooking, but also to improve satisfaction and efficiency in the cooking process by providing appropriate feedback that takes into account the cook's emotional state. However, existing cooking support systems do not take into account the psychological factors of the cook, and therefore have the problem of not being able to provide appropriate support, especially for those who are inexperienced in cooking.

[0170] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0171] In this invention, the server includes means for the user to input information about the dish, means for a generating artificial intelligence to analyze the input information and generate correction suggestions, means for recognizing the user's emotional state, and means for adjusting the correction suggestions based on that emotional state. This makes it possible to solve the cooking problem of seasoning while providing encouragement and suggestions that are tailored to the user's psychological state.

[0172] A "user" is an entity that uses this system to input information about cooking and receive feedback.

[0173] "Information about cooking" refers to data about the state and problems of the food being cooked, as well as information about the seasonings being used.

[0174] "Generative artificial intelligence" is artificial intelligence that has the function of analyzing input information and generating correction suggestions.

[0175] A "correction suggestion" is a suggestion for improving a dish, provided by artificial intelligence generated based on the state of the dish.

[0176] "Emotional state" refers to the user's psychological or emotional state, which is recognized from facial expressions, voice, text, etc.

[0177] "Adjustment feedback" refers to feedback provided after adjusting suggested modifications based on the user's emotional state.

[0178] A "server" is a device or system that receives input from a user, processes the information using generative artificial intelligence and emotion recognition capabilities, and provides suggestions and feedback.

[0179] In the system for realizing this invention, the user first inputs information about the dish using a terminal. This includes the state of the dish being cooked, any current problems, and a list of seasonings the user possesses. For sentiment analysis, the user's facial expressions, voice, and text are also collected from the terminal. The terminal then transmits this input information to a server.

[0180] The server uses Python®-based generative artificial intelligence and emotion recognition libraries (e.g., Microsoft® Azure® Emotion API) to analyze the transmitted information. The generative AI generates correction suggestions based on the state of the dish, and adjusts the suggestions by determining the user's emotional state through emotion recognition. Specifically, if the user is feeling stressed, the suggestions will use gentler language. For example, even if the user has burned the dish, it will naturally offer support such as, "Let's take a little time and readjust the ingredients."

[0181] Suggested revisions and feedback after adjustments are displayed on the device, allowing users to proceed with cooking accordingly. This enables improvement of cooking skills and provides a sense of psychological reassurance.

[0182] For example, if a user inputs "The soup is too salty," the generative AI model will suggest "add water to dilute it." If the emotion recognition determines that the user is "feeling down," it will provide adjustment feedback such as, "It's okay, just add a little more water and it'll be perfect."

[0183] Examples of prompts to input into a generative AI model are as follows:

[0184] "Current dish status: too salty. User emotion: upset. Suggest an adjustment and comforting feedback."

[0185] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0186] Step 1:

[0187] Users input information about the state of their dishes, any problems, and the seasonings they possess via a terminal. Users also provide their facial expressions and voice as input data to the terminal. This information is used to improve the dishes.

[0188] Step 2:

[0189] The terminal sends data entered by the user to the server. The input data includes information about the dish and voice and facial expression data indicating the user's emotions, which are sent to the server as prompt messages.

[0190] Step 3:

[0191] The server uses generative artificial intelligence to analyze the cooking information and generate optimal modification suggestions. This analysis involves inputting the cooking information into the generative AI model and generating suggestions. The output is the suggested modifications.

[0192] Step 4:

[0193] The server uses an emotion recognition library to determine the user's emotional state from their input. Voice or facial expression data is used as input, and the result of the emotion analysis is output as an emotional state, such as "feeling stressed" or "feeling depressed."

[0194] Step 5:

[0195] The server adjusts the generated revision suggestions based on the emotional state obtained. Specifically, it processes the wording and expression of the suggestions to change according to the emotion. Here, the suggestions are adjusted to use gentler language and then output.

[0196] Step 6:

[0197] The server sends the adjusted correction suggestions and feedback to the terminal. The user receives the feedback displayed on the terminal and can proceed with the cooking process based on the suggestions. Finally, output is provided with the aim of improving the user's cooking.

[0198] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0199] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0200] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0201] [Second Embodiment]

[0202] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0203] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0204] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0205] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0206] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0207] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0208] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0209] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0210] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0211] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0212] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0213] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0214] This invention is a system designed to rectify situations where a user has failed to season a dish properly while cooking. Specifically, it uses generative artificial intelligence to suggest the optimal seasoning method. The invention consists of a terminal used by the user, an artificial intelligence program implemented on a server, and data communication means.

[0215] The user enters the name of the dish, the current problem, and a list of seasonings they have on hand via the terminal. For example, if a user notices that beef stew is bland while making it, they would enter information such as "beef stew, bland, salt, sugar, olive oil."

[0216] This information is sent from the terminal to the server. The generative artificial intelligence on the server analyzes the input information and determines what modifications would be effective in improving the taste of the dish. Here, the generative AI refers to a database of dish characteristics and seasoning combinations to generate the optimal suggestion for solving the problem.

[0217] For example, if the AI ​​determines that "adding a little salt and increasing the simmering time by another 5 minutes would enhance the flavor," specific advice is sent from the server to the terminal. This advice is displayed in an easy-to-understand text format, allowing the user to adjust the flavor of the dish accordingly.

[0218] In this way, users can avoid wasting failed dishes and ultimately provide delicious meals. This system aims to solve common seasoning mistakes that occur in home cooking and provide the enjoyment of cooking, and is especially useful for users with little cooking experience.

[0219] The following describes the processing flow.

[0220] Step 1:

[0221] The user uses a terminal to enter the name of the dish, the current seasoning issue, and the condiments they have. For example, they might enter "Curry, Not spicy enough, Salt, Curry powder, Tomato paste."

[0222] Step 2:

[0223] The terminal verifies the entered information and confirms that all necessary information is present. If any information is missing, it prompts the user for additional input. After verification, it sends the information to the server.

[0224] Step 3:

[0225] The server passes the received information to the generating AI module for analysis. The generating AI then starts analyzing the database to provide solutions based on the characteristics of the dishes and the combinations of seasonings.

[0226] Step 4:

[0227] The generating AI uses the analysis results to create optimal solutions to the input problem. For example, it might create specific suggestions such as, "To increase the spiciness, add 1 teaspoon of curry powder and a small amount of tomato paste."

[0228] Step 5:

[0229] The server sends the generated revisions to the terminal. The data sent is converted to a text format that is easy for the user to understand.

[0230] Step 6:

[0231] The device displays the proposed fixes to the user. The display is presented in a visually easy-to-understand format, and additional explanations and implementation steps are shown if necessary.

[0232] Step 7:

[0233] The user adjusts the dish according to the displayed suggestions. For example, they can add curry powder, taste it again, and make further adjustments as needed to achieve the ideal flavor.

[0234] (Example 1)

[0235] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0236] For beginners, seasoning and cooking methods are often challenging aspects of home and personal cooking. In particular, when trying new recipes, seasoning mistakes are common, leading to wasted ingredients. Effective methods are needed to avoid such situations and reduce cooking failures.

[0237] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0238] In this invention, the server includes means for the user to input data related to cooking, means for transmitting that data to a central processing unit, and means for a generative artificial intelligence implemented in the central processing unit to analyze the input data and generate suggestions for correction. This allows the user to quickly receive appropriate advice regarding problems during cooking, and to effectively avoid cooking failures.

[0239] A "user" is an individual or entity that inputs cooking-related data and receives advice from the system before carrying out cooking.

[0240] "Cooking-related data" includes data such as the name of the dish, current cooking problems, and information about the seasonings and ingredients available.

[0241] A "central processing unit" is a server or computer system installed to receive and analyze data sent from users.

[0242] "Generative artificial intelligence" refers to an artificial intelligence model used to analyze cooking-related data and generate suggestions for improvements.

[0243] "Analysis" is the process of identifying problems and areas for improvement based on input data related to cooking, and proposing the optimal cooking method.

[0244] A "suggested fix" is specific advice or recommended action generated to solve a cooking problem the user is facing.

[0245] "Visual presentation" refers to displaying the generated correction suggestions on a display device in a way that is easy for the user to understand.

[0246] This invention is a system for users to solve cooking-related problems, and was developed in particular to correct mistakes in seasoning and cooking methods during cooking. The system consists of multiple components, including a terminal, a server, and a generative AI model.

[0247] The user uses a terminal to input information about the cooking process. Specifically, they input the name of the dish, the problem they are currently experiencing, and a list of seasonings they have on hand. For example, if a user is cooking pasta and finds the taste unsatisfactory, they might input information such as "pasta, average taste, garlic, Parmesan cheese, black pepper." This information is then sent from the terminal to the server.

[0248] The server uses a generative AI model to analyze the received information. This generative AI refers to a database of food characteristics and seasoning combinations, and devises the optimal seasoning method based on the input information. The generative AI model analyzes the input data and generates suggestions for modifications to solve the problem.

[0249] Finally, the seasoning methods and modifications suggested by the generating AI are sent to the terminal by the server. The terminal visually displays the suggestions to the user. For example, if the AI ​​determines that "adding a little garlic and sprinkling Parmesan cheese will enhance the flavor," that advice will be displayed to the user.

[0250] This allows users to effectively resolve cooking problems by following specific advice received from the system. The system aims to improve the user's cooking experience by increasing cooking efficiency and reducing failures.

[0251] Example of a prompt:

[0252] Dish name: Pasta

[0253] Current problem: The taste is unremarkable.

[0254] Ingredients I have: Garlic, Parmesan cheese, black pepper

[0255] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0256] Step 1:

[0257] The terminal receives cooking information from the user as input. The user enters the name of the dish, the current problem, and the seasonings they have into the terminal's input interface. This inputs specific cooking data into the terminal (e.g., "pasta, average taste, garlic, Parmesan cheese, black pepper").

[0258] Step 2:

[0259] The terminal formats the entered cooking information and prepares it for data transmission. The data is formatted in text format and sent to the server via the network. The terminal's output becomes the data to be sent to the server.

[0260] Step 3:

[0261] The server receives data from the terminal as input and activates the generative AI model. The generative AI model refers to a database of cooking characteristics and performs analysis based on the input cooking data. Specifically, it identifies problems and devises solutions. As a result of the analysis, proposed modifications are generated.

[0262] Step 4:

[0263] The server receives the suggested modifications generated by the generative AI model as output and sends that information to the terminal. The output from the server is specific advice for improving the dish (e.g., "Add a little garlic and sprinkle with Parmesan cheese"). The server converts this advice into a format that is easy for the user to understand.

[0264] Step 5:

[0265] The terminal receives suggested corrections from the server as input and presents them visually to the user. The user reviews the advice displayed on the terminal and adjusts the cooking process accordingly. Based on the information provided, the user can correct any seasoning mistakes by performing the necessary actions during the cooking process. The terminal's output is an advertisement for the user.

[0266] (Application Example 1)

[0267] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0268] When customers cook in a physical store, those with little cooking experience often make mistakes with seasoning. In such cases, there is a need to provide efficient and appropriate means to correct seasoning mistakes and guide the dish to its optimal state. Furthermore, when customers bring various ingredients and use limited seasonings, flexible and quick solutions are necessary.

[0269] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0270] In this invention, the server includes means for the user to input information about the dish, means for transmitting that information to the processing unit, and means for the processing unit to analyze the input information and generate suggested modifications. This makes it possible to suggest appropriate seasonings based on the ingredients and seasonings brought by the customer when they cook in a physical store, thereby improving the customer's cooking experience.

[0271] A "user" is an individual who uses the system to input information about cooking and receive seasoning suggestions.

[0272] "Means of inputting information" refers to an interface in which users input data such as the type of dish, problems, and available seasonings into the terminal.

[0273] "Means of transmitting to a processing unit" refers to communication methods that transmit information entered by the user to a server or AI platform in the cloud via a network.

[0274] A "processing device" is a computer equipped with a database and algorithms for analyzing input information received from a user and generating optimal correction suggestions.

[0275] "Methods for generating revised proposals" refers to a method that uses a generative AI model to create specific suggestions for improving the taste of a dish based on input information.

[0276] "Means of presenting to the user" refers to an interface that displays the proposed modifications generated by the processing unit on the user's terminal.

[0277] A "physical store" is a real place where customers actually visit and learn or practice cooking.

[0278] A "customer" is an individual who visits a physical store and receives support for a culinary experience.

[0279] The "means to support seasoning" is a mechanism that provides optimal seasoning advice based on seasonings and ingredients that customers can use when cooking.

[0280] This system utilizes the user's smart device application to support the cooking experience in physical stores. The user inputs details such as the name of the dish, the problems they have, and the seasonings they possess through the terminal. For example, information such as "Chicken Curry, the taste is bland, tomato paste, coriander, garlic salt" is included. This information is sent from the terminal to the server on the cloud.

[0281] The server uses APIs such as Google Cloud AI and OpenAI to analyze this information and processes the input data using a specific generative AI model. The AI model generates proposed modifications for optimal seasoning while referring to a database regarding the characteristics of the dish and the combinations of seasonings. The generated proposals are presented to the terminal in an easy-to-understand form, and the user can adjust the taste of the dish based on them.

[0282] As a specific example, when the user notices that the taste is weak while making "Chicken Curry", they input the above prompt text into the app. In response to this input, the server proposes "Adding a little tomato paste and simmering for 10 minutes will enhance the flavor". Through this process, the user can obtain clues to finish the dish with an ideal taste.

[0283] The flow of the specific process in Application Example will be described using Figure 12.

[0284] Step 1:

[0285] The user inputs the dish name, current problem, and seasonings they have on the terminal. This information includes the specific dish name and problems. The input data is structured in a format such as "Chicken Curry, the taste is bland, tomato paste, coriander, garlic salt". The terminal correctly receives this and packages it into a transmissible format.

[0286] Step 2:

[0287] The terminal sends the processed user input information to the server on the cloud. The input data is transferred to the server through a secure channel via TCP / IP. The input content is received as structured data on the server side and prepared for analysis.

[0288] Step 3:

[0289] The server conducts analysis based on the received information. Here, a generative AI model incorporating Google Cloud AI or OpenAI API functions. The server refers to the information on dishes and seasonings in the database and generates amendments for optimal seasoning based on the input data. This analysis is carried out by making full use of database searches and algorithms to quickly respond to the user's questions.

[0290] Step 4:

[0291] The server returns the generated amendments to the user terminal. This output data includes things like the increase or decrease of seasonings and the adjustment of cooking time that are easy for the user to understand. The output data is converted into a form for display on the user's terminal in text format.

[0292] Step 5:

[0293] The user cooks based on the suggested modifications displayed on the device. For example, following the suggestion, "Adding a little tomato paste and simmering for 10 minutes will enhance the flavor," the user adjusts the seasonings and performs additional steps to improve the dish's quality. This allows the user to bring a failed dish closer to the ideal taste.

[0294] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0295] This invention is a system that not only helps users recover from seasoning mistakes during cooking, but also provides a better user experience by taking into account the user's emotions. The system not only suggests the optimal cooking correction plan based on the user's input information and possessed seasoning data, but also uses an emotion engine to provide feedback that takes the user's psychological state into account.

[0296] The user first inputs the state of the dish, the current problem, and a list of available seasonings into the terminal. Meanwhile, the emotion engine receives data including the user's facial expressions, voice, and text input to determine the user's emotional state. This information is sent to the server, where the generative artificial intelligence and emotion engine on the server begin working together to analyze it.

[0297] The server first generates suggested modifications based on the input cooking information using artificial intelligence. Simultaneously, an emotion engine adjusts the suggestions to take into account the user's emotional state. For example, if the emotion engine detects that the user is feeling down, the feedback will include words of encouragement and be delivered in a gentle tone. Specifically, it might say something like, "Add a little salt and let it simmer slowly. It'll be good when the aroma starts to develop!"

[0298] The terminal displays the proposals and adjustment feedback sent from the server. The user can improve the dish by making adjustments while receiving feedback according to their emotions along with the proposals. This system aims to provide optimal support for users in different moods and is particularly helpful for users who are not familiar with cooking.

[0299] The processing flow will be described below.

[0300] Step 1:

[0301] The user inputs the name of the dish currently being cooked, the seasoning problems faced, and the list of seasonings they have on the terminal. Additionally, emotion data is obtained by collecting expressions and voices through the terminal's camera or microphone.

[0302] Step 2:

[0303] The terminal sends the input dish information and emotion data to the server. The necessary security protocol is used for transmission to ensure the safe transfer of data.

[0304] Step 3:

[0305] The server's generative artificial intelligence receives the dish information and analyzes the problems. The analysis takes into account the type of dish, the combination of seasonings, and the normal seasoning balance.

[0306] Step 4:

[0307] The emotion engine processes the received emotion data and determines what emotional state the user is in. For example, it identifies cases where the user is feeling stressed.

[0308] Step 5:

[0309] Based on the analysis of the dish, the server's AI generates suggested improvements. This process incorporates the analysis results of the emotion engine, providing feedback tailored to the user's emotional state. For example, it might offer feedback such as, "Adding a little salt would be good. Don't rush, just continue cooking and it will taste delicious!"

[0310] Step 6:

[0311] The server sends back the generated revisions and emotionally appropriate feedback to the terminal.

[0312] Step 7:

[0313] The device displays the received correction suggestions and feedback to the user. Based on the feedback, the user can make corrections to the cooking process and actually improve the taste of the dish.

[0314] (Example 2)

[0315] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0316] When cooking, users sometimes struggle to find appropriate solutions if they make a mistake with the seasoning. Furthermore, while a user's psychological state often influences the quality of the dish and their overall experience, conventional systems have struggled to provide support that takes user emotions into account.

[0317] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0318] In this invention, the server includes means for the user to input the state of the dish, problems, and seasoning information; means for collecting emotional data through the user's facial expressions, voice, and text input; and means for analyzing the input information using a generative AI model and an emotion engine to generate and adjust correction suggestions. This makes it possible to provide optimal cooking correction suggestions while taking the user's psychological state into consideration, and to enjoy an improved cooking experience.

[0319] A "user" refers to a person who uses the system to receive suggestions regarding the adjustment of their cooking.

[0320] "The state of the dish" refers to information indicating the progress and quality of the dish during cooking.

[0321] "Problems" refer to specific challenges or malfunctions that users encounter while cooking.

[0322] "Seasoning information" refers to data about the types and quantities of seasonings that the user possesses.

[0323] A "server" refers to a central computing resource that receives and processes user input information.

[0324] A "generative AI model" refers to an algorithm that generates optimal suggestions based on input data.

[0325] An "emotion engine" refers to a system that analyzes a user's emotional state and generates corresponding feedback.

[0326] "Proposed improvements" refers to information that specifically outlines suggestions and advice for addressing problems in the cooking process.

[0327] "Emotional data" refers to information about the user's psychological state, and includes data obtained from facial expressions, voice, and text input.

[0328] "Adjustment" refers to the process of incorporating sentiment data into the generated correction suggestions and presenting them to the user in a way that is individually tailored to their needs.

[0329] This invention is a system that assists users in improving their seasoning skills by providing optimal cooking correction suggestions and feedback when they make mistakes in seasoning during cooking. The system consists of a terminal and a server, and a key feature is that it generates feedback that takes the user's emotions into consideration.

[0330] First, the user uses a device to input information about the state of the dish, any current problems, and a list of seasonings they possess. At this stage, the device collects the user's facial expressions and voice through its camera and microphone, and also acquires emotional data through text input. Both the collected information and emotional data are then sent to the server.

[0331] On the server, a generative AI model and an emotion engine are used to analyze the provided data and generate the optimal cooking improvement suggestions for the user. The generative AI model has the ability to analyze the data entered using prompt sentences, producing accurate and effective suggestions. For example, if the user enters "It's bland," the prompt sentence used will be "The pasta you are currently cooking is bland. You have salt, pepper, soy sauce, and basil. Please generate the optimal cooking improvement suggestion." The suggestions obtained by this generative AI model are then adjusted by the emotion engine according to the user's emotional state. The emotion engine uses collected facial expressions, voice, and text information to determine the user's psychological state and adjusts the suggestions to include encouragement and kindness.

[0332] The revised suggestions, once adjusted, are presented to the user via the device. This allows users to receive not only suggestions to correct seasoning mistakes but also emotionally resonant feedback, resulting in a more satisfying cooking experience.

[0333] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0334] Step 1:

[0335] The user enters the dish's condition, any problems, and a list of seasonings into the terminal. This information is then entered into the system. Specifically, the user enters information using text fields on the interface and saves the data to the terminal by pressing the submit button.

[0336] Step 2:

[0337] The device collects the user's facial expressions and voice using a camera and microphone to acquire emotional data. The input here is camera images and audio data, which are used for emotion analysis. Specifically, the camera recognizes the user's face and records changes in facial expressions as digital data, the microphone captures the voice, and text-to-speech recognition software analyzes the emotions. The analysis result outputs the current emotional state.

[0338] Step 3:

[0339] The collected cooking information and emotional data are sent from the terminal to the server. This transmission takes place via an internet connection, and the data is received on the server side. The input data includes the state and problems of the dish, a list of seasonings, and the emotional state.

[0340] Step 4:

[0341] The server generates correction suggestions using a generative AI model based on the received data. In this process, the server identifies the problem with the cooking from the input information and creates a prompt message to input into the AI ​​model. Specifically, it uses a prompt such as, "The dish currently being cooked is bland. The available seasonings are salt, pepper, soy sauce, and basil. Please generate the best cooking improvement plan," and the AI ​​model outputs the optimal improvement solution.

[0342] Step 5:

[0343] The server's emotion engine analyzes the user's emotion data and adjusts the generated correction suggestions according to the user's emotional state. If the server determines that the user is "depressed," it adds words of encouragement or kindness to the suggestions. The output of this process is an adjusted correction suggestion.

[0344] Step 6:

[0345] The server-adjusted correction suggestions are sent to the terminal and presented to the user. The terminal outputs the data received from the server to its display interface, presenting the advice in a way that the user can easily understand.

[0346] Step 7:

[0347] The user modifies the dish based on the suggestions displayed on the device. For example, if the user receives the suggestion to "add a little salt and simmer it slowly," they actually add the seasonings in the kitchen and monitor the process. The output of this step is the modified dish.

[0348] (Application Example 2)

[0349] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0350] It is necessary to not only prevent seasoning mistakes during cooking, but also to improve satisfaction and efficiency in the cooking process by providing appropriate feedback that takes into account the cook's emotional state. However, existing cooking support systems do not take into account the psychological factors of the cook, and therefore have the problem of not being able to provide appropriate support, especially for those who are inexperienced in cooking.

[0351] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0352] In this invention, the server includes means for the user to input information about the dish, means for a generating artificial intelligence to analyze the input information and generate correction suggestions, means for recognizing the user's emotional state, and means for adjusting the correction suggestions based on that emotional state. This makes it possible to solve the cooking problem of seasoning while providing encouragement and suggestions that are tailored to the user's psychological state.

[0353] A "user" is an entity that uses this system to input information about cooking and receive feedback.

[0354] "Information about cooking" refers to data about the state and problems of the food being cooked, as well as information about the seasonings being used.

[0355] "Generative artificial intelligence" is artificial intelligence that has the function of analyzing input information and generating correction suggestions.

[0356] A "correction suggestion" is a suggestion for improving a dish, provided by artificial intelligence generated based on the state of the dish.

[0357] "Emotional state" refers to the user's psychological or emotional state, which is recognized from facial expressions, voice, text, etc.

[0358] "Adjustment feedback" refers to feedback provided after adjusting suggested modifications based on the user's emotional state.

[0359] A "server" is a device or system that receives input from a user, processes the information using generative artificial intelligence and emotion recognition capabilities, and provides suggestions and feedback.

[0360] In the system for realizing this invention, the user first inputs information about the dish using a terminal. This includes the state of the dish being cooked, any current problems, and a list of seasonings the user possesses. For sentiment analysis, the user's facial expressions, voice, and text are also collected from the terminal. The terminal then transmits this input information to a server.

[0361] The server uses Python-based generative artificial intelligence and emotion recognition libraries (e.g., Microsoft Azure Emotion API) to analyze the transmitted information. The generative AI generates correction suggestions based on the state of the dish, and emotion recognition adjusts the suggestions by determining the user's emotional state. Specifically, if the user is feeling stressed, the suggestions will use gentler language. For example, even if the user has burned the dish, the system will naturally offer support such as, "Let's take a little time and readjust the ingredients."

[0362] Suggested revisions and feedback after adjustments are displayed on the device, allowing users to proceed with cooking accordingly. This enables improvement of cooking skills and provides a sense of psychological reassurance.

[0363] For example, if a user inputs "The soup is too salty," the generative AI model will suggest "add water to dilute it." If the emotion recognition determines that the user is "feeling down," it will provide adjustment feedback such as, "It's okay, just add a little more water and it'll be perfect."

[0364] Examples of prompts to input into a generative AI model are as follows:

[0365] "Current dish status: too salty. User emotion: upset. Suggest an adjustment and comforting feedback."

[0366] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0367] Step 1:

[0368] Users input information about the state of their dishes, any problems, and the seasonings they possess via a terminal. Users also provide their facial expressions and voice as input data to the terminal. This information is used to improve the dishes.

[0369] Step 2:

[0370] The terminal sends data entered by the user to the server. The input data includes information about the dish and voice and facial expression data indicating the user's emotions, which are sent to the server as prompt messages.

[0371] Step 3:

[0372] The server uses generative artificial intelligence to analyze the cooking information and generate optimal modification suggestions. This analysis involves inputting the cooking information into the generative AI model and generating suggestions. The output is the suggested modifications.

[0373] Step 4:

[0374] The server uses an emotion recognition library to determine the user's emotional state from their input. Voice or facial expression data is used as input, and the result of the emotion analysis is output as an emotional state, such as "feeling stressed" or "feeling depressed."

[0375] Step 5:

[0376] The server adjusts the generated revision suggestions based on the emotional state obtained. Specifically, it processes the wording and expression of the suggestions to change according to the emotion. Here, the suggestions are adjusted to use gentler language and then output.

[0377] Step 6:

[0378] The server sends the adjusted correction suggestions and feedback to the terminal. The user receives the feedback displayed on the terminal and can proceed with the cooking process based on the suggestions. Finally, output is provided with the aim of improving the user's cooking.

[0379] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0380] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0381] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0382] [Third Embodiment]

[0383] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0384] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0385] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0386] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0387] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0388] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0389] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0390] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0391] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0392] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0393] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0394] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0395] This invention is a system designed to rectify situations where a user has failed to season a dish properly while cooking. Specifically, it uses generative artificial intelligence to suggest the optimal seasoning method. The invention consists of a terminal used by the user, an artificial intelligence program implemented on a server, and data communication means.

[0396] The user enters the name of the dish, the current problem, and a list of seasonings they have on hand via the terminal. For example, if a user notices that beef stew is bland while making it, they would enter information such as "beef stew, bland, salt, sugar, olive oil."

[0397] This information is sent from the terminal to the server. The generative artificial intelligence on the server analyzes the input information and determines what modifications would be effective in improving the taste of the dish. Here, the generative AI refers to a database of dish characteristics and seasoning combinations to generate the optimal suggestion for solving the problem.

[0398] For example, if the AI ​​determines that "adding a little salt and increasing the simmering time by another 5 minutes would enhance the flavor," specific advice is sent from the server to the terminal. This advice is displayed in an easy-to-understand text format, allowing the user to adjust the flavor of the dish accordingly.

[0399] In this way, users can avoid wasting failed dishes and ultimately provide delicious meals. This system aims to solve common seasoning mistakes that occur in home cooking and provide the enjoyment of cooking, and is especially useful for users with little cooking experience.

[0400] The following describes the processing flow.

[0401] Step 1:

[0402] The user uses a terminal to enter the name of the dish, the current seasoning issue, and the condiments they have. For example, they might enter "Curry, Not spicy enough, Salt, Curry powder, Tomato paste."

[0403] Step 2:

[0404] The terminal verifies the entered information and confirms that all necessary information is present. If any information is missing, it prompts the user for additional input. After verification, it sends the information to the server.

[0405] Step 3:

[0406] The server passes the received information to the generating AI module for analysis. The generating AI then starts analyzing the database to provide solutions based on the characteristics of the dishes and the combinations of seasonings.

[0407] Step 4:

[0408] The generating AI uses the analysis results to create optimal solutions to the input problem. For example, it might create specific suggestions such as, "To increase the spiciness, add 1 teaspoon of curry powder and a small amount of tomato paste."

[0409] Step 5:

[0410] The server sends the generated revisions to the terminal. The data sent is converted to a text format that is easy for the user to understand.

[0411] Step 6:

[0412] The device displays the proposed fixes to the user. The display is presented in a visually easy-to-understand format, and additional explanations and implementation steps are shown if necessary.

[0413] Step 7:

[0414] The user adjusts the dish according to the displayed suggestions. For example, they can add curry powder, taste it again, and make further adjustments as needed to achieve the ideal flavor.

[0415] (Example 1)

[0416] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0417] For beginners, seasoning and cooking methods are often challenging aspects of home and personal cooking. In particular, when trying new recipes, seasoning mistakes are common, leading to wasted ingredients. Effective methods are needed to avoid such situations and reduce cooking failures.

[0418] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0419] In this invention, the server includes means for the user to input data related to cooking, means for transmitting that data to a central processing unit, and means for a generative artificial intelligence implemented in the central processing unit to analyze the input data and generate suggestions for correction. This allows the user to quickly receive appropriate advice regarding problems during cooking, and to effectively avoid cooking failures.

[0420] A "user" is an individual or entity that inputs cooking-related data and receives advice from the system before carrying out cooking.

[0421] "Cooking-related data" includes data such as the name of the dish, current cooking problems, and information about the seasonings and ingredients available.

[0422] A "central processing unit" is a server or computer system installed to receive and analyze data sent from users.

[0423] "Generative artificial intelligence" refers to an artificial intelligence model used to analyze cooking-related data and generate suggestions for improvements.

[0424] "Analysis" is the process of identifying problems and areas for improvement based on input data related to cooking, and proposing the optimal cooking method.

[0425] A "suggested fix" is specific advice or recommended action generated to solve a cooking problem the user is facing.

[0426] "Visual presentation" refers to displaying the generated correction suggestions on a display device in a way that is easy for the user to understand.

[0427] This invention is a system for users to solve cooking-related problems, and was developed in particular to correct mistakes in seasoning and cooking methods during cooking. The system consists of multiple components, including a terminal, a server, and a generative AI model.

[0428] The user uses a terminal to input information about the cooking process. Specifically, they input the name of the dish, the problem they are currently experiencing, and a list of seasonings they have on hand. For example, if a user is cooking pasta and finds the taste unsatisfactory, they might input information such as "pasta, average taste, garlic, Parmesan cheese, black pepper." This information is then sent from the terminal to the server.

[0429] The server uses a generative AI model to analyze the received information. This generative AI refers to a database of food characteristics and seasoning combinations, and devises the optimal seasoning method based on the input information. The generative AI model analyzes the input data and generates suggestions for modifications to solve the problem.

[0430] Finally, the seasoning methods and modifications suggested by the generating AI are sent to the terminal by the server. The terminal visually displays the suggestions to the user. For example, if the AI ​​determines that "adding a little garlic and sprinkling Parmesan cheese will enhance the flavor," that advice will be displayed to the user.

[0431] This allows users to effectively resolve cooking problems by following specific advice received from the system. The system aims to improve the user's cooking experience by increasing cooking efficiency and reducing failures.

[0432] Example of a prompt:

[0433] Dish name: Pasta

[0434] Current problem: The taste is unremarkable.

[0435] Ingredients I have: Garlic, Parmesan cheese, black pepper

[0436] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0437] Step 1:

[0438] The terminal receives cooking information from the user as input. The user enters the name of the dish, the current problem, and the seasonings they have into the terminal's input interface. This inputs specific cooking data into the terminal (e.g., "pasta, average taste, garlic, Parmesan cheese, black pepper").

[0439] Step 2:

[0440] The terminal formats the entered cooking information and prepares it for data transmission. The data is formatted in text format and sent to the server via the network. The terminal's output becomes the data to be sent to the server.

[0441] Step 3:

[0442] The server receives data from the terminal as input and activates the generative AI model. The generative AI model refers to a database of cooking characteristics and performs analysis based on the input cooking data. Specifically, it identifies problems and devises solutions. As a result of the analysis, proposed modifications are generated.

[0443] Step 4:

[0444] The server receives the suggested modifications generated by the generative AI model as output and sends that information to the terminal. The output from the server is specific advice for improving the dish (e.g., "Add a little garlic and sprinkle with Parmesan cheese"). The server converts this advice into a format that is easy for the user to understand.

[0445] Step 5:

[0446] The terminal receives suggested corrections from the server as input and presents them visually to the user. The user reviews the advice displayed on the terminal and adjusts the cooking process accordingly. Based on the information provided, the user can correct any seasoning mistakes by performing the necessary actions during the cooking process. The terminal's output is an advertisement for the user.

[0447] (Application Example 1)

[0448] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0449] When customers cook in a physical store, those with little cooking experience often make mistakes with seasoning. In such cases, there is a need to provide efficient and appropriate means to correct seasoning mistakes and guide the dish to its optimal state. Furthermore, when customers bring various ingredients and use limited seasonings, flexible and quick solutions are necessary.

[0450] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0451] In this invention, the server includes means for the user to input information about the dish, means for transmitting that information to the processing unit, and means for the processing unit to analyze the input information and generate suggested modifications. This makes it possible to suggest appropriate seasonings based on the ingredients and seasonings brought by the customer when they cook in a physical store, thereby improving the customer's cooking experience.

[0452] A "user" is an individual who uses the system to input information about cooking and receive seasoning suggestions.

[0453] "Means of inputting information" refers to an interface in which users input data such as the type of dish, problems, and available seasonings into the terminal.

[0454] "Means of transmitting to a processing unit" refers to communication methods that transmit information entered by the user to a server or AI platform in the cloud via a network.

[0455] A "processing device" is a computer equipped with a database and algorithms for analyzing input information received from a user and generating optimal correction suggestions.

[0456] "Methods for generating revised proposals" refers to a method that uses a generative AI model to create specific suggestions for improving the taste of a dish based on input information.

[0457] "Means of presenting to the user" refers to an interface that displays the proposed modifications generated by the processing unit on the user's terminal.

[0458] A "physical store" is a real place where customers actually visit and learn or practice cooking.

[0459] A "customer" is an individual who visits a physical store and receives support for a culinary experience.

[0460] "Means of supporting seasoning" refers to a system that provides optimal seasoning advice based on the seasonings and ingredients that customers can use when cooking.

[0461] This system utilizes the user's smart device application to support the in-store cooking experience. Users input details such as the name of the dish, any issues they are experiencing, and the types of seasonings they possess via their device. For example, this might include information like "chicken curry, bland flavor, tomato paste, coriander, garlic salt." This information is then transmitted from the device to a server in the cloud.

[0462] The server uses Google Cloud AI and OpenAI APIs to analyze this information and processes the input data using a specific generative AI model. The AI ​​model generates suggestions for optimal seasoning by referencing a database of dish characteristics and seasoning combinations. The generated suggestions are presented on the device in an easy-to-understand format, allowing the user to adjust the taste of the dish based on them.

[0463] For example, if a user is making chicken curry and notices that it's bland, they would enter the aforementioned prompt into the app. In response, the server would suggest, "Adding a little tomato paste and simmering for 10 minutes will enhance the flavor." Through this process, the user can gain clues to achieve the ideal taste in their dish.

[0464] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0465] Step 1:

[0466] The user enters the dish name, current problem, and available seasonings into the terminal. This information includes the specific dish name and problem. The input data is structured in a format such as "chicken curry, bland taste, tomato paste, coriander, garlic salt." The terminal correctly receives this information and packages it into a format that can be transmitted.

[0467] Step 2:

[0468] The terminal sends the processed user input information to a server in the cloud. The input data is transferred to the server via a secure channel using TCP / IP. The input content is received on the server side as structured data and prepared for analysis.

[0469] Step 3:

[0470] The server performs analysis based on the received information. This is where generative AI models incorporating Google Cloud AI and OpenAI APIs come into play. The server references information about dishes and seasonings in the database and generates suggested modifications for optimal seasoning based on the input data. This analysis is performed using database searches and algorithms to quickly respond to user inquiries.

[0471] Step 4:

[0472] The server sends the generated revised instructions back to the user's terminal. This output data includes easy-to-understand adjustments such as increasing or decreasing seasonings and adjusting cooking times. The output data is converted into a text format for display on the user's terminal.

[0473] Step 5:

[0474] The user cooks based on the suggested modifications displayed on the device. For example, following the suggestion, "Adding a little tomato paste and simmering for 10 minutes will enhance the flavor," the user adjusts the seasonings and performs additional steps to improve the dish's quality. This allows the user to bring a failed dish closer to the ideal taste.

[0475] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0476] This invention is a system that not only helps users recover from seasoning mistakes during cooking, but also provides a better user experience by taking into account the user's emotions. The system not only suggests the optimal cooking correction plan based on the user's input information and possessed seasoning data, but also uses an emotion engine to provide feedback that takes the user's psychological state into account.

[0477] The user first inputs the state of the dish, the current problem, and a list of available seasonings into the terminal. Meanwhile, the emotion engine receives data including the user's facial expressions, voice, and text input to determine the user's emotional state. This information is sent to the server, where the generative artificial intelligence and emotion engine on the server begin working together to analyze it.

[0478] The server first generates suggested modifications based on the input cooking information using artificial intelligence. Simultaneously, an emotion engine adjusts the suggestions to take into account the user's emotional state. For example, if the emotion engine detects that the user is feeling down, the feedback will include words of encouragement and be delivered in a gentle tone. Specifically, it might say something like, "Add a little salt and let it simmer slowly. It'll be good when the aroma starts to develop!"

[0479] The terminal displays suggestions and adjustment feedback sent from the server. Users can improve their cooking by making adjustments while receiving emotionally-responsive feedback along with the suggestions. This system aims to provide optimal support to users with different moods, and will be especially helpful for users who are unfamiliar with cooking.

[0480] The following describes the processing flow.

[0481] Step 1:

[0482] The user enters the name of the dish they are currently cooking, the seasoning problem they are facing, and a list of seasonings they possess into the device. In addition, emotional data is obtained by collecting facial expressions and voice through the device's camera or microphone.

[0483] Step 2:

[0484] The terminal sends the entered food information and emotion data to the server. Necessary security protocols are used during transmission to ensure the data is transferred securely.

[0485] Step 3:

[0486] The server receives cooking information from a generating artificial intelligence and analyzes the problem. The analysis takes into account the type of dish, the combination of seasonings, and the usual balance of flavors.

[0487] Step 4:

[0488] The emotion engine processes the received emotion data and determines the user's emotional state. For example, it identifies when the user is feeling stressed.

[0489] Step 5:

[0490] Based on the analysis of the dish, the server's AI generates suggested improvements. This process incorporates the analysis results of the emotion engine, providing feedback tailored to the user's emotional state. For example, it might offer feedback such as, "Adding a little salt would be good. Don't rush, just continue cooking and it will taste delicious!"

[0491] Step 6:

[0492] The server sends back the generated revisions and emotionally appropriate feedback to the terminal.

[0493] Step 7:

[0494] The device displays the received correction suggestions and feedback to the user. Based on the feedback, the user can make corrections to the cooking process and actually improve the taste of the dish.

[0495] (Example 2)

[0496] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0497] When cooking, users sometimes struggle to find appropriate solutions if they make a mistake with the seasoning. Furthermore, while a user's psychological state often influences the quality of the dish and their overall experience, conventional systems have struggled to provide support that takes user emotions into account.

[0498] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0499] In this invention, the server includes means for the user to input the state of the dish, problems, and seasoning information; means for collecting emotional data through the user's facial expressions, voice, and text input; and means for analyzing the input information using a generative AI model and an emotion engine to generate and adjust correction suggestions. This makes it possible to provide optimal cooking correction suggestions while taking the user's psychological state into consideration, and to enjoy an improved cooking experience.

[0500] A "user" refers to a person who uses the system to receive suggestions regarding the adjustment of their cooking.

[0501] "The state of the dish" refers to information indicating the progress and quality of the dish during cooking.

[0502] "Problems" refer to specific challenges or malfunctions that users encounter while cooking.

[0503] "Seasoning information" refers to data about the types and quantities of seasonings that the user possesses.

[0504] A "server" refers to a central computing resource that receives and processes user input information.

[0505] A "generative AI model" refers to an algorithm that generates optimal suggestions based on input data.

[0506] An "emotion engine" refers to a system that analyzes a user's emotional state and generates corresponding feedback.

[0507] "Proposed improvements" refers to information that specifically outlines suggestions and advice for addressing problems in the cooking process.

[0508] "Emotional data" refers to information about the user's psychological state, and includes data obtained from facial expressions, voice, and text input.

[0509] "Adjustment" refers to the process of incorporating sentiment data into the generated correction suggestions and presenting them to the user in a way that is individually tailored to their needs.

[0510] This invention is a system that assists users in improving their seasoning skills by providing optimal cooking correction suggestions and feedback when they make mistakes in seasoning during cooking. The system consists of a terminal and a server, and a key feature is that it generates feedback that takes the user's emotions into consideration.

[0511] First, the user uses a device to input information about the state of the dish, any current problems, and a list of seasonings they possess. At this stage, the device collects the user's facial expressions and voice through its camera and microphone, and also acquires emotional data through text input. Both the collected information and emotional data are then sent to the server.

[0512] On the server, a generative AI model and an emotion engine are used to analyze the provided data and generate the optimal cooking improvement suggestions for the user. The generative AI model has the ability to analyze the data entered using prompt sentences, producing accurate and effective suggestions. For example, if the user enters "It's bland," the prompt sentence used will be "The pasta you are currently cooking is bland. You have salt, pepper, soy sauce, and basil. Please generate the optimal cooking improvement suggestion." The suggestions obtained by this generative AI model are then adjusted by the emotion engine according to the user's emotional state. The emotion engine uses collected facial expressions, voice, and text information to determine the user's psychological state and adjusts the suggestions to include encouragement and kindness.

[0513] The revised suggestions, once adjusted, are presented to the user via the device. This allows users to receive not only suggestions to correct seasoning mistakes but also emotionally resonant feedback, resulting in a more satisfying cooking experience.

[0514] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0515] Step 1:

[0516] The user enters the dish's condition, any problems, and a list of seasonings into the terminal. This information is then entered into the system. Specifically, the user enters information using text fields on the interface and saves the data to the terminal by pressing the submit button.

[0517] Step 2:

[0518] The device collects the user's facial expressions and voice using a camera and microphone to acquire emotional data. The input here is camera images and audio data, which are used for emotion analysis. Specifically, the camera recognizes the user's face and records changes in facial expressions as digital data, the microphone captures the voice, and text-to-speech recognition software analyzes the emotions. The analysis result outputs the current emotional state.

[0519] Step 3:

[0520] The collected cooking information and emotional data are sent from the terminal to the server. This transmission takes place via an internet connection, and the data is received on the server side. The input data includes the state and problems of the dish, a list of seasonings, and the emotional state.

[0521] Step 4:

[0522] The server generates correction suggestions using a generative AI model based on the received data. In this process, the server identifies the problem with the cooking from the input information and creates a prompt message to input into the AI ​​model. Specifically, it uses a prompt such as, "The dish currently being cooked is bland. The available seasonings are salt, pepper, soy sauce, and basil. Please generate the best cooking improvement plan," and the AI ​​model outputs the optimal improvement solution.

[0523] Step 5:

[0524] The server's emotion engine analyzes the user's emotion data and adjusts the generated correction suggestions according to the user's emotional state. If the server determines that the user is "depressed," it adds words of encouragement or kindness to the suggestions. The output of this process is an adjusted correction suggestion.

[0525] Step 6:

[0526] The server-adjusted correction suggestions are sent to the terminal and presented to the user. The terminal outputs the data received from the server to its display interface, presenting the advice in a way that the user can easily understand.

[0527] Step 7:

[0528] The user modifies the dish based on the suggestions displayed on the device. For example, if the user receives the suggestion to "add a little salt and simmer it slowly," they actually add the seasonings in the kitchen and monitor the process. The output of this step is the modified dish.

[0529] (Application Example 2)

[0530] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0531] It is necessary to not only prevent seasoning mistakes during cooking, but also to improve satisfaction and efficiency in the cooking process by providing appropriate feedback that takes into account the cook's emotional state. However, existing cooking support systems do not take into account the psychological factors of the cook, and therefore have the problem of not being able to provide appropriate support, especially for those who are inexperienced in cooking.

[0532] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0533] In this invention, the server includes means for the user to input information about the dish, means for a generating artificial intelligence to analyze the input information and generate correction suggestions, means for recognizing the user's emotional state, and means for adjusting the correction suggestions based on that emotional state. This makes it possible to solve the cooking problem of seasoning while providing encouragement and suggestions that are tailored to the user's psychological state.

[0534] A "user" is an entity that uses this system to input information about cooking and receive feedback.

[0535] "Information about cooking" refers to data about the state and problems of the food being cooked, as well as information about the seasonings being used.

[0536] "Generative artificial intelligence" is artificial intelligence that has the function of analyzing input information and generating correction suggestions.

[0537] A "correction suggestion" is a suggestion for improving a dish, provided by artificial intelligence generated based on the state of the dish.

[0538] "Emotional state" refers to the user's psychological or emotional state, which is recognized from facial expressions, voice, text, etc.

[0539] "Adjustment feedback" refers to feedback provided after adjusting suggested modifications based on the user's emotional state.

[0540] A "server" is a device or system that receives input from a user, processes the information using generative artificial intelligence and emotion recognition capabilities, and provides suggestions and feedback.

[0541] In the system for realizing this invention, the user first inputs information about the dish using a terminal. This includes the state of the dish being cooked, any current problems, and a list of seasonings the user possesses. For sentiment analysis, the user's facial expressions, voice, and text are also collected from the terminal. The terminal then transmits this input information to a server.

[0542] The server uses Python-based generative artificial intelligence and emotion recognition libraries (e.g., Microsoft Azure Emotion API) to analyze the transmitted information. The generative AI generates correction suggestions based on the state of the dish, and emotion recognition adjusts the suggestions by determining the user's emotional state. Specifically, if the user is feeling stressed, the suggestions will use gentler language. For example, even if the user has burned the dish, the system will naturally offer support such as, "Let's take a little time and readjust the ingredients."

[0543] Suggested revisions and feedback after adjustments are displayed on the device, allowing users to proceed with cooking accordingly. This enables improvement of cooking skills and provides a sense of psychological reassurance.

[0544] For example, if a user inputs "The soup is too salty," the generative AI model will suggest "add water to dilute it." If the emotion recognition determines that the user is "feeling down," it will provide adjustment feedback such as, "It's okay, just add a little more water and it'll be perfect."

[0545] Examples of prompts to input into a generative AI model are as follows:

[0546] "Current dish status: too salty. User emotion: upset. Suggest an adjustment and comforting feedback."

[0547] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0548] Step 1:

[0549] Users input information about the state of their dishes, any problems, and the seasonings they possess via a terminal. Users also provide their facial expressions and voice as input data to the terminal. This information is used to improve the dishes.

[0550] Step 2:

[0551] The terminal sends data entered by the user to the server. The input data includes information about the dish and voice and facial expression data indicating the user's emotions, which are sent to the server as prompt messages.

[0552] Step 3:

[0553] The server uses generative artificial intelligence to analyze the cooking information and generate optimal modification suggestions. This analysis involves inputting the cooking information into the generative AI model and generating suggestions. The output is the suggested modifications.

[0554] Step 4:

[0555] The server uses an emotion recognition library to determine the user's emotional state from their input. Voice or facial expression data is used as input, and the result of the emotion analysis is output as an emotional state, such as "feeling stressed" or "feeling depressed."

[0556] Step 5:

[0557] The server adjusts the generated revision suggestions based on the emotional state obtained. Specifically, it processes the wording and expression of the suggestions to change according to the emotion. Here, the suggestions are adjusted to use gentler language and then output.

[0558] Step 6:

[0559] The server sends the adjusted correction suggestions and feedback to the terminal. The user receives the feedback displayed on the terminal and can proceed with the cooking process based on the suggestions. Finally, output is provided with the aim of improving the user's cooking.

[0560] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0561] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0562] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0563] [Fourth Embodiment]

[0564] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0565] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0566] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0567] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0568] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0569] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0570] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0571] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0572] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0573] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0574] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0575] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0576] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0577] This invention is a system designed to rectify situations where a user has failed to season a dish properly while cooking. Specifically, it uses generative artificial intelligence to suggest the optimal seasoning method. The invention consists of a terminal used by the user, an artificial intelligence program implemented on a server, and data communication means.

[0578] The user enters the name of the dish, the current problem, and a list of seasonings they have on hand via the terminal. For example, if a user notices that beef stew is bland while making it, they would enter information such as "beef stew, bland, salt, sugar, olive oil."

[0579] This information is sent from the terminal to the server. The generative artificial intelligence on the server analyzes the input information and determines what modifications would be effective in improving the taste of the dish. Here, the generative AI refers to a database of dish characteristics and seasoning combinations to generate the optimal suggestion for solving the problem.

[0580] For example, if the AI ​​determines that "adding a little salt and increasing the simmering time by another 5 minutes would enhance the flavor," specific advice is sent from the server to the terminal. This advice is displayed in an easy-to-understand text format, allowing the user to adjust the flavor of the dish accordingly.

[0581] In this way, users can avoid wasting failed dishes and ultimately provide delicious meals. This system aims to solve common seasoning mistakes that occur in home cooking and provide the enjoyment of cooking, and is especially useful for users with little cooking experience.

[0582] The following describes the processing flow.

[0583] Step 1:

[0584] The user uses a terminal to enter the name of the dish, the current seasoning issue, and the condiments they have. For example, they might enter "Curry, Not spicy enough, Salt, Curry powder, Tomato paste."

[0585] Step 2:

[0586] The terminal verifies the entered information and confirms that all necessary information is present. If any information is missing, it prompts the user for additional input. After verification, it sends the information to the server.

[0587] Step 3:

[0588] The server passes the received information to the generating AI module for analysis. The generating AI then starts analyzing the database to provide solutions based on the characteristics of the dishes and the combinations of seasonings.

[0589] Step 4:

[0590] The generating AI uses the analysis results to create optimal solutions to the input problem. For example, it might create specific suggestions such as, "To increase the spiciness, add 1 teaspoon of curry powder and a small amount of tomato paste."

[0591] Step 5:

[0592] The server sends the generated revisions to the terminal. The data sent is converted to a text format that is easy for the user to understand.

[0593] Step 6:

[0594] The device displays the proposed fixes to the user. The display is presented in a visually easy-to-understand format, and additional explanations and implementation steps are shown if necessary.

[0595] Step 7:

[0596] The user adjusts the dish according to the displayed suggestions. For example, they can add curry powder, taste it again, and make further adjustments as needed to achieve the ideal flavor.

[0597] (Example 1)

[0598] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0599] For beginners, seasoning and cooking methods are often challenging aspects of home and personal cooking. In particular, when trying new recipes, seasoning mistakes are common, leading to wasted ingredients. Effective methods are needed to avoid such situations and reduce cooking failures.

[0600] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0601] In this invention, the server includes means for the user to input data related to cooking, means for transmitting that data to a central processing unit, and means for a generative artificial intelligence implemented in the central processing unit to analyze the input data and generate suggestions for correction. This allows the user to quickly receive appropriate advice regarding problems during cooking, and to effectively avoid cooking failures.

[0602] A "user" is an individual or entity that inputs cooking-related data and receives advice from the system before carrying out cooking.

[0603] "Cooking-related data" includes data such as the name of the dish, current cooking problems, and information about the seasonings and ingredients available.

[0604] A "central processing unit" is a server or computer system installed to receive and analyze data sent from users.

[0605] "Generative artificial intelligence" refers to an artificial intelligence model used to analyze cooking-related data and generate suggestions for improvements.

[0606] "Analysis" is the process of identifying problems and areas for improvement based on input data related to cooking, and proposing the optimal cooking method.

[0607] A "suggested fix" is specific advice or recommended action generated to solve a cooking problem the user is facing.

[0608] "Visual presentation" refers to displaying the generated correction suggestions on a display device in a way that is easy for the user to understand.

[0609] This invention is a system for users to solve cooking-related problems, and was developed in particular to correct mistakes in seasoning and cooking methods during cooking. The system consists of multiple components, including a terminal, a server, and a generative AI model.

[0610] The user uses a terminal to input information about the cooking process. Specifically, they input the name of the dish, the problem they are currently experiencing, and a list of seasonings they have on hand. For example, if a user is cooking pasta and finds the taste unsatisfactory, they might input information such as "pasta, average taste, garlic, Parmesan cheese, black pepper." This information is then sent from the terminal to the server.

[0611] The server uses a generative AI model to analyze the received information. This generative AI refers to a database of food characteristics and seasoning combinations, and devises the optimal seasoning method based on the input information. The generative AI model analyzes the input data and generates suggestions for modifications to solve the problem.

[0612] Finally, the seasoning methods and modifications suggested by the generating AI are sent to the terminal by the server. The terminal visually displays the suggestions to the user. For example, if the AI ​​determines that "adding a little garlic and sprinkling Parmesan cheese will enhance the flavor," that advice will be displayed to the user.

[0613] This allows users to effectively resolve cooking problems by following specific advice received from the system. The system aims to improve the user's cooking experience by increasing cooking efficiency and reducing failures.

[0614] Example of a prompt:

[0615] Dish name: Pasta

[0616] Current problem: The taste is unremarkable.

[0617] Ingredients I have: Garlic, Parmesan cheese, black pepper

[0618] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0619] Step 1:

[0620] The terminal receives cooking information from the user as input. The user enters the name of the dish, the current problem, and the seasonings they have into the terminal's input interface. This inputs specific cooking data into the terminal (e.g., "pasta, average taste, garlic, Parmesan cheese, black pepper").

[0621] Step 2:

[0622] The terminal formats the entered cooking information and prepares it for data transmission. The data is formatted in text format and sent to the server via the network. The terminal's output becomes the data to be sent to the server.

[0623] Step 3:

[0624] The server receives data from the terminal as input and activates the generative AI model. The generative AI model refers to a database of cooking characteristics and performs analysis based on the input cooking data. Specifically, it identifies problems and devises solutions. As a result of the analysis, proposed modifications are generated.

[0625] Step 4:

[0626] The server receives the suggested modifications generated by the generative AI model as output and sends that information to the terminal. The output from the server is specific advice for improving the dish (e.g., "Add a little garlic and sprinkle with Parmesan cheese"). The server converts this advice into a format that is easy for the user to understand.

[0627] Step 5:

[0628] The terminal receives suggested corrections from the server as input and presents them visually to the user. The user reviews the advice displayed on the terminal and adjusts the cooking process accordingly. Based on the information provided, the user can correct any seasoning mistakes by performing the necessary actions during the cooking process. The terminal's output is an advertisement for the user.

[0629] (Application Example 1)

[0630] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0631] When customers cook in a physical store, those with little cooking experience often make mistakes with seasoning. In such cases, there is a need to provide efficient and appropriate means to correct seasoning mistakes and guide the dish to its optimal state. Furthermore, when customers bring various ingredients and use limited seasonings, flexible and quick solutions are necessary.

[0632] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0633] In this invention, the server includes means for the user to input information about the dish, means for transmitting that information to the processing unit, and means for the processing unit to analyze the input information and generate suggested modifications. This makes it possible to suggest appropriate seasonings based on the ingredients and seasonings brought by the customer when they cook in a physical store, thereby improving the customer's cooking experience.

[0634] A "user" is an individual who uses the system to input information about cooking and receive seasoning suggestions.

[0635] "Means of inputting information" refers to an interface in which users input data such as the type of dish, problems, and available seasonings into the terminal.

[0636] "Means of transmitting to a processing unit" refers to communication methods that transmit information entered by the user to a server or AI platform in the cloud via a network.

[0637] A "processing device" is a computer equipped with a database and algorithms for analyzing input information received from a user and generating optimal correction suggestions.

[0638] "Methods for generating revised proposals" refers to a method that uses a generative AI model to create specific suggestions for improving the taste of a dish based on input information.

[0639] "Means of presenting to the user" refers to an interface that displays the proposed modifications generated by the processing unit on the user's terminal.

[0640] A "physical store" is a real place where customers actually visit and learn or practice cooking.

[0641] A "customer" is an individual who visits a physical store and receives support for a culinary experience.

[0642] "Means of supporting seasoning" refers to a system that provides optimal seasoning advice based on the seasonings and ingredients that customers can use when cooking.

[0643] This system utilizes the user's smart device application to support the in-store cooking experience. Users input details such as the name of the dish, any issues they are experiencing, and the types of seasonings they possess via their device. For example, this might include information like "chicken curry, bland flavor, tomato paste, coriander, garlic salt." This information is then transmitted from the device to a server in the cloud.

[0644] The server uses Google Cloud AI and OpenAI APIs to analyze this information and processes the input data using a specific generative AI model. The AI ​​model generates suggestions for optimal seasoning by referencing a database of dish characteristics and seasoning combinations. The generated suggestions are presented on the device in an easy-to-understand format, allowing the user to adjust the taste of the dish based on them.

[0645] For example, if a user is making chicken curry and notices that it's bland, they would enter the aforementioned prompt into the app. In response, the server would suggest, "Adding a little tomato paste and simmering for 10 minutes will enhance the flavor." Through this process, the user can gain clues to achieve the ideal taste in their dish.

[0646] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0647] Step 1:

[0648] The user enters the dish name, current problem, and available seasonings into the terminal. This information includes the specific dish name and problem. The input data is structured in a format such as "chicken curry, bland taste, tomato paste, coriander, garlic salt." The terminal correctly receives this information and packages it into a format that can be transmitted.

[0649] Step 2:

[0650] The terminal sends the processed user input information to a server in the cloud. The input data is transferred to the server via a secure channel using TCP / IP. The input content is received on the server side as structured data and prepared for analysis.

[0651] Step 3:

[0652] The server performs analysis based on the received information. This is where generative AI models incorporating Google Cloud AI and OpenAI APIs come into play. The server references information about dishes and seasonings in the database and generates suggested modifications for optimal seasoning based on the input data. This analysis is performed using database searches and algorithms to quickly respond to user inquiries.

[0653] Step 4:

[0654] The server sends the generated revised instructions back to the user's terminal. This output data includes easy-to-understand adjustments such as increasing or decreasing seasonings and adjusting cooking times. The output data is converted into a text format for display on the user's terminal.

[0655] Step 5:

[0656] The user cooks based on the suggested modifications displayed on the device. For example, following the suggestion, "Adding a little tomato paste and simmering for 10 minutes will enhance the flavor," the user adjusts the seasonings and performs additional steps to improve the dish's quality. This allows the user to bring a failed dish closer to the ideal taste.

[0657] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0658] This invention is a system that not only helps users recover from seasoning mistakes during cooking, but also provides a better user experience by taking into account the user's emotions. The system not only suggests the optimal cooking correction plan based on the user's input information and possessed seasoning data, but also uses an emotion engine to provide feedback that takes the user's psychological state into account.

[0659] The user first inputs the state of the dish, the current problem, and a list of available seasonings into the terminal. Meanwhile, the emotion engine receives data including the user's facial expressions, voice, and text input to determine the user's emotional state. This information is sent to the server, where the generative artificial intelligence and emotion engine on the server begin working together to analyze it.

[0660] The server first generates suggested modifications based on the input cooking information using artificial intelligence. Simultaneously, an emotion engine adjusts the suggestions to take into account the user's emotional state. For example, if the emotion engine detects that the user is feeling down, the feedback will include words of encouragement and be delivered in a gentle tone. Specifically, it might say something like, "Add a little salt and let it simmer slowly. It'll be good when the aroma starts to develop!"

[0661] The terminal displays suggestions and adjustment feedback sent from the server. Users can improve their cooking by making adjustments while receiving emotionally-responsive feedback along with the suggestions. This system aims to provide optimal support to users with different moods, and will be especially helpful for users who are unfamiliar with cooking.

[0662] The following describes the processing flow.

[0663] Step 1:

[0664] The user enters the name of the dish they are currently cooking, the seasoning problem they are facing, and a list of seasonings they possess into the device. In addition, emotional data is obtained by collecting facial expressions and voice through the device's camera or microphone.

[0665] Step 2:

[0666] The terminal sends the entered food information and emotion data to the server. Necessary security protocols are used during transmission to ensure the data is transferred securely.

[0667] Step 3:

[0668] The server receives cooking information from a generating artificial intelligence and analyzes the problem. The analysis takes into account the type of dish, the combination of seasonings, and the usual balance of flavors.

[0669] Step 4:

[0670] The emotion engine processes the received emotion data and determines the user's emotional state. For example, it identifies when the user is feeling stressed.

[0671] Step 5:

[0672] Based on the analysis of the dish, the server's AI generates suggested improvements. This process incorporates the analysis results of the emotion engine, providing feedback tailored to the user's emotional state. For example, it might offer feedback such as, "Adding a little salt would be good. Don't rush, just continue cooking and it will taste delicious!"

[0673] Step 6:

[0674] The server sends back the generated revisions and emotionally appropriate feedback to the terminal.

[0675] Step 7:

[0676] The device displays the received correction suggestions and feedback to the user. Based on the feedback, the user can make corrections to the cooking process and actually improve the taste of the dish.

[0677] (Example 2)

[0678] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0679] When cooking, users sometimes struggle to find appropriate solutions if they make a mistake with the seasoning. Furthermore, while a user's psychological state often influences the quality of the dish and their overall experience, conventional systems have struggled to provide support that takes user emotions into account.

[0680] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0681] In this invention, the server includes means for the user to input the state of the dish, problems, and seasoning information; means for collecting emotional data through the user's facial expressions, voice, and text input; and means for analyzing the input information using a generative AI model and an emotion engine to generate and adjust correction suggestions. This makes it possible to provide optimal cooking correction suggestions while taking the user's psychological state into consideration, and to enjoy an improved cooking experience.

[0682] A "user" refers to a person who uses the system to receive suggestions regarding the adjustment of their cooking.

[0683] "The state of the dish" refers to information indicating the progress and quality of the dish during cooking.

[0684] "Problems" refer to specific challenges or malfunctions that users encounter while cooking.

[0685] "Seasoning information" refers to data about the types and quantities of seasonings that the user possesses.

[0686] A "server" refers to a central computing resource that receives and processes user input information.

[0687] A "generative AI model" refers to an algorithm that generates optimal suggestions based on input data.

[0688] An "emotion engine" refers to a system that analyzes a user's emotional state and generates corresponding feedback.

[0689] "Proposed improvements" refers to information that specifically outlines suggestions and advice for addressing problems in the cooking process.

[0690] "Emotional data" refers to information about the user's psychological state, and includes data obtained from facial expressions, voice, and text input.

[0691] "Adjustment" refers to the process of incorporating sentiment data into the generated correction suggestions and presenting them to the user in a way that is individually tailored to their needs.

[0692] This invention is a system that assists users in improving their seasoning skills by providing optimal cooking correction suggestions and feedback when they make mistakes in seasoning during cooking. The system consists of a terminal and a server, and a key feature is that it generates feedback that takes the user's emotions into consideration.

[0693] First, the user uses a device to input information about the state of the dish, any current problems, and a list of seasonings they possess. At this stage, the device collects the user's facial expressions and voice through its camera and microphone, and also acquires emotional data through text input. Both the collected information and emotional data are then sent to the server.

[0694] On the server, a generative AI model and an emotion engine are used to analyze the provided data and generate the optimal cooking improvement suggestions for the user. The generative AI model has the ability to analyze the data entered using prompt sentences, producing accurate and effective suggestions. For example, if the user enters "It's bland," the prompt sentence used will be "The pasta you are currently cooking is bland. You have salt, pepper, soy sauce, and basil. Please generate the optimal cooking improvement suggestion." The suggestions obtained by this generative AI model are then adjusted by the emotion engine according to the user's emotional state. The emotion engine uses collected facial expressions, voice, and text information to determine the user's psychological state and adjusts the suggestions to include encouragement and kindness.

[0695] The revised suggestions, once adjusted, are presented to the user via the device. This allows users to receive not only suggestions to correct seasoning mistakes but also emotionally resonant feedback, resulting in a more satisfying cooking experience.

[0696] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0697] Step 1:

[0698] The user enters the dish's condition, any problems, and a list of seasonings into the terminal. This information is then entered into the system. Specifically, the user enters information using text fields on the interface and saves the data to the terminal by pressing the submit button.

[0699] Step 2:

[0700] The device collects the user's facial expressions and voice using a camera and microphone to acquire emotional data. The input here is camera images and audio data, which are used for emotion analysis. Specifically, the camera recognizes the user's face and records changes in facial expressions as digital data, the microphone captures the voice, and text-to-speech recognition software analyzes the emotions. The analysis result outputs the current emotional state.

[0701] Step 3:

[0702] The collected cooking information and emotional data are sent from the terminal to the server. This transmission takes place via an internet connection, and the data is received on the server side. The input data includes the state and problems of the dish, a list of seasonings, and the emotional state.

[0703] Step 4:

[0704] The server generates correction suggestions using a generative AI model based on the received data. In this process, the server identifies the problem with the cooking from the input information and creates a prompt message to input into the AI ​​model. Specifically, it uses a prompt such as, "The dish currently being cooked is bland. The available seasonings are salt, pepper, soy sauce, and basil. Please generate the best cooking improvement plan," and the AI ​​model outputs the optimal improvement solution.

[0705] Step 5:

[0706] The server's emotion engine analyzes the user's emotion data and adjusts the generated correction suggestions according to the user's emotional state. If the server determines that the user is "depressed," it adds words of encouragement or kindness to the suggestions. The output of this process is an adjusted correction suggestion.

[0707] Step 6:

[0708] The server-adjusted correction suggestions are sent to the terminal and presented to the user. The terminal outputs the data received from the server to its display interface, presenting the advice in a way that the user can easily understand.

[0709] Step 7:

[0710] The user modifies the dish based on the suggestions displayed on the device. For example, if the user receives the suggestion to "add a little salt and simmer it slowly," they actually add the seasonings in the kitchen and monitor the process. The output of this step is the modified dish.

[0711] (Application Example 2)

[0712] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0713] It is necessary to not only prevent seasoning mistakes during cooking, but also to improve satisfaction and efficiency in the cooking process by providing appropriate feedback that takes into account the cook's emotional state. However, existing cooking support systems do not take into account the psychological factors of the cook, and therefore have the problem of not being able to provide appropriate support, especially for those who are inexperienced in cooking.

[0714] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0715] In this invention, the server includes means for the user to input information about the dish, means for a generating artificial intelligence to analyze the input information and generate correction suggestions, means for recognizing the user's emotional state, and means for adjusting the correction suggestions based on that emotional state. This makes it possible to solve the cooking problem of seasoning while providing encouragement and suggestions that are tailored to the user's psychological state.

[0716] A "user" is an entity that uses this system to input information about cooking and receive feedback.

[0717] "Information about cooking" refers to data about the state and problems of the food being cooked, as well as information about the seasonings being used.

[0718] "Generative artificial intelligence" is artificial intelligence that has the function of analyzing input information and generating correction suggestions.

[0719] A "correction suggestion" is a suggestion for improving a dish, provided by artificial intelligence generated based on the state of the dish.

[0720] "Emotional state" refers to the user's psychological or emotional state, which is recognized from facial expressions, voice, text, etc.

[0721] "Adjustment feedback" refers to feedback provided after adjusting suggested modifications based on the user's emotional state.

[0722] A "server" is a device or system that receives input from a user, processes the information using generative artificial intelligence and emotion recognition capabilities, and provides suggestions and feedback.

[0723] In the system for realizing this invention, the user first inputs information about the dish using a terminal. This includes the state of the dish being cooked, any current problems, and a list of seasonings the user possesses. For sentiment analysis, the user's facial expressions, voice, and text are also collected from the terminal. The terminal then transmits this input information to a server.

[0724] The server uses Python-based generative artificial intelligence and emotion recognition libraries (e.g., Microsoft Azure Emotion API) to analyze the transmitted information. The generative AI generates correction suggestions based on the state of the dish, and emotion recognition adjusts the suggestions by determining the user's emotional state. Specifically, if the user is feeling stressed, the suggestions will use gentler language. For example, even if the user has burned the dish, the system will naturally offer support such as, "Let's take a little time and readjust the ingredients."

[0725] Suggested revisions and feedback after adjustments are displayed on the device, allowing users to proceed with cooking accordingly. This enables improvement of cooking skills and provides a sense of psychological reassurance.

[0726] For example, if a user inputs "The soup is too salty," the generative AI model will suggest "add water to dilute it." If the emotion recognition determines that the user is "feeling down," it will provide adjustment feedback such as, "It's okay, just add a little more water and it'll be perfect."

[0727] Examples of prompts to input into a generative AI model are as follows:

[0728] "Current dish status: too salty. User emotion: upset. Suggest an adjustment and comforting feedback."

[0729] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0730] Step 1:

[0731] Users input information about the state of their dishes, any problems, and the seasonings they possess via a terminal. Users also provide their facial expressions and voice as input data to the terminal. This information is used to improve the dishes.

[0732] Step 2:

[0733] The terminal sends data entered by the user to the server. The input data includes information about the dish and voice and facial expression data indicating the user's emotions, which are sent to the server as prompt messages.

[0734] Step 3:

[0735] The server uses generative artificial intelligence to analyze the cooking information and generate optimal modification suggestions. This analysis involves inputting the cooking information into the generative AI model and generating suggestions. The output is the suggested modifications.

[0736] Step 4:

[0737] The server uses an emotion recognition library to determine the user's emotional state from their input. Voice or facial expression data is used as input, and the result of the emotion analysis is output as an emotional state, such as "feeling stressed" or "feeling depressed."

[0738] Step 5:

[0739] The server adjusts the generated revision suggestions based on the emotional state obtained. Specifically, it processes the wording and expression of the suggestions to change according to the emotion. Here, the suggestions are adjusted to use gentler language and then output.

[0740] Step 6:

[0741] The server sends the adjusted correction suggestions and feedback to the terminal. The user receives the feedback displayed on the terminal and can proceed with the cooking process based on the suggestions. Finally, output is provided with the aim of improving the user's cooking.

[0742] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0743] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0744] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0745] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0746] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0747] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0748] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0749] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0750] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0751] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0752] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0753] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0754] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0755] 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.

[0756] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0757] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0758] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0759] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0760] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0761] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0762] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted as being incorporated by reference.

[0763] The following is further disclosed regarding the embodiments described above.

[0764] (Claim 1)

[0765] A means for users to input information about the dish,

[0766] A means of transmitting that information to an artificial intelligence,

[0767] A means by which a generative artificial intelligence analyzes input information and generates correction suggestions,

[0768] A means of presenting those proposed revisions to the user,

[0769] A system that includes this.

[0770] (Claim 2)

[0771] The system according to claim 1, comprising a means for generating artificial intelligence to propose the optimal cooking modification plan based on the user's information on seasonings.

[0772] (Claim 3)

[0773] The system according to claim 1, further comprising means for displaying adjustment methods suggested by generating artificial intelligence when the user performs additional cooking based on input information.

[0774] "Example 1"

[0775] (Claim 1)

[0776] A means for users to input data related to cooking,

[0777] A means for transmitting that data to a central processing unit,

[0778] A means by which a generative artificial intelligence implemented in a central processing unit analyzes input data and generates correction suggestions,

[0779] A means for visually presenting the generated revision suggestions to the user,

[0780] A system that includes this.

[0781] (Claim 2)

[0782] The system according to claim 1, comprising means for generating artificial intelligence to suggest the optimal cooking modification plan based on information about the seasoning ingredients possessed by the user.

[0783] (Claim 3)

[0784] The system according to claim 1, further comprising means for presenting adjustment methods suggested by generating artificial intelligence when performing additional cooking based on data entered by the user.

[0785] "Application Example 1"

[0786] (Claim 1)

[0787] A means for users to input information about the dish,

[0788] A means for transmitting that information to a processing device,

[0789] A means by which a processing unit analyzes input information and generates a revised version,

[0790] A means of presenting the proposed revisions to the user,

[0791] When cooking using ingredients brought in by customers within a physical store, a means of supporting the seasoning process is provided.

[0792] A system that includes this.

[0793] (Claim 2)

[0794] The system according to claim 1, comprising a means for the processing device to propose an optimal cooking modification plan based on the user's information on seasonings.

[0795] (Claim 3)

[0796] The system according to claim 1, further comprising means for displaying adjustment methods suggested by the processing device when the user performs additional cooking based on input information.

[0797] "Example 2 of combining an emotion engine"

[0798] (Claim 1)

[0799] A means for the user to input the state of the dish, any problems, and information about the seasonings,

[0800] A means of collecting emotional data through the user's facial expressions, voice, and text input,

[0801] A means of transmitting that information and emotional data to a server,

[0802] The server includes means for analyzing input information using a generative AI model and an emotion engine, and for generating and adjusting correction suggestions.

[0803] A means of presenting the adjusted correction suggestions to the user,

[0804] A system that includes this.

[0805] (Claim 2)

[0806] The system according to claim 1, comprising a means for a generating AI model to propose the optimal cooking modification plan based on the user's condiment information, and further adjusting the feedback to reflect the user's emotional state.

[0807] (Claim 3)

[0808] The system according to claim 1, comprising means for displaying adjustment methods suggested by a generating AI model and emotion-based feedback when the user performs additional cooking based on input information.

[0809] "Application example 2 when combining with an emotional engine"

[0810] (Claim 1)

[0811] A means for users to input information about the dish,

[0812] A means of transmitting that information to an artificial intelligence,

[0813] A means by which a generative artificial intelligence analyzes input information and generates correction suggestions,

[0814] A means of recognizing the user's emotional state,

[0815] A means of adjusting the proposed changes based on that emotional state,

[0816] A means of presenting those revision suggestions and adjustment feedback to the user,

[0817] A system that includes this.

[0818] (Claim 2)

[0819] The system according to claim 1, comprising a means for generating artificial intelligence to propose the optimal cooking modification plan based on the user's information on seasonings.

[0820] (Claim 3)

[0821] The system according to claim 1, further comprising means for displaying suggestions from generating artificial intelligence and feedback adjusted based on the user's emotional state when the user performs additional cooking based on input information. [Explanation of Symbols]

[0822] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means for users to input information about the dish, A means of transmitting that information to an artificial intelligence, A means by which a generative artificial intelligence analyzes input information and generates correction suggestions, A means of presenting those proposed revisions to the user, A system that includes this.

2. The system according to claim 1, comprising a means for generating artificial intelligence to propose the optimal cooking modification plan based on the user's information on seasonings.

3. The system according to claim 1, further comprising means for displaying adjustment methods suggested by generating artificial intelligence when the user performs additional cooking based on input information.

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