system

Abstract

We provide the system. [Solution] An input means for receiving taste information and health information from the user, A generation means for generating a taste profile based on the aforementioned taste information, Based on the taste profile generated by the generation means, an identification means identifies the optimal cooking method by referring to a past recipe database, A means for adjusting cooking methods and ingredients, taking into account the user's health information, An output means for generating and providing final recipe data 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, the method including the 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 modern society, many people have the desire to taste the flavors of childhood memories or the dishes of stores that have already closed. However, it is difficult to faithfully reproduce these flavors, and it is particularly difficult for people who are subject to specific dietary restrictions for health reasons to obtain such satisfaction. Conventional cooking recipes and cooking methods have the problem that it is difficult to adjust recipes considering individual tastes and health, and they cannot meet individual needs.

Means for Solving the Problems

[0005] This invention provides a system that receives taste and health information memorized by the user as input, and uses AI to generate an optimal taste profile based on this information. This system identifies cooking methods that reproduce the user's unique taste experience by referring to a database of past recipes, and further adjusts the cooking methods and ingredients based on health information to generate a recipe suitable for the user. The final recipe is provided to the user in a way that considers the balance between taste and health, thereby allowing the user to enjoy familiar tastes while also addressing their health restrictions.

[0006] A "user" refers to an individual who uses the system to provide taste and health information.

[0007] "Taste information" refers to data about specific taste characteristics and preferences that a user remembers.

[0008] "Health information" refers to data related to the user's health status, including dietary restrictions and specific nutritional requirements.

[0009] "Input means" refers to a device or process used to receive information from a user.

[0010] A "taste profile" is a data structure generated by AI based on taste information provided by the user, and it represents specific taste characteristics.

[0011] "Generation means" refers to the process or mechanism for constructing a taste profile using the user's taste information as input.

[0012] "Identification means" refers to the process or function of identifying the optimal cooking method from a database of past recipes using the generated taste profile.

[0013] "Adjustment means" refers to a process or mechanism for modifying and optimizing cooking methods and ingredient selections, taking into account the user's health information.

[0014] "Output means" refers to the device or process used to provide the user with the final generated recipe.

[0015] "Recipe data" refers to a collection of data about how to prepare a dish, including information such as ingredients, cooking steps, and taste characteristics. [Brief explanation of the drawing]

[0016] [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] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This 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] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This 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] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This 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] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This 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 combined with an emotion engine. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when combined with an emotion engine.

Mode for Carrying Out the Invention

[0017] Hereinafter, an example of an embodiment of the system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0018] First, the terms used in the following description will be explained.

[0019] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be one arithmetic unit or a combination of a plurality of arithmetic units. Also, the processor may be one type of arithmetic unit or a combination of a plurality of 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.

[0020] 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.

[0021] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0022] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0023] 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."

[0024] [First Embodiment]

[0025] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0026] 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.

[0027] 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).

[0028] 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.

[0029] 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.

[0030] 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.

[0031] 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.

[0032] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0033] 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.

[0034] 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.

[0035] 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.

[0036] 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".

[0037] This invention is a system that starts with the user inputting specific taste and health information, and then uses AI technology to generate an optimal recipe based on that information. Specifically, it is carried out in the following steps.

[0038] First, users use a smartphone or computer to input information about nostalgic dishes and flavors they remember. This information includes specific dish names, flavor characteristics (e.g., "sweet and soy sauce flavor"), ingredients used, and cooking methods. Users also input information about their health, such as dietary restrictions related to high blood pressure or diabetes.

[0039] Next, the terminal sends the data entered by the user to the server. During this transmission process, the data is encrypted and sent in a secure manner. In particular, metadata such as the user ID and time are also transmitted simultaneously to ensure the integrity and consistency of the data.

[0040] The server uses the received data to generate a taste profile through an AI algorithm. This profile analyzes text data using natural language processing to extract the essential elements of the taste the user desires. Next, this profile is used to search a database for past recipes with similar taste characteristics and select the optimal cooking method and ingredients.

[0041] Furthermore, the server generates nutritionally balanced recipes based on the user's health information. Here, an AI-designed evaluation model is used to adjust the appropriate quantities of ingredients and seasonings. This process ensures that users receive recipes that are both health-conscious and satisfying in taste.

[0042] Finally, the server sends the final determined recipe to the terminal in digital format. The terminal displays this information on its user interface, allowing the user to cook the dish accordingly. It may also include visual support and step-by-step instructions, making cooking easier.

[0043] As a concrete example, consider a case where a user wants to recreate the taste of "meat and potato stew that their mother used to make." In this case, the user inputs "sweet and salty soy sauce flavor" as a characteristic of the dish. Based on this information, the server searches for recipe data with a similar taste profile, and further takes into account that the user needs to be mindful of high blood pressure, generating a recipe that is satisfying while being low in salt. In this way, the user can enjoy the desired taste safely and healthily.

[0044] The following describes the processing flow.

[0045] Step 1:

[0046] Users launch a dedicated app and enter taste information about their favorite dishes, as well as personal health information. Specifically, they enter the dish name, flavor characteristics, ingredients used, cooking method, and health restrictions (e.g., salt restriction).

[0047] Step 2:

[0048] The terminal formats the information entered by the user into the appropriate format and sends it to the server. The transmitted data includes not only the user's input but also the user ID and transmission time.

[0049] Step 3:

[0050] The server analyzes the received user information and uses natural language processing techniques to extract important keywords from the input text. This analysis generates a taste profile that clearly understands the user's requests.

[0051] Step 4:

[0052] The server searches a recipe database based on the generated taste profile and identifies past recipes with similar taste characteristics. This process utilizes pattern matching and clustering techniques.

[0053] Step 5:

[0054] The server adjusts the identified recipe, taking into account the user's health information. An AI model appropriately modifies the quantity and type of ingredients and the proportion of seasonings to optimize the nutritional balance. This step utilizes nutritional models and health restriction models.

[0055] Step 6:

[0056] The server finalizes the adjusted recipe and creates recipe data in a user-friendly format. The recipe includes an ingredient list, cooking instructions, preparation time, and equipment information.

[0057] Step 7:

[0058] The server sends the completed recipe to the terminal. The transmission method is optimized for the user interface, and the system is designed to allow for easy and convenient display of the recipe.

[0059] Step 8:

[0060] The device displays the received recipe on the user interface, allowing the user to begin cooking. The display includes visual support and other features to make the cooking process intuitively understandable.

[0061] (Example 1)

[0062] 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."

[0063] In today's world, providing recipes tailored to the diverse taste preferences and health conditions of individual users is a challenging task. Especially when there are specific health constraints, achieving a satisfying taste while meeting those conditions requires specialized knowledge, placing a significant burden on the average person. Furthermore, even when there is a desire to recreate nostalgic flavors, selecting effective cooking methods and ingredients can be difficult. Therefore, there is a need for a method that can generate appropriate recipes while considering individual preferences and health information.

[0064] 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.

[0065] In this invention, the server includes data input means, profile generation means, processing means, meal adjustment means, and information output means. This makes it possible for users to recreate nostalgic flavors while being provided with nutritionally balanced recipes that take health into consideration.

[0066] "Data input means" refers to a function for collecting taste and health information from users and incorporating it into the system.

[0067] The "profile generation means" is a function that models the characteristics of taste based on the taste information provided by the user and creates a taste profile.

[0068] The "processing means" refers to a function that references the generated taste profile and identifies the optimal cooking method from a database of past cooking information.

[0069] A "meal adjustment tool" is a function that adjusts cooking methods and ingredients based on the user's health information to create a meal suitable for their health.

[0070] The "information output means" refers to a function for providing the user with the final generated meal data.

[0071] This invention is a system that begins with the user inputting specific taste and health information. The user uses a device such as a smartphone or personal computer to input detailed information about nostalgic dishes and their tastes. This input information includes specific dish names and taste characteristics (e.g., "sweet and soy sauce flavor"), ingredients used, cooking methods, and even health information (e.g., information on dietary restrictions such as high blood pressure or diabetes).

[0072] Information entered by the user is transmitted to the server via the terminal. The terminal encrypts the data and transmits it using a secure protocol (e.g., HTTPS). Based on the received information, the server analyzes the user's taste data using natural language processing technology and extracts its essential elements. The taste profile generated based on the analysis results is created using a profile generation method that utilizes AI technology.

[0073] The server has a processing mechanism that refers to this taste profile and identifies the optimal cooking method from a database of past cooking information. It also includes a meal adjustment mechanism that adjusts cooking methods and ingredients considering the user's health information. This generates final meal data with adjusted nutritional balance.

[0074] The generated recipe is sent from the server to the terminal, which displays it in the user interface. The user can then cook the dish according to this, and the recipe may include visual support and step-by-step instructions, making cooking easier.

[0075] A concrete example is when a user wants to recreate the taste of "meat and potato stew that their mother used to make." In this case, the user enters "sweet and salty soy sauce flavor" as a characteristic. Based on this information, the server searches for recipe data with a similar taste profile and then provides a low-sodium recipe that takes high blood pressure into consideration.

[0076] An example of a prompt message might be, "I want to recreate the sweet soy sauce-flavored nikujaga (meat and potato stew) my mother used to make. I have high blood pressure, so I'd like a low-sodium recipe." Such a system allows users to enjoy dishes that suit their preferences while also being mindful of their health.

[0077] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0078] Step 1:

[0079] Users input information about nostalgic dishes and their flavors using devices such as smartphones and personal computers. This input includes specific dish names, flavor characteristics (e.g., "sweetness and soy sauce flavor"), ingredients used, cooking methods, and health information (e.g., high blood pressure or diabetes). The input data is structured and handled in an organized format by the device. As output, a data package is generated on the device, encoded and ready for transmission.

[0080] Step 2:

[0081] The terminal encrypts the data package entered by the user and sends it to the server using a secure communication protocol (e.g., HTTPS). The input includes encoded data packages from the terminal. The data is sent in chunks and encrypted to maintain network security. The output is the highly secure data received by the server.

[0082] Step 3:

[0083] The server decodes the received data and analyzes the user's taste and health information. The input is decrypted user data. The server uses natural language processing technology to analyze the received text data and extract the taste characteristics desired by the user. After data analysis, a taste profile is generated through AI technology. The taste profile is generated as output and is then passed on to the next processing step.

[0084] Step 4:

[0085] The server uses the generated taste profile to search for the optimal cooking method from a database of past cooking information. The input is the generated taste profile. The AI ​​algorithm uses this profile to search for and select similar recipes. The output is a list of the optimal cooking methods and ingredients.

[0086] Step 5:

[0087] The server adjusts the selected cooking method and ingredients based on the user's health information. The input consists of a list of cooking methods and ingredients, and the user's health profile. The server utilizes an AI evaluation model to readjust ingredient quantities, considering nutritional balance. The output is a health-conscious recipe.

[0088] Step 6:

[0089] The final recipe data is sent from the server to the terminal. The input is health-conscious recipe information sent from the server. The terminal displays the received recipe through a user interface. The user can proceed with cooking according to the presented recipe, receiving visual support and step-by-step instructions. The output is the completed recipe information displayed on the terminal.

[0090] (Application Example 1)

[0091] 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."

[0092] Today's individual customers, despite various health constraints, want to enjoy meals that suit their individual preferences. However, in reality, it is difficult for typical restaurants to provide customized dishes that take into account each customer's detailed taste preferences and health information. Furthermore, the lack of immediate menu suggestions from restaurant staff makes improving customer satisfaction a challenge.

[0093] 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.

[0094] In this invention, the server includes data input means for receiving taste information and health information from the user, profile generation means for generating a taste profile based on the taste information, and evaluation means for identifying the optimal cooking method by referring to a past food database. This enables the generation of customized recipes that take into account the taste and health of individual customers, and rapid menu suggestions using a digital visualization device.

[0095] "Data input means" refers to methods for obtaining taste information and health information from users.

[0096] The "profile generation means" is a function that creates an optimal taste profile for each individual user based on the acquired taste information.

[0097] The "evaluation method" is a method that identifies the optimal cooking method by referring to a previously recorded food database based on the generated taste profile.

[0098] "Nutritional adjustment means" refers to a function that optimizes the selected cooking method and the nutrients of ingredients, taking into account the health information provided by the user.

[0099] "Information presentation means" refers to a method of displaying the final generated dish composition data on the user's terminal and making suggestions to the user.

[0100] "Visual presentation means" refers to the function of visually displaying the proposed dishes using digital visualization devices in the store.

[0101] "Natural language processing technology" is a technology that analyzes information from users and extracts necessary information from text data.

[0102] A "computational model" is an algorithm used to optimize nutrients based on a user's health information.

[0103] This invention is a system that enables restaurants to suggest customized dishes based on the individual preferences and health conditions of their customers. By using a server, terminals, and a digital visualization device, it achieves effective cooking suggestions.

[0104] The server receives taste and health information from the user via a data input device. This information is transmitted from the user's smart glasses or mobile device. The data is encrypted and transmitted to the server using a secure communication method. The server uses the taste information to generate a taste profile using a profile generation device. Then, using an evaluation device, it refers to a food database based on the generated profile to identify the optimal cooking method and ingredients for the user.

[0105] This cooking method and ingredient information is further optimized based on the user's health information using a nutritional adjustment mechanism. The adjusted dish composition data is provided to the user terminal by an information presentation mechanism. A digital visualization device provides a visual suggestion of the dish as a visual presentation mechanism, displaying the steps and reasons for recommending the cooking in real time.

[0106] For example, if a user enters a prompt such as, "I would like a sweet and rich Japanese-style curry, but I want it to be low in calories," the system instantly identifies the curry recipe and cooking procedure that meets the request and displays it on a digital display. This allows store staff to quickly prepare and serve the dish.

[0107] The generative AI model optimizes recipe suggestions based on user input, adjusting specific ingredients and procedures using a computational model. To achieve this, it utilizes a cloud server, smart glasses, or mobile device as hardware, and a natural language processing library as software.

[0108] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0109] Step 1:

[0110] Users input taste and health information via smart glasses or mobile devices. This input includes prompts regarding specific taste characteristics and health restrictions. The information is encrypted and transmitted to the server using a secure communication protocol.

[0111] Step 2:

[0112] The server receives the received information through a data input mechanism and analyzes it using natural language processing technology. This extracts necessary taste and health-related elements from the text data. The extracted data is then used as material for generating taste profiles.

[0113] Step 3:

[0114] The server's profile generation mechanism generates a taste profile based on the extracted data. The taste profile represents a specific taste pattern that reflects the user's preferences. This profile is used in the next step.

[0115] Step 4:

[0116] The server uses evaluation tools to search a food database based on taste profiles and identify the optimal cooking method and ingredients. This process is performed by a database matching algorithm, which selects the most suitable recipe from among many.

[0117] Step 5:

[0118] The identified cooking methods and ingredients are adjusted to the user's health information using nutritional adjustment tools. This adjustment utilizes a computational model that optimizes nutrients. After this, the final dish composition data is generated.

[0119] Step 6:

[0120] The server transmits the generated dish composition data to the user's terminal via an information display device. The user can then use this as a reference for cooking.

[0121] Step 7:

[0122] Through visual presentation methods, cooking suggestions are visually displayed on a digital visualization device. Recommended cooking procedures and the reasons behind them are shown, assisting store staff in immediately serving the dishes.

[0123] 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.

[0124] This invention is a system that generates recipes that take into account the user's taste and health information, as well as their emotional state. The system is equipped with an emotion engine as an input means to accept user input, thereby reflecting the emotional aspects that the user seeks in cooking in the recipe generation.

[0125] First, the user uses a dedicated application to input information about the taste of the dish in question, as well as any health restrictions (e.g., sugar or calorie restrictions). Furthermore, because the system is equipped with an emotion engine, the user also provides information about their current emotions and mood. Emotional information is entered by selecting from a set of options, or it is acquired using voice or facial recognition technology.

[0126] The device that receives this information sends the data to the server. The information sent includes all of the following: taste information, health restrictions, and emotional state.

[0127] The server uses natural language processing technology to build a taste profile based on the received data. Furthermore, it analyzes the emotional value required for the recipe based on emotional information. Depending on the user's emotions, it makes adjustments such as adding aromatic ingredients that enhance relaxation or introducing cooking methods that contribute to stress reduction.

[0128] Next, the server references a database of past recipes, searching for similar recipes and optimizing them to be health- and emotionally stimulating. This process ensures that the generated recipes align with the user's taste preferences, health considerations, and current emotions.

[0129] Finally, the generated recipe is sent to the device in digital format. The device displays the recipe in a visually and emotionally easy-to-understand format, making it easy for the user to cook and providing emotional satisfaction during the process.

[0130] As a concrete example, suppose a user requests a meal that will "relax them after a busy day." The system receives the user's emotional information that they "want to relax" and suggests a recipe based on that. For example, it might generate a dessert using herbal tea or a stew using herbs with relaxing effects, allowing the user to have a pleasant and relaxing experience through the meal.

[0131] The following describes the processing flow.

[0132] Step 1:

[0133] Users input information about the taste of nostalgic dishes, health restrictions, and emotions through an app on their device. Emotional information includes stress levels and moods such as wanting to relax, and can be entered using multiple-choice options or voice input.

[0134] Step 2:

[0135] The terminal formats the input information as digital data, which includes taste information, health information, and emotional information. This data is then transmitted to the server in a secure manner.

[0136] Step 3:

[0137] The server analyzes the received data and uses natural language processing technology to construct a taste profile based on the user's taste information. Furthermore, it uses an emotion engine to analyze emotional information and understand the emotional values ​​the user seeks.

[0138] Step 4:

[0139] Based on the taste profile and emotional analysis results, the server searches a database of past recipes to identify recipes with similar tastes. In this process, it prioritizes selecting cooking methods and ingredients that match the user's emotions.

[0140] Step 5:

[0141] The server adjusts the selected recipe based on the user's health information. Specifically, it uses a nutritional assessment model to modify ingredients and adjust seasonings within a healthy range.

[0142] Step 6:

[0143] The server generates the final recipe and organizes it as digital data, taking into account emotional and health values.

[0144] Step 7:

[0145] The server sends the generated recipe data to the terminal. During this process, emotionally resonant food photos and supplementary information regarding the cooking procedure are automatically added.

[0146] Step 8:

[0147] The device displays the received recipe on its user interface. The user can then begin cooking based on this information, and the app also includes a function to record the cooking progress.

[0148] (Example 2)

[0149] 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".

[0150] Conventional recipe generation systems only consider the user's taste and health information, failing to provide recipes that reflect the user's current emotional state. As a result, users were unable to choose meals that suited their mood at the time, sometimes leading to low emotional satisfaction. Furthermore, there is a growing need for meal suggestions that consider both health and emotional value.

[0151] 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.

[0152] In this invention, the server includes means for receiving taste information, health information, and emotional information; means for generating a taste profile; and means for adjusting cooking methods and ingredients. This makes it possible to provide recipes that take into account the user's emotional state and also consider health aspects.

[0153] "Input means" refers to a device or method for receiving taste information, health information, and emotional information from a user.

[0154] "Generation means" refers to an apparatus or technology for generating a taste profile based on received information.

[0155] "Specification method" refers to a device or technology that identifies the optimal cooking method from a past database based on the generated taste profile.

[0156] "Adjustment means" refers to a device or method for appropriately modifying cooking methods and ingredients, taking into account the user's health information and emotional information.

[0157] "Output means" refers to a device or method for providing the user with the generated final recipe data.

[0158] "Natural language processing technology" is a technology that analyzes user input data and allows machines to understand and process human language.

[0159] A "generative AI model" is an artificial intelligence model that learns from large amounts of data and generates new information.

[0160] "Similar recipes" are highly relevant recipes found from a database of past recipes based on the user's taste, health, and emotional information.

[0161] An "evaluation model" is a computational model that evaluates and optimizes data for a specific purpose based on the input information.

[0162] "Emotional information" refers to information that indicates the user's current emotions and psychological state.

[0163] To implement this invention, the user, terminal, and server each play specific roles. The user inputs detailed information about the dish in question using a dedicated application. This information includes taste information, health restriction information, and current emotional information. Emotional information is input either in the form of multiple-choice options or through speech recognition or facial recognition technology.

[0164] The input data is received by the terminal and sent to the server via a communication protocol. The hardware used here primarily consists of communication-capable computer devices (smartphones and tablets). The server, upon receiving the data, analyzes it using natural language processing techniques to generate a user-specific taste profile. This profile is then used, leveraging a generative AI model, to search for similar recipes from a database of past recipes.

[0165] Furthermore, the server takes into account the user's emotional information and makes adjustments that incorporate emotional value. It considers health and adjusts cooking methods and ingredients according to the emotional state. For example, if relaxation is desired, it can provide appropriate herbs and spices.

[0166] The generated optimal recipe is sent from the server to the terminal. The terminal then presents this to the user in a visually easy-to-understand format. Specifically, it displays the ingredient list, cooking instructions, cooking time, and expected emotional effects step by step.

[0167] For example, if a user requests a meal to "calm down after a stressful day," the system will use this emotional information to suggest recipes such as a soup made with relaxing herbal tea or a mild-flavored stew.

[0168] An example of a prompt message could be, "Please suggest a dish that would be suitable for a user who wants to relax." This could be used to instruct the generative AI model.

[0169] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0170] Step 1:

[0171] Users input information about food taste, health restrictions, and emotions through a dedicated application. Specifically, they select their preferred tastes (sweet, spicy, etc.), health restrictions (calories, sugar), and even emotional requests such as "I want to relax" within the app, or provide this information using voice or facial recognition. This input data is sent to the device as a collection of information reflecting the user's current needs.

[0172] Step 2:

[0173] The terminal combines the received user's taste information, health restrictions, and emotional information into a single digital data packet. This data packet serves as the basis for subsequent processing and is structured in a format such as JSON. The terminal then prepares to send this data to the server.

[0174] Step 3:

[0175] The terminal sends structured data packets to the server using a communication protocol (e.g., HTTPS). This transmission process ensures that all the necessary data for processing the user's request is delivered to the server.

[0176] Step 4:

[0177] The server generates a taste profile using natural language processing technology based on the received data. Specifically, it analyzes the taste and emotional information entered by the user and quantifies or categorizes each element. As a result, a taste profile that matches the user's preferences and emotions is generated.

[0178] Step 5:

[0179] The server uses a generative AI model to search a database of past recipes and extract recipes similar to the user's taste profile. During this process, the model is instructed using the prompt, "Suggest a dish that would be suitable if the user wants to relax." This selects recipes that are likely to meet the user's emotional and health requirements.

[0180] Step 6:

[0181] The server optimizes the extracted recipes based on the user's health and emotional information. For example, it might add herbs with relaxing effects or change cooking methods to reduce calories. This generates optimized recipes that are tailored to the user's needs.

[0182] Step 7:

[0183] The server then sends the final optimized recipe data to the terminal in a digital format. This recipe includes an ingredient list, cooking instructions, cooking time, and expected emotional effects.

[0184] Step 8:

[0185] The device displays received recipes in a user-friendly interface. Based on the displayed information, users can easily prepare meals and emotionally enjoy the process. Specifically, it includes visually easy-to-understand step-by-step guides and comments that evoke emotional impact.

[0186] (Application Example 2)

[0187] 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".

[0188] In modern society, users have dietary needs that respond to stress and emotional changes, but existing food delivery services lack the technology to adequately meet these needs. In particular, there is a demand for services that dynamically suggest meals tailored to each user's emotional and health state. However, conventional systems are unable to comprehensively analyze this information and fully reflect users' emotional and health needs.

[0189] 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.

[0190] In this invention, the server includes input means for receiving taste information, health information, and emotional information from a user; generation means for generating a taste profile and an emotional profile based on the taste information and emotional information; and identification means for identifying the optimal cooking method by referring to a past cooking database based on the profiles generated by the generation means. This makes it possible to quickly provide the optimal cooking method according to the user's emotional state and health state.

[0191] A "user" is an entity that uses the system to provide information about taste, health, and emotions.

[0192] "Taste information" refers to information about the user's preferences and desired taste of food.

[0193] "Health information" refers to information about the user's health status and dietary restrictions.

[0194] "Emotional information" refers to information about the user's current emotional state and mood.

[0195] "Input means" refers to an interface that receives taste, health, and emotional information from the user.

[0196] "Generation means" refers to a function that executes the process of creating taste profiles and emotion profiles based on taste information and emotion information.

[0197] A "taste profile" is a model of preferences created based on the taste information provided by the user.

[0198] An "emotional profile" is a psychological state model created based on a user's emotional information.

[0199] "Identification method" refers to the process of referring to the generated profile to identify the optimal cooking method.

[0200] "Adjustment mechanism" refers to a function that adjusts suggested cooking methods and ingredients, taking into account the user's health and emotional information.

[0201] The "output means" is an interface for providing the user with the final generated cooking instructions and recipe data.

[0202] The embodiment for carrying out this invention is a system comprising a user, a terminal, and a server as its main elements. The user inputs taste information, health information, and emotional information using a terminal such as a smartphone. Emotional information is acquired through facial recognition technology using the terminal's camera and voice input. This information is transmitted from the terminal to the server.

[0203] The server constructs taste and emotion profiles using generation methods based on the user's received taste and emotion information. This involves a process in which a generation AI model analyzes the user's preferences using natural language processing technology. Based on the generated profiles, the server identifies optimized cooking methods and makes adjustments according to the user's health and emotion information. For example, considering the emotion information that the user wants to relax, it can suggest dishes with relaxing effects.

[0204] The terminal receives the final cooking instructions sent from the server and presents them to the user in an easy-to-understand manner. The adjusted menu is presented to the user through a visually intuitive interface. Specifically, for a user who wants a relaxing meal at the end of a busy day, a dish using relaxing herbs is suggested.

[0205] An example of a prompt message could be: "The user wants to feel better. Please suggest a healthy and delicious menu that matches this feeling." Based on this prompt message, the system can suggest cooking methods that meet the user's request.

[0206] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0207] Step 1:

[0208] The user inputs taste, health, and emotional information using a device. Emotional information is acquired through a selection method, facial recognition technology using the device's camera, or speech recognition. Once this information is input, the device converts it into data packets and prepares to send them to the server. The input data consists of text information about taste and health constraints, and numerical data indicating emotional states. The output is a data packet ready for transmission.

[0209] Step 2:

[0210] The terminal sends the input taste information, health information, and emotional information to the server as data packets. The transmitted data packets arrive at the server via the internet. The input is the data packets generated in step 1, and the output is the data as it arrives at the server.

[0211] Step 3:

[0212] The server analyzes the received data packets and generates taste and emotion profiles using a generative AI model. The server uses natural language processing techniques to analyze text information and create individual profiles. The input is taste, health, and emotion information received from the user, and the output is a taste and emotion profile specific to each user.

[0213] Step 4:

[0214] The server identifies appropriate cooking methods by referencing a database of past cooking techniques based on the generated taste and emotion profiles. It searches for the most matching cooking method from past data and, if necessary, tunes it to fit the profile. The input is the profile generated in step 3 and the existing recipe database, and the output is the identified cooking method and ingredient information.

[0215] Step 5:

[0216] The server adjusts the identified cooking methods and ingredients as needed, taking into account the user's health information and emotional state. It creates the final cooking instructions by making nutritional or psychological adjustments to the suggested recipe based on the user's health restrictions and emotional state. The input is the cooking methods identified in step 4 and the user's health information, and the output is the adjusted final recipe information.

[0217] Step 6:

[0218] The server sends the final recipe information to the terminal. The terminal receives this information and displays it in a user-friendly format. Cooking details are presented through a user interface that provides visual information and emotional feedback. The input is the recipe information adjusted in step 5, and the output is the cooking instructions displayed on the terminal screen.

[0219] 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.

[0220] 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.

[0221] 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.

[0222] [Second Embodiment]

[0223] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0224] 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.

[0225] 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).

[0226] 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.

[0227] 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.

[0228] 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).

[0229] 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.

[0230] 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.

[0231] 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.

[0232] 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.

[0233] 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.

[0234] 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".

[0235] This invention is a system that starts with the user inputting specific taste and health information, and then uses AI technology to generate an optimal recipe based on that information. Specifically, it is carried out in the following steps.

[0236] First, users use a smartphone or computer to input information about nostalgic dishes and flavors they remember. This information includes specific dish names, flavor characteristics (e.g., "sweet and soy sauce flavor"), ingredients used, and cooking methods. Users also input information about their health, such as dietary restrictions related to high blood pressure or diabetes.

[0237] Next, the terminal sends the data entered by the user to the server. During this transmission process, the data is encrypted and sent in a secure manner. In particular, metadata such as the user ID and time are also transmitted simultaneously to ensure the integrity and consistency of the data.

[0238] The server uses the received data to generate a taste profile through an AI algorithm. This profile analyzes text data using natural language processing to extract the essential elements of the taste the user desires. Next, this profile is used to search a database for past recipes with similar taste characteristics and select the optimal cooking method and ingredients.

[0239] Furthermore, the server generates nutritionally balanced recipes based on the user's health information. Here, an AI-designed evaluation model is used to adjust the appropriate quantities of ingredients and seasonings. This process ensures that users receive recipes that are both health-conscious and satisfying in taste.

[0240] Finally, the server sends the final determined recipe to the terminal in digital format. The terminal displays this information on its user interface, allowing the user to cook the dish accordingly. It may also include visual support and step-by-step instructions, making cooking easier.

[0241] As a concrete example, consider a case where a user wants to recreate the taste of "meat and potato stew that their mother used to make." In this case, the user inputs "sweet and salty soy sauce flavor" as a characteristic of the dish. Based on this information, the server searches for recipe data with a similar taste profile, and further takes into account that the user needs to be mindful of high blood pressure, generating a recipe that is satisfying while being low in salt. In this way, the user can enjoy the desired taste safely and healthily.

[0242] The following describes the processing flow.

[0243] Step 1:

[0244] Users launch a dedicated app and enter taste information about their favorite dishes, as well as personal health information. Specifically, they enter the dish name, flavor characteristics, ingredients used, cooking method, and health restrictions (e.g., salt restriction).

[0245] Step 2:

[0246] The terminal formats the information entered by the user into the appropriate format and sends it to the server. The transmitted data includes not only the user's input but also the user ID and transmission time.

[0247] Step 3:

[0248] The server analyzes the received user information and uses natural language processing techniques to extract important keywords from the input text. This analysis generates a taste profile that clearly understands the user's requests.

[0249] Step 4:

[0250] The server searches a recipe database based on the generated taste profile and identifies past recipes with similar taste characteristics. This process utilizes pattern matching and clustering techniques.

[0251] Step 5:

[0252] The server adjusts the identified recipe, taking into account the user's health information. An AI model appropriately modifies the quantity and type of ingredients and the proportion of seasonings to optimize the nutritional balance. This step utilizes nutritional models and health restriction models.

[0253] Step 6:

[0254] The server finalizes the adjusted recipe and creates recipe data in a user-friendly format. The recipe includes an ingredient list, cooking instructions, preparation time, and equipment information.

[0255] Step 7:

[0256] The server sends the completed recipe to the terminal. The transmission method is optimized for the user interface, and the system is designed to allow for easy and convenient display of the recipe.

[0257] Step 8:

[0258] The device displays the received recipe on the user interface, allowing the user to begin cooking. The display includes visual support and other features to make the cooking process intuitively understandable.

[0259] (Example 1)

[0260] 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."

[0261] In today's world, providing recipes tailored to the diverse taste preferences and health conditions of individual users is a challenging task. Especially when there are specific health constraints, achieving a satisfying taste while meeting those conditions requires specialized knowledge, placing a significant burden on the average person. Furthermore, even when there is a desire to recreate nostalgic flavors, selecting effective cooking methods and ingredients can be difficult. Therefore, there is a need for a method that can generate appropriate recipes while considering individual preferences and health information.

[0262] 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.

[0263] In this invention, the server includes data input means, profile generation means, processing means, meal adjustment means, and information output means. This makes it possible for users to recreate nostalgic flavors while being provided with nutritionally balanced recipes that take health into consideration.

[0264] "Data input means" refers to a function for collecting taste and health information from users and incorporating it into the system.

[0265] The "profile generation means" is a function that models the characteristics of taste based on the taste information provided by the user and creates a taste profile.

[0266] The "processing means" refers to a function that references the generated taste profile and identifies the optimal cooking method from a database of past cooking information.

[0267] A "meal adjustment tool" is a function that adjusts cooking methods and ingredients based on the user's health information to create a meal suitable for their health.

[0268] The "information output means" refers to a function for providing the user with the final generated meal data.

[0269] This invention is a system that begins with the user inputting specific taste and health information. The user uses a device such as a smartphone or personal computer to input detailed information about nostalgic dishes and their tastes. This input information includes specific dish names and taste characteristics (e.g., "sweet and soy sauce flavor"), ingredients used, cooking methods, and even health information (e.g., information on dietary restrictions such as high blood pressure or diabetes).

[0270] Information entered by the user is transmitted to the server via the terminal. The terminal encrypts the data and transmits it using a secure protocol (e.g., HTTPS). Based on the received information, the server analyzes the user's taste data using natural language processing technology and extracts its essential elements. The taste profile generated based on the analysis results is created using a profile generation method that utilizes AI technology.

[0271] The server has a processing mechanism that refers to this taste profile and identifies the optimal cooking method from a database of past cooking information. It also includes a meal adjustment mechanism that adjusts cooking methods and ingredients considering the user's health information. This generates final meal data with adjusted nutritional balance.

[0272] The generated recipe is sent from the server to the terminal, which displays it in the user interface. The user can then cook the dish according to this, and the recipe may include visual support and step-by-step instructions, making cooking easier.

[0273] A concrete example is when a user wants to recreate the taste of "meat and potato stew that their mother used to make." In this case, the user enters "sweet and salty soy sauce flavor" as a characteristic. Based on this information, the server searches for recipe data with a similar taste profile and then provides a low-sodium recipe that takes high blood pressure into consideration.

[0274] An example of a prompt message might be, "I want to recreate the sweet soy sauce-flavored nikujaga (meat and potato stew) my mother used to make. I have high blood pressure, so I'd like a low-sodium recipe." Such a system allows users to enjoy dishes that suit their preferences while also being mindful of their health.

[0275] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0276] Step 1:

[0277] The user uses a terminal such as a smartphone or a personal computer to input information about nostalgic dishes and their flavors. This input information includes specific dish names, taste characteristics (e.g., "sweetness and soy sauce flavor"), ingredients used, cooking methods, and health information (e.g., high blood pressure or diabetes). The input data is structured by the terminal and handled in an organized format. As output, a data package encoded on the terminal side and ready for transmission is generated.

[0278] Step 2:

[0279] The terminal encrypts the data package input by the user and sends it to the server using a secure communication protocol (e.g., HTTPS). The input includes the encoded data package from the terminal. The data is sent in a batch as a block, and encryption processing is performed to maintain security on the network. As output, highly secure data received by the server is obtained.

[0280] Step 3:

[0281] The server decodes the received data and analyzes the user's taste information and health information respectively. The input is the decrypted user data. The server uses natural language processing technology to analyze the received text data and extract the taste characteristics required by the user. After data analysis, a taste profile is generated through AI technology. As output, a taste profile is generated and passed on to the next process.

[0282] Step 4:

[0283] The server uses the generated taste profile to search for the optimal cooking method from the past cooking information database. The input is the generated taste profile. An AI algorithm searches for and selects similar recipes based on this profile. As output, a list of the optimal cooking method and ingredients is obtained.

[0284] Step 5:

[0285] The server takes into account the user's health information and adjusts the selected cooking method and ingredients. The input is a list of cooking methods and ingredients and the user's health profile. The server uses an AI evaluation model to readjust the quantity of ingredients considering the nutritional balance. As output, a health-conscious recipe is generated.

[0286] Step 6:

[0287] The final recipe data is sent from the server to the terminal. As input, health-conscious recipe information is sent from the server. The terminal displays the received recipe through the user interface. The user can proceed with cooking according to the presented recipe while receiving visual support and step-by-step instructions. The output is the completed recipe information displayed on the terminal.

[0288] (Application Example 1)

[0289] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0290] Modern individual customers want to enjoy meals that suit their respective preferences while having various health-related constraints. However, in general restaurants, it is currently difficult to provide customized dishes according to the detailed taste and health information of each customer. Also, menu suggestions by store staff are not made immediately, and improving customer satisfaction is an issue.

[0291] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following respective means.

[0292] In this invention, the server includes data input means for receiving taste information and health information from the user, profile generation means for generating a taste profile based on the taste information, and evaluation means for identifying the optimal cooking method by referring to a past food database. This enables the generation of customized recipes that take into account the taste and health of individual customers, and rapid menu suggestions using a digital visualization device.

[0293] "Data input means" refers to methods for obtaining taste information and health information from users.

[0294] The "profile generation means" is a function that creates an optimal taste profile for each individual user based on the acquired taste information.

[0295] The "evaluation method" is a method that identifies the optimal cooking method by referring to a previously recorded food database based on the generated taste profile.

[0296] "Nutritional adjustment means" refers to a function that optimizes the selected cooking method and the nutrients of ingredients, taking into account the health information provided by the user.

[0297] "Information presentation means" refers to a method of displaying the final generated dish composition data on the user's terminal and making suggestions to the user.

[0298] "Visual presentation means" refers to the function of visually displaying the proposed dishes using digital visualization devices in the store.

[0299] "Natural language processing technology" is a technology that analyzes information from users and extracts necessary information from text data.

[0300] A "computational model" is an algorithm used to optimize nutrients based on a user's health information.

[0301] This invention is a system that enables restaurants to propose customized dishes based on individual user preferences and health conditions. By using a server, a terminal, and a digital visualization device, an effective cooking proposal is realized.

[0302] The server receives taste information and health information from the user via data input means. This information is transmitted from the user's smart glasses or mobile terminal. The data is encrypted and transmitted to the server in a secure communication method. The server generates a taste profile using the taste information by profile generation means. Then, based on the generated profile, the evaluation means refers to the food database to identify the optimal cooking method and ingredients for the user.

[0303] This cooking method and ingredient information are further optimized based on the user's health information by nutritional adjustment means. The adjusted dish composition data is provided to the user terminal by information presentation means. The digital visualization device makes a visual proposal of the dish as a visual presentation means and displays the steps and recommended reasons for cooking in real time.

[0304] As a specific example, when the user inputs a prompt sentence such as "I want a sweet and rich Japanese curry and want to keep the calories low", the system instantly identifies the recipe and cooking procedure of the curry according to the request and displays it on the digital visualization device. Thereby, the store staff can quickly prepare and provide the dish.

[0305] The generated AI model optimizes the recipe proposal based on the user's input and adjusts the specific ingredients and procedures using a calculation model. For this purpose, a cloud server, smart glasses or a mobile terminal is used as hardware, and a natural language processing library is used as software.

[0306] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0307] Step 1:

[0308] Users input taste and health information via smart glasses or mobile devices. This input includes prompts regarding specific taste characteristics and health restrictions. The information is encrypted and transmitted to the server using a secure communication protocol.

[0309] Step 2:

[0310] The server receives the received information through a data input mechanism and analyzes it using natural language processing technology. This extracts necessary taste and health-related elements from the text data. The extracted data is then used as material for generating taste profiles.

[0311] Step 3:

[0312] The server's profile generation mechanism generates a taste profile based on the extracted data. The taste profile represents a specific taste pattern that reflects the user's preferences. This profile is used in the next step.

[0313] Step 4:

[0314] The server uses evaluation tools to search a food database based on taste profiles and identify the optimal cooking method and ingredients. This process is performed by a database matching algorithm, which selects the most suitable recipe from among many.

[0315] Step 5:

[0316] The identified cooking methods and ingredients are adjusted to the user's health information using nutritional adjustment tools. This adjustment utilizes a computational model that optimizes nutrients. After this, the final dish composition data is generated.

[0317] Step 6:

[0318] The server transmits the generated dish composition data to the user's terminal via an information display device. The user can then use this as a reference for cooking.

[0319] Step 7:

[0320] Through visual presentation methods, cooking suggestions are visually displayed on a digital visualization device. Recommended cooking procedures and the reasons behind them are shown, assisting store staff in immediately serving the dishes.

[0321] 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.

[0322] This invention is a system that generates recipes that take into account the user's taste and health information, as well as their emotional state. The system is equipped with an emotion engine as an input means to accept user input, thereby reflecting the emotional aspects that the user seeks in cooking in the recipe generation.

[0323] First, the user uses a dedicated application to input information about the taste of the dish in question, as well as any health restrictions (e.g., sugar or calorie restrictions). Furthermore, because the system is equipped with an emotion engine, the user also provides information about their current emotions and mood. Emotional information is entered by selecting from a set of options, or it is acquired using voice or facial recognition technology.

[0324] The device that receives this information sends the data to the server. The information sent includes all of the following: taste information, health restrictions, and emotional state.

[0325] The server uses natural language processing technology to build a taste profile based on the received data. Furthermore, it analyzes the emotional value required for the recipe based on emotional information. Depending on the user's emotions, it makes adjustments such as adding aromatic ingredients that enhance relaxation or introducing cooking methods that contribute to stress reduction.

[0326] Next, the server references a database of past recipes, searching for similar recipes and optimizing them to be health- and emotionally stimulating. This process ensures that the generated recipes align with the user's taste preferences, health considerations, and current emotions.

[0327] Finally, the generated recipe is sent to the device in digital format. The device displays the recipe in a visually and emotionally easy-to-understand format, making it easy for the user to cook and providing emotional satisfaction during the process.

[0328] As a concrete example, suppose a user requests a meal that will "relax them after a busy day." The system receives the user's emotional information that they "want to relax" and suggests a recipe based on that. For example, it might generate a dessert using herbal tea or a stew using herbs with relaxing effects, allowing the user to have a pleasant and relaxing experience through the meal.

[0329] The following describes the processing flow.

[0330] Step 1:

[0331] Users input information about the taste of nostalgic dishes, health restrictions, and emotions through an app on their device. Emotional information includes stress levels and moods such as wanting to relax, and can be entered using multiple-choice options or voice input.

[0332] Step 2:

[0333] The terminal formats the input information as digital data, which includes taste information, health information, and emotional information. This data is then transmitted to the server in a secure manner.

[0334] Step 3:

[0335] The server analyzes the received data and uses natural language processing technology to construct a taste profile based on the user's taste information. Furthermore, it uses an emotion engine to analyze emotional information and understand the emotional values ​​the user seeks.

[0336] Step 4:

[0337] Based on the taste profile and emotional analysis results, the server searches a database of past recipes to identify recipes with similar tastes. In this process, it prioritizes selecting cooking methods and ingredients that match the user's emotions.

[0338] Step 5:

[0339] The server adjusts the selected recipe based on the user's health information. Specifically, it uses a nutritional assessment model to modify ingredients and adjust seasonings within a healthy range.

[0340] Step 6:

[0341] The server generates the final recipe and organizes it as digital data, taking into account emotional and health values.

[0342] Step 7:

[0343] The server sends the generated recipe data to the terminal. During this process, emotionally resonant food photos and supplementary information regarding the cooking procedure are automatically added.

[0344] Step 8:

[0345] The device displays the received recipe on its user interface. The user can then begin cooking based on this information, and the app also includes a function to record the cooking progress.

[0346] (Example 2)

[0347] 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".

[0348] Conventional recipe generation systems only consider the user's taste and health information, failing to provide recipes that reflect the user's current emotional state. As a result, users were unable to choose meals that suited their mood at the time, sometimes leading to low emotional satisfaction. Furthermore, there is a growing need for meal suggestions that consider both health and emotional value.

[0349] 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.

[0350] In this invention, the server includes means for receiving taste information, health information, and emotional information; means for generating a taste profile; and means for adjusting cooking methods and ingredients. This makes it possible to provide recipes that take into account the user's emotional state and also consider health aspects.

[0351] "Input means" refers to a device or method for receiving taste information, health information, and emotional information from a user.

[0352] "Generation means" refers to an apparatus or technology for generating a taste profile based on received information.

[0353] "Specification method" refers to a device or technology that identifies the optimal cooking method from a past database based on the generated taste profile.

[0354] "Adjustment means" refers to a device or method for appropriately modifying cooking methods and ingredients, taking into account the user's health information and emotional information.

[0355] "Output means" refers to a device or method for providing the user with the generated final recipe data.

[0356] "Natural language processing technology" is a technology that analyzes user input data and allows machines to understand and process human language.

[0357] A "generative AI model" is an artificial intelligence model that learns from large amounts of data and generates new information.

[0358] "Similar recipes" are highly relevant recipes found from a database of past recipes based on the user's taste, health, and emotional information.

[0359] An "evaluation model" is a computational model that evaluates and optimizes data for a specific purpose based on the input information.

[0360] "Emotional information" refers to information that indicates the user's current emotions and psychological state.

[0361] To implement this invention, the user, terminal, and server each play specific roles. The user inputs detailed information about the dish in question using a dedicated application. This information includes taste information, health restriction information, and current emotional information. Emotional information is input either in the form of multiple-choice options or through speech recognition or facial recognition technology.

[0362] The input data is received by the terminal and sent to the server via a communication protocol. The hardware used here primarily consists of communication-capable computer devices (smartphones and tablets). The server, upon receiving the data, analyzes it using natural language processing techniques to generate a user-specific taste profile. This profile is then used, leveraging a generative AI model, to search for similar recipes from a database of past recipes.

[0363] Furthermore, the server takes into account the user's emotional information and makes adjustments that incorporate emotional value. It considers health and adjusts cooking methods and ingredients according to the emotional state. For example, if relaxation is desired, it can provide appropriate herbs and spices.

[0364] The generated optimal recipe is sent from the server to the terminal. The terminal then presents this to the user in a visually easy-to-understand format. Specifically, it displays the ingredient list, cooking instructions, cooking time, and expected emotional effects step by step.

[0365] For example, if a user requests a meal to "calm down after a stressful day," the system will use this emotional information to suggest recipes such as a soup made with relaxing herbal tea or a mild-flavored stew.

[0366] An example of a prompt message could be, "Please suggest a dish that would be suitable for a user who wants to relax." This could be used to instruct the generative AI model.

[0367] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0368] Step 1:

[0369] Users input information about food taste, health restrictions, and emotions through a dedicated application. Specifically, they select their preferred tastes (sweet, spicy, etc.), health restrictions (calories, sugar), and even emotional requests such as "I want to relax" within the app, or provide this information using voice or facial recognition. This input data is sent to the device as a collection of information reflecting the user's current needs.

[0370] Step 2:

[0371] The terminal combines the received user's taste information, health restrictions, and emotional information into a single digital data packet. This data packet serves as the basis for subsequent processing and is structured in a format such as JSON. The terminal then prepares to send this data to the server.

[0372] Step 3:

[0373] The terminal sends structured data packets to the server using a communication protocol (e.g., HTTPS). This transmission process ensures that all the necessary data for processing the user's request is delivered to the server.

[0374] Step 4:

[0375] The server generates a taste profile using natural language processing technology based on the received data. Specifically, it analyzes the taste and emotional information entered by the user and quantifies or categorizes each element. As a result, a taste profile that matches the user's preferences and emotions is generated.

[0376] Step 5:

[0377] The server uses a generative AI model to search a database of past recipes and extract recipes similar to the user's taste profile. During this process, the model is instructed using the prompt, "Suggest a dish that would be suitable if the user wants to relax." This selects recipes that are likely to meet the user's emotional and health requirements.

[0378] Step 6:

[0379] The server optimizes the extracted recipes based on the user's health and emotional information. For example, it might add herbs with relaxing effects or change cooking methods to reduce calories. This generates optimized recipes that are tailored to the user's needs.

[0380] Step 7:

[0381] The server then sends the final optimized recipe data to the terminal in a digital format. This recipe includes an ingredient list, cooking instructions, cooking time, and expected emotional effects.

[0382] Step 8:

[0383] The device displays received recipes in a user-friendly interface. Based on the displayed information, users can easily prepare meals and emotionally enjoy the process. Specifically, it includes visually easy-to-understand step-by-step guides and comments that evoke emotional impact.

[0384] (Application Example 2)

[0385] 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."

[0386] In modern society, users have dietary needs that respond to stress and emotional changes, but existing food delivery services lack the technology to adequately meet these needs. In particular, there is a demand for services that dynamically suggest meals tailored to each user's emotional and health state. However, conventional systems are unable to comprehensively analyze this information and fully reflect users' emotional and health needs.

[0387] 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.

[0388] In this invention, the server includes input means for receiving taste information, health information, and emotional information from a user; generation means for generating a taste profile and an emotional profile based on the taste information and emotional information; and identification means for identifying the optimal cooking method by referring to a past cooking database based on the profiles generated by the generation means. This makes it possible to quickly provide the optimal cooking method according to the user's emotional state and health state.

[0389] A "user" is an entity that uses the system to provide information about taste, health, and emotions.

[0390] "Taste information" refers to information about the user's preferences and desired taste of food.

[0391] "Health information" refers to information about the user's health status and dietary restrictions.

[0392] "Emotional information" refers to information about the user's current emotional state and mood.

[0393] "Input means" refers to an interface that receives taste, health, and emotional information from the user.

[0394] "Generation means" refers to a function that executes the process of creating taste profiles and emotion profiles based on taste information and emotion information.

[0395] A "taste profile" is a model of preferences created based on the taste information provided by the user.

[0396] An "emotional profile" is a psychological state model created based on a user's emotional information.

[0397] "Identification method" refers to the process of referring to the generated profile to identify the optimal cooking method.

[0398] "Adjustment mechanism" refers to a function that adjusts suggested cooking methods and ingredients, taking into account the user's health and emotional information.

[0399] The "output means" is an interface for providing the user with the final generated cooking instructions and recipe data.

[0400] The embodiment for carrying out this invention is a system comprising a user, a terminal, and a server as its main elements. The user inputs taste information, health information, and emotional information using a terminal such as a smartphone. Emotional information is acquired through facial recognition technology using the terminal's camera and voice input. This information is transmitted from the terminal to the server.

[0401] The server constructs taste and emotion profiles using generation methods based on the user's received taste and emotion information. This involves a process in which a generation AI model analyzes the user's preferences using natural language processing technology. Based on the generated profiles, the server identifies optimized cooking methods and makes adjustments according to the user's health and emotion information. For example, considering the emotion information that the user wants to relax, it can suggest dishes with relaxing effects.

[0402] The terminal receives the final cooking instructions sent from the server and presents them to the user in an easy-to-understand manner. The adjusted menu is presented to the user through a visually intuitive interface. Specifically, for a user who wants a relaxing meal at the end of a busy day, a dish using relaxing herbs is suggested.

[0403] An example of a prompt message could be: "The user wants to feel better. Please suggest a healthy and delicious menu that reflects this feeling." Based on this prompt message, the system can suggest cooking methods that meet the user's request.

[0404] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0405] Step 1:

[0406] The user inputs taste, health, and emotional information using a device. Emotional information is acquired through a selection method, facial recognition technology using the device's camera, or speech recognition. Once this information is input, the device converts it into data packets and prepares to send them to the server. The input data consists of text information about taste and health constraints, and numerical data indicating emotional states. The output is a data packet ready for transmission.

[0407] Step 2:

[0408] The terminal sends the input taste information, health information, and emotional information to the server as data packets. The transmitted data packets arrive at the server via the internet. The input is the data packets generated in step 1, and the output is the data as it arrives at the server.

[0409] Step 3:

[0410] The server analyzes the received data packets and generates taste and emotion profiles using a generative AI model. The server uses natural language processing techniques to analyze text information and create individual profiles. The input is taste, health, and emotion information received from the user, and the output is a taste and emotion profile specific to each user.

[0411] Step 4:

[0412] The server identifies appropriate cooking methods by referencing a database of past cooking techniques based on the generated taste and emotion profiles. It searches for the most matching cooking method from past data and, if necessary, tunes it to fit the profile. The input is the profile generated in step 3 and the existing recipe database, and the output is the identified cooking method and ingredient information.

[0413] Step 5:

[0414] The server adjusts the identified cooking methods and ingredients as needed, taking into account the user's health information and emotional state. It creates the final cooking instructions by making nutritional or psychological adjustments to the suggested recipe based on the user's health restrictions and emotional state. The input is the cooking methods identified in step 4 and the user's health information, and the output is the adjusted final recipe information.

[0415] Step 6:

[0416] The server sends the final recipe information to the terminal. The terminal receives this information and displays it in a user-friendly format. Cooking details are presented through a user interface that provides visual information and emotional feedback. The input is the recipe information adjusted in step 5, and the output is the cooking instructions displayed on the terminal screen.

[0417] 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.

[0418] 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.

[0419] 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.

[0420] [Third Embodiment]

[0421] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0422] 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.

[0423] 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).

[0424] 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.

[0425] 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.

[0426] 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).

[0427] 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.

[0428] 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.

[0429] 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.

[0430] 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.

[0431] 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.

[0432] 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".

[0433] This invention is a system that starts with the user inputting specific taste and health information, and then uses AI technology to generate an optimal recipe based on that information. Specifically, it is carried out in the following steps.

[0434] First, users use a smartphone or computer to input information about nostalgic dishes and flavors they remember. This information includes specific dish names, flavor characteristics (e.g., "sweet and soy sauce flavor"), ingredients used, and cooking methods. Users also input information about their health, such as dietary restrictions related to high blood pressure or diabetes.

[0435] Next, the terminal sends the data entered by the user to the server. During this transmission process, the data is encrypted and sent in a secure manner. In particular, metadata such as the user ID and time are also transmitted simultaneously to ensure the integrity and consistency of the data.

[0436] The server uses the received data to generate a taste profile through an AI algorithm. This profile analyzes text data using natural language processing to extract the essential elements of the taste the user desires. Next, this profile is used to search a database for past recipes with similar taste characteristics and select the optimal cooking method and ingredients.

[0437] Furthermore, the server generates nutritionally balanced recipes based on the user's health information. Here, an AI-designed evaluation model is used to adjust the appropriate quantities of ingredients and seasonings. This process ensures that users receive recipes that are both health-conscious and satisfying in taste.

[0438] Finally, the server sends the final determined recipe to the terminal in digital format. The terminal displays this information on its user interface, allowing the user to cook the dish accordingly. It may also include visual support and step-by-step instructions, making cooking easier.

[0439] As a concrete example, consider a case where a user wants to recreate the taste of "meat and potato stew that their mother used to make." In this case, the user inputs "sweet and salty soy sauce flavor" as a characteristic of the dish. Based on this information, the server searches for recipe data with a similar taste profile, and further takes into account that the user needs to be mindful of high blood pressure, generating a recipe that is satisfying while being low in salt. In this way, the user can enjoy the desired taste safely and healthily.

[0440] The following describes the processing flow.

[0441] Step 1:

[0442] Users launch a dedicated app and enter taste information about their favorite dishes, as well as personal health information. Specifically, they enter the dish name, flavor characteristics, ingredients used, cooking method, and health restrictions (e.g., salt restriction).

[0443] Step 2:

[0444] The terminal formats the information entered by the user into the appropriate format and sends it to the server. The transmitted data includes not only the user's input but also the user ID and transmission time.

[0445] Step 3:

[0446] The server analyzes the received user information and uses natural language processing techniques to extract important keywords from the input text. This analysis generates a taste profile that clearly understands the user's requests.

[0447] Step 4:

[0448] The server searches a recipe database based on the generated taste profile and identifies past recipes with similar taste characteristics. This process utilizes pattern matching and clustering techniques.

[0449] Step 5:

[0450] The server adjusts the identified recipe, taking into account the user's health information. An AI model appropriately modifies the quantity and type of ingredients and the proportion of seasonings to optimize the nutritional balance. This step utilizes nutritional models and health restriction models.

[0451] Step 6:

[0452] The server finalizes the adjusted recipe and creates recipe data in a user-friendly format. The recipe includes an ingredient list, cooking instructions, preparation time, and equipment information.

[0453] Step 7:

[0454] The server sends the completed recipe to the terminal. The transmission method is optimized for the user interface, and the system is designed to allow for easy and convenient display of the recipe.

[0455] Step 8:

[0456] The device displays the received recipe on the user interface, allowing the user to begin cooking. The display includes visual support and other features to make the cooking process intuitively understandable.

[0457] (Example 1)

[0458] 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."

[0459] In today's world, providing recipes tailored to the diverse taste preferences and health conditions of individual users is a challenging task. Especially when there are specific health constraints, achieving a satisfying taste while meeting those conditions requires specialized knowledge, placing a significant burden on the average person. Furthermore, even when there is a desire to recreate nostalgic flavors, selecting effective cooking methods and ingredients can be difficult. Therefore, there is a need for a method that can generate appropriate recipes while considering individual preferences and health information.

[0460] 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.

[0461] In this invention, the server includes data input means, profile generation means, processing means, meal adjustment means, and information output means. This makes it possible for users to recreate nostalgic flavors while being provided with nutritionally balanced recipes that take health into consideration.

[0462] "Data input means" refers to a function for collecting taste and health information from users and incorporating it into the system.

[0463] The "profile generation means" is a function that models the characteristics of taste based on the taste information provided by the user and creates a taste profile.

[0464] The "processing means" refers to a function that references the generated taste profile and identifies the optimal cooking method from a database of past cooking information.

[0465] A "meal adjustment tool" is a function that adjusts cooking methods and ingredients based on the user's health information to create a meal suitable for their health.

[0466] The "information output means" refers to a function for providing the user with the final generated meal data.

[0467] This invention is a system that begins with the user inputting specific taste and health information. The user uses a device such as a smartphone or personal computer to input detailed information about nostalgic dishes and their tastes. This input information includes specific dish names and taste characteristics (e.g., "sweet and soy sauce flavor"), ingredients used, cooking methods, and even health information (e.g., information on dietary restrictions such as high blood pressure or diabetes).

[0468] Information entered by the user is transmitted to the server via the terminal. The terminal encrypts the data and transmits it using a secure protocol (e.g., HTTPS). Based on the received information, the server analyzes the user's taste data using natural language processing technology and extracts its essential elements. The taste profile generated based on the analysis results is created using a profile generation method that utilizes AI technology.

[0469] The server has a processing mechanism that refers to this taste profile and identifies the optimal cooking method from a database of past cooking information. It also includes a meal adjustment mechanism that adjusts cooking methods and ingredients considering the user's health information. This generates final meal data with adjusted nutritional balance.

[0470] The generated recipe is sent from the server to the terminal, which displays it in the user interface. The user can then cook the dish according to this, and the recipe may include visual support and step-by-step instructions, making cooking easier.

[0471] A concrete example is when a user wants to recreate the taste of "meat and potato stew that their mother used to make." In this case, the user enters "sweet and salty soy sauce flavor" as a characteristic. Based on this information, the server searches for recipe data with a similar taste profile and then provides a low-sodium recipe that takes high blood pressure into consideration.

[0472] An example of a prompt message might be, "I want to recreate the sweet soy sauce-flavored nikujaga (meat and potato stew) my mother used to make. I have high blood pressure, so I'd like a low-sodium recipe." Such a system allows users to enjoy dishes that suit their preferences while also being mindful of their health.

[0473] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0474] Step 1:

[0475] Users input information about nostalgic dishes and their flavors using devices such as smartphones and personal computers. This input includes specific dish names, flavor characteristics (e.g., "sweetness and soy sauce flavor"), ingredients used, cooking methods, and health information (e.g., high blood pressure or diabetes). The input data is structured and handled in an organized format by the device. As output, a data package is generated on the device, encoded and ready for transmission.

[0476] Step 2:

[0477] The terminal encrypts the data package entered by the user and sends it to the server using a secure communication protocol (e.g., HTTPS). The input includes encoded data packages from the terminal. The data is sent in chunks and encrypted to maintain network security. The output is the highly secure data received by the server.

[0478] Step 3:

[0479] The server decodes the received data and analyzes the user's taste and health information. The input is decrypted user data. The server uses natural language processing technology to analyze the received text data and extract the taste characteristics desired by the user. After data analysis, a taste profile is generated through AI technology. The taste profile is generated as output and is then passed on to the next processing step.

[0480] Step 4:

[0481] The server uses the generated taste profile to search for the optimal cooking method from a database of past cooking information. The input is the generated taste profile. The AI ​​algorithm uses this profile to search for and select similar recipes. The output is a list of the optimal cooking methods and ingredients.

[0482] Step 5:

[0483] The server adjusts the selected cooking method and ingredients based on the user's health information. The input consists of a list of cooking methods and ingredients, and the user's health profile. The server utilizes an AI evaluation model to readjust ingredient quantities, considering nutritional balance. The output is a health-conscious recipe.

[0484] Step 6:

[0485] The final recipe data is sent from the server to the terminal. The input is health-conscious recipe information sent from the server. The terminal displays the received recipe through a user interface. The user can proceed with cooking according to the presented recipe, receiving visual support and step-by-step instructions. The output is the completed recipe information displayed on the terminal.

[0486] (Application Example 1)

[0487] 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."

[0488] Today's individual customers, despite various health constraints, want to enjoy meals that suit their individual preferences. However, in reality, it is difficult for typical restaurants to provide customized dishes that take into account each customer's detailed taste preferences and health information. Furthermore, the lack of immediate menu suggestions from restaurant staff makes improving customer satisfaction a challenge.

[0489] 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.

[0490] In this invention, the server includes data input means for receiving taste information and health information from the user, profile generation means for generating a taste profile based on the taste information, and evaluation means for identifying the optimal cooking method by referring to a past food database. This enables the generation of customized recipes that take into account the taste and health of individual customers, and rapid menu suggestions using a digital visualization device.

[0491] "Data input means" refers to methods for obtaining taste information and health information from users.

[0492] The "profile generation means" is a function that creates an optimal taste profile for each individual user based on the acquired taste information.

[0493] The "evaluation method" is a method that identifies the optimal cooking method by referring to a previously recorded food database based on the generated taste profile.

[0494] "Nutritional adjustment means" refers to a function that optimizes the selected cooking method and the nutrients of ingredients, taking into account the health information provided by the user.

[0495] "Information presentation means" refers to a method of displaying the final generated dish composition data on the user's terminal and making suggestions to the user.

[0496] "Visual presentation means" refers to the function of visually displaying the proposed dishes using digital visualization devices in the store.

[0497] "Natural language processing technology" is a technology that analyzes information from users and extracts necessary information from text data.

[0498] A "computational model" is an algorithm used to optimize nutrients based on a user's health information.

[0499] This invention is a system that enables restaurants to suggest customized dishes based on the individual preferences and health conditions of their customers. By using a server, terminals, and a digital visualization device, it achieves effective cooking suggestions.

[0500] The server receives taste and health information from the user via a data input device. This information is transmitted from the user's smart glasses or mobile device. The data is encrypted and transmitted to the server using a secure communication method. The server uses the taste information to generate a taste profile using a profile generation device. Then, using an evaluation device, it refers to a food database based on the generated profile to identify the optimal cooking method and ingredients for the user.

[0501] This cooking method and ingredient information is further optimized based on the user's health information using a nutritional adjustment mechanism. The adjusted dish composition data is provided to the user terminal by an information presentation mechanism. A digital visualization device provides a visual suggestion of the dish as a visual presentation mechanism, displaying the steps and reasons for recommending the cooking in real time.

[0502] For example, if a user enters a prompt such as, "I would like a sweet and rich Japanese-style curry, but I want it to be low in calories," the system instantly identifies the curry recipe and cooking procedure that meets the request and displays it on a digital display. This allows store staff to quickly prepare and serve the dish.

[0503] The generative AI model optimizes recipe suggestions based on user input, adjusting specific ingredients and procedures using a computational model. To achieve this, it utilizes a cloud server, smart glasses, or mobile device as hardware, and a natural language processing library as software.

[0504] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0505] Step 1:

[0506] Users input taste and health information via smart glasses or mobile devices. This input includes prompts regarding specific taste characteristics and health restrictions. The information is encrypted and transmitted to the server using a secure communication protocol.

[0507] Step 2:

[0508] The server receives the received information through a data input mechanism and analyzes it using natural language processing technology. This extracts necessary taste and health-related elements from the text data. The extracted data is then used as material for generating taste profiles.

[0509] Step 3:

[0510] The server's profile generation mechanism generates a taste profile based on the extracted data. The taste profile represents a specific taste pattern that reflects the user's preferences. This profile is used in the next step.

[0511] Step 4:

[0512] The server uses evaluation tools to search a food database based on taste profiles and identify the optimal cooking method and ingredients. This process is performed by a database matching algorithm, which selects the most suitable recipe from among many.

[0513] Step 5:

[0514] The identified cooking methods and ingredients are adjusted to the user's health information using nutritional adjustment tools. This adjustment utilizes a computational model that optimizes nutrients. After this, the final dish composition data is generated.

[0515] Step 6:

[0516] The server transmits the generated dish composition data to the user's terminal via an information display device. The user can then use this as a reference for cooking.

[0517] Step 7:

[0518] Through visual presentation methods, cooking suggestions are visually displayed on a digital visualization device. Recommended cooking procedures and the reasons behind them are shown, assisting store staff in immediately serving the dishes.

[0519] 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.

[0520] This invention is a system that generates recipes that take into account the user's taste and health information, as well as their emotional state. The system is equipped with an emotion engine as an input means to accept user input, thereby reflecting the emotional aspects that the user seeks in cooking in the recipe generation.

[0521] First, the user uses a dedicated application to input information about the taste of the dish in question, as well as any health restrictions (e.g., sugar or calorie restrictions). Furthermore, because the system is equipped with an emotion engine, the user also provides information about their current emotions and mood. Emotional information is entered by selecting from a set of options, or it is acquired using voice or facial recognition technology.

[0522] The device that receives this information sends the data to the server. The information sent includes all of the following: taste information, health restrictions, and emotional state.

[0523] The server uses natural language processing technology to build a taste profile based on the received data. Furthermore, it analyzes the emotional value required for the recipe based on emotional information. Depending on the user's emotions, it makes adjustments such as adding aromatic ingredients that enhance relaxation or introducing cooking methods that contribute to stress reduction.

[0524] Next, the server references a database of past recipes, searching for similar recipes and optimizing them to be health- and emotionally stimulating. This process ensures that the generated recipes align with the user's taste preferences, health considerations, and current emotions.

[0525] Finally, the generated recipe is sent to the device in digital format. The device displays the recipe in a visually and emotionally easy-to-understand format, making it easy for the user to cook and providing emotional satisfaction during the process.

[0526] As a concrete example, suppose a user requests a meal that will "relax them after a busy day." The system receives the user's emotional information that they "want to relax" and suggests a recipe based on that. For example, it might generate a dessert using herbal tea or a stew using herbs with relaxing effects, allowing the user to have a pleasant and relaxing experience through the meal.

[0527] The following describes the processing flow.

[0528] Step 1:

[0529] Users input information about the taste of nostalgic dishes, health restrictions, and emotions through an app on their device. Emotional information includes stress levels and moods such as wanting to relax, and can be entered using multiple-choice options or voice input.

[0530] Step 2:

[0531] The terminal formats the input information as digital data, which includes taste information, health information, and emotional information. This data is then transmitted to the server in a secure manner.

[0532] Step 3:

[0533] The server analyzes the received data and uses natural language processing technology to construct a taste profile based on the user's taste information. Furthermore, it uses an emotion engine to analyze emotional information and understand the emotional values ​​the user seeks.

[0534] Step 4:

[0535] Based on the taste profile and emotional analysis results, the server searches a database of past recipes to identify recipes with similar tastes. In this process, it prioritizes selecting cooking methods and ingredients that match the user's emotions.

[0536] Step 5:

[0537] The server adjusts the selected recipe based on the user's health information. Specifically, it uses a nutritional assessment model to modify ingredients and adjust seasonings within a healthy range.

[0538] Step 6:

[0539] The server generates the final recipe and organizes it as digital data, taking into account emotional and health values.

[0540] Step 7:

[0541] The server sends the generated recipe data to the terminal. During this process, emotionally resonant food photos and supplementary information regarding the cooking procedure are automatically added.

[0542] Step 8:

[0543] The device displays the received recipe on its user interface. The user can then begin cooking based on this information, and the app also includes a function to record the cooking progress.

[0544] (Example 2)

[0545] 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."

[0546] Conventional recipe generation systems only consider the user's taste and health information, failing to provide recipes that reflect the user's current emotional state. As a result, users were unable to choose meals that suited their mood at the time, sometimes leading to low emotional satisfaction. Furthermore, there is a growing need for meal suggestions that consider both health and emotional value.

[0547] 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.

[0548] In this invention, the server includes means for receiving taste information, health information, and emotional information; means for generating a taste profile; and means for adjusting cooking methods and ingredients. This makes it possible to provide recipes that take into account the user's emotional state and also consider health aspects.

[0549] "Input means" refers to a device or method for receiving taste information, health information, and emotional information from a user.

[0550] "Generation means" refers to an apparatus or technology for generating a taste profile based on received information.

[0551] "Specification method" refers to a device or technology that identifies the optimal cooking method from a past database based on the generated taste profile.

[0552] "Adjustment means" refers to a device or method for appropriately modifying cooking methods and ingredients, taking into account the user's health information and emotional information.

[0553] "Output means" refers to a device or method for providing the user with the generated final recipe data.

[0554] "Natural language processing technology" is a technology that analyzes user input data and allows machines to understand and process human language.

[0555] A "generative AI model" is an artificial intelligence model that learns from large amounts of data and generates new information.

[0556] "Similar recipes" are highly relevant recipes found from a database of past recipes based on the user's taste, health, and emotional information.

[0557] An "evaluation model" is a computational model that evaluates and optimizes data for a specific purpose based on the input information.

[0558] "Emotional information" refers to information that indicates the user's current emotions and psychological state.

[0559] To implement this invention, the user, terminal, and server each play specific roles. The user inputs detailed information about the dish in question using a dedicated application. This information includes taste information, health restriction information, and current emotional information. Emotional information is input either in the form of multiple-choice options or through speech recognition or facial recognition technology.

[0560] The input data is received by the terminal and sent to the server via a communication protocol. The hardware used here primarily consists of communication-capable computer devices (smartphones and tablets). The server, upon receiving the data, analyzes it using natural language processing techniques to generate a user-specific taste profile. This profile is then used, leveraging a generative AI model, to search for similar recipes from a database of past recipes.

[0561] Furthermore, the server takes into account the user's emotional information and makes adjustments that incorporate emotional value. It considers health and adjusts cooking methods and ingredients according to the emotional state. For example, if relaxation is desired, it can provide appropriate herbs and spices.

[0562] The generated optimal recipe is sent from the server to the terminal. The terminal then presents this to the user in a visually easy-to-understand format. Specifically, it displays the ingredient list, cooking instructions, cooking time, and expected emotional effects step by step.

[0563] For example, if a user requests a meal to "calm down after a stressful day," the system will use this emotional information to suggest recipes such as a soup made with relaxing herbal tea or a mild-flavored stew.

[0564] An example of a prompt message could be, "Please suggest a dish that would be suitable for a user who wants to relax." This could be used to instruct the generative AI model.

[0565] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0566] Step 1:

[0567] Users input information about food taste, health restrictions, and emotions through a dedicated application. Specifically, they select their preferred tastes (sweet, spicy, etc.), health restrictions (calories, sugar), and even emotional requests such as "I want to relax" within the app, or provide this information using voice or facial recognition. This input data is sent to the device as a collection of information reflecting the user's current needs.

[0568] Step 2:

[0569] The terminal combines the received user's taste information, health restrictions, and emotional information into a single digital data packet. This data packet serves as the basis for subsequent processing and is structured in a format such as JSON. The terminal then prepares to send this data to the server.

[0570] Step 3:

[0571] The terminal sends structured data packets to the server using a communication protocol (e.g., HTTPS). This transmission process ensures that all the necessary data for processing the user's request is delivered to the server.

[0572] Step 4:

[0573] The server generates a taste profile using natural language processing technology based on the received data. Specifically, it analyzes the taste and emotional information entered by the user and quantifies or categorizes each element. As a result, a taste profile that matches the user's preferences and emotions is generated.

[0574] Step 5:

[0575] The server uses a generative AI model to search a database of past recipes and extract recipes similar to the user's taste profile. During this process, the model is instructed using the prompt, "Suggest a dish that would be suitable if the user wants to relax." This selects recipes that are likely to meet the user's emotional and health requirements.

[0576] Step 6:

[0577] The server optimizes the extracted recipes based on the user's health and emotional information. For example, it might add herbs with relaxing effects or change cooking methods to reduce calories. This generates optimized recipes that are tailored to the user's needs.

[0578] Step 7:

[0579] The server then sends the final optimized recipe data to the terminal in a digital format. This recipe includes an ingredient list, cooking instructions, cooking time, and expected emotional effects.

[0580] Step 8:

[0581] The device displays received recipes in a user-friendly interface. Based on the displayed information, users can easily prepare meals and emotionally enjoy the process. Specifically, it includes visually easy-to-understand step-by-step guides and comments that evoke emotional impact.

[0582] (Application Example 2)

[0583] 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."

[0584] In modern society, users have dietary needs that respond to stress and emotional changes, but existing food delivery services lack the technology to adequately meet these needs. In particular, there is a demand for services that dynamically suggest meals tailored to each user's emotional and health state. However, conventional systems are unable to comprehensively analyze this information and fully reflect users' emotional and health needs.

[0585] 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.

[0586] In this invention, the server includes input means for receiving taste information, health information, and emotional information from a user; generation means for generating a taste profile and an emotional profile based on the taste information and emotional information; and identification means for identifying the optimal cooking method by referring to a past cooking database based on the profiles generated by the generation means. This makes it possible to quickly provide the optimal cooking method according to the user's emotional state and health state.

[0587] A "user" is an entity that uses the system to provide information about taste, health, and emotions.

[0588] "Taste information" refers to information about the user's preferences and desired taste of food.

[0589] "Health information" refers to information about the user's health status and dietary restrictions.

[0590] "Emotional information" refers to information about the user's current emotional state and mood.

[0591] "Input means" refers to an interface that receives taste, health, and emotional information from the user.

[0592] "Generation means" refers to a function that executes the process of creating taste profiles and emotion profiles based on taste information and emotion information.

[0593] A "taste profile" is a model of preferences created based on the taste information provided by the user.

[0594] An "emotional profile" is a psychological state model created based on a user's emotional information.

[0595] "Identification method" refers to the process of referring to the generated profile to identify the optimal cooking method.

[0596] "Adjustment mechanism" refers to a function that adjusts suggested cooking methods and ingredients, taking into account the user's health and emotional information.

[0597] The "output means" is an interface for providing the user with the final generated cooking instructions and recipe data.

[0598] The embodiment for carrying out this invention is a system comprising a user, a terminal, and a server as its main elements. The user inputs taste information, health information, and emotional information using a terminal such as a smartphone. Emotional information is acquired through facial recognition technology using the terminal's camera and voice input. This information is transmitted from the terminal to the server.

[0599] The server constructs taste and emotion profiles using generation methods based on the user's received taste and emotion information. This involves a process in which a generation AI model analyzes the user's preferences using natural language processing technology. Based on the generated profiles, the server identifies optimized cooking methods and makes adjustments according to the user's health and emotion information. For example, considering the emotion information that the user wants to relax, it can suggest dishes with relaxing effects.

[0600] The terminal receives the final cooking instructions sent from the server and presents them to the user in an easy-to-understand manner. The adjusted menu is presented to the user through a visually intuitive interface. Specifically, for a user who wants a relaxing meal at the end of a busy day, a dish using relaxing herbs is suggested.

[0601] An example of a prompt message could be: "The user wants to feel better. Please suggest a healthy and delicious menu that matches this feeling." Based on this prompt message, the system can suggest cooking methods that meet the user's request.

[0602] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0603] Step 1:

[0604] The user inputs taste, health, and emotional information using a device. Emotional information is acquired through a selection method, facial recognition technology using the device's camera, or speech recognition. Once this information is input, the device converts it into data packets and prepares to send them to the server. The input data consists of text information about taste and health constraints, and numerical data indicating emotional states. The output is a data packet ready for transmission.

[0605] Step 2:

[0606] The terminal sends the input taste information, health information, and emotional information to the server as data packets. The transmitted data packets arrive at the server via the internet. The input is the data packets generated in step 1, and the output is the data as it arrives at the server.

[0607] Step 3:

[0608] The server analyzes the received data packets and generates taste and emotion profiles using a generative AI model. The server uses natural language processing techniques to analyze text information and create individual profiles. The input is taste, health, and emotion information received from the user, and the output is a taste and emotion profile specific to each user.

[0609] Step 4:

[0610] The server identifies appropriate cooking methods by referencing a database of past cooking techniques based on the generated taste and emotion profiles. It searches for the most matching cooking method from past data and, if necessary, tunes it to fit the profile. The input is the profile generated in step 3 and the existing recipe database, and the output is the identified cooking method and ingredient information.

[0611] Step 5:

[0612] The server adjusts the identified cooking methods and ingredients as needed, taking into account the user's health information and emotional state. It creates the final cooking instructions by making nutritional or psychological adjustments to the suggested recipe based on the user's health restrictions and emotional state. The input is the cooking methods identified in step 4 and the user's health information, and the output is the adjusted final recipe information.

[0613] Step 6:

[0614] The server sends the final recipe information to the terminal. The terminal receives this information and displays it in a user-friendly format. Cooking details are presented through a user interface that provides visual information and emotional feedback. The input is the recipe information adjusted in step 5, and the output is the cooking instructions displayed on the terminal screen.

[0615] 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.

[0616] 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.

[0617] 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.

[0618] [Fourth Embodiment]

[0619] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0620] 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.

[0621] 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).

[0622] 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.

[0623] 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.

[0624] 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).

[0625] 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.

[0626] 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.

[0627] 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.

[0628] 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.

[0629] 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.

[0630] 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.

[0631] 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".

[0632] This invention is a system that starts with the user inputting specific taste and health information, and then uses AI technology to generate an optimal recipe based on that information. Specifically, it is carried out in the following steps.

[0633] First, users use a smartphone or computer to input information about nostalgic dishes and flavors they remember. This information includes specific dish names, flavor characteristics (e.g., "sweet and soy sauce flavor"), ingredients used, and cooking methods. Users also input information about their health, such as dietary restrictions related to high blood pressure or diabetes.

[0634] Next, the terminal sends the data entered by the user to the server. During this transmission process, the data is encrypted and sent in a secure manner. In particular, metadata such as the user ID and time are also transmitted simultaneously to ensure the integrity and consistency of the data.

[0635] The server uses the received data to generate a taste profile through an AI algorithm. This profile analyzes text data using natural language processing to extract the essential elements of the taste the user desires. Next, this profile is used to search a database for past recipes with similar taste characteristics and select the optimal cooking method and ingredients.

[0636] Furthermore, the server generates nutritionally balanced recipes based on the user's health information. Here, an AI-designed evaluation model is used to adjust the appropriate quantities of ingredients and seasonings. This process ensures that users receive recipes that are both health-conscious and satisfying in taste.

[0637] Finally, the server sends the final determined recipe to the terminal in digital format. The terminal displays this information on its user interface, allowing the user to cook the dish accordingly. It may also include visual support and step-by-step instructions, making cooking easier.

[0638] As a concrete example, consider a case where a user wants to recreate the taste of "meat and potato stew that their mother used to make." In this case, the user inputs "sweet and salty soy sauce flavor" as a characteristic of the dish. Based on this information, the server searches for recipe data with a similar taste profile, and further takes into account that the user needs to be mindful of high blood pressure, generating a recipe that is satisfying while being low in salt. In this way, the user can enjoy the desired taste safely and healthily.

[0639] The following describes the processing flow.

[0640] Step 1:

[0641] Users launch a dedicated app and enter taste information about their favorite dishes, as well as personal health information. Specifically, they enter the dish name, flavor characteristics, ingredients used, cooking method, and health restrictions (e.g., salt restriction).

[0642] Step 2:

[0643] The terminal formats the information entered by the user into the appropriate format and sends it to the server. The transmitted data includes not only the user's input but also the user ID and transmission time.

[0644] Step 3:

[0645] The server analyzes the received user information and uses natural language processing techniques to extract important keywords from the input text. This analysis generates a taste profile that clearly understands the user's requests.

[0646] Step 4:

[0647] The server searches a recipe database based on the generated taste profile and identifies past recipes with similar taste characteristics. This process utilizes pattern matching and clustering techniques.

[0648] Step 5:

[0649] The server adjusts the identified recipe, taking into account the user's health information. An AI model appropriately modifies the quantity and type of ingredients and the proportion of seasonings to optimize the nutritional balance. This step utilizes nutritional models and health restriction models.

[0650] Step 6:

[0651] The server finalizes the adjusted recipe and creates recipe data in a user-friendly format. The recipe includes an ingredient list, cooking instructions, preparation time, and equipment information.

[0652] Step 7:

[0653] The server sends the completed recipe to the terminal. The transmission method is optimized for the user interface, and the system is designed to allow for easy and convenient display of the recipe.

[0654] Step 8:

[0655] The device displays the received recipe on the user interface, allowing the user to begin cooking. The display includes visual support and other features to make the cooking process intuitively understandable.

[0656] (Example 1)

[0657] 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".

[0658] In today's world, providing recipes tailored to the diverse taste preferences and health conditions of individual users is a challenging task. Especially when there are specific health constraints, achieving a satisfying taste while meeting those conditions requires specialized knowledge, placing a significant burden on the average person. Furthermore, even when there is a desire to recreate nostalgic flavors, selecting effective cooking methods and ingredients can be difficult. Therefore, there is a need for a method that can generate appropriate recipes while considering individual preferences and health information.

[0659] 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.

[0660] In this invention, the server includes data input means, profile generation means, processing means, meal adjustment means, and information output means. This makes it possible for users to recreate nostalgic flavors while being provided with nutritionally balanced recipes that take health into consideration.

[0661] "Data input means" refers to a function for collecting taste and health information from users and incorporating it into the system.

[0662] The "profile generation means" is a function that models the characteristics of taste based on the taste information provided by the user and creates a taste profile.

[0663] The "processing means" refers to a function that references the generated taste profile and identifies the optimal cooking method from a database of past cooking information.

[0664] A "meal adjustment tool" is a function that adjusts cooking methods and ingredients based on the user's health information to create a meal suitable for their health.

[0665] The "information output means" refers to a function for providing the user with the final generated meal data.

[0666] This invention is a system that begins with the user inputting specific taste and health information. The user uses a device such as a smartphone or personal computer to input detailed information about nostalgic dishes and their tastes. This input information includes specific dish names and taste characteristics (e.g., "sweet and soy sauce flavor"), ingredients used, cooking methods, and even health information (e.g., information on dietary restrictions such as high blood pressure or diabetes).

[0667] Information entered by the user is transmitted to the server via the terminal. The terminal encrypts the data and transmits it using a secure protocol (e.g., HTTPS). Based on the received information, the server analyzes the user's taste data using natural language processing technology and extracts its essential elements. The taste profile generated based on the analysis results is created using a profile generation method that utilizes AI technology.

[0668] The server has a processing mechanism that refers to this taste profile and identifies the optimal cooking method from a database of past cooking information. It also includes a meal adjustment mechanism that adjusts cooking methods and ingredients considering the user's health information. This generates final meal data with adjusted nutritional balance.

[0669] The generated recipe is sent from the server to the terminal, which displays it in the user interface. The user can then cook the dish according to this, and the recipe may include visual support and step-by-step instructions, making cooking easier.

[0670] A concrete example is when a user wants to recreate the taste of "meat and potato stew that their mother used to make." In this case, the user enters "sweet and salty soy sauce flavor" as a characteristic. Based on this information, the server searches for recipe data with a similar taste profile and then provides a low-sodium recipe that takes high blood pressure into consideration.

[0671] An example of a prompt message might be, "I want to recreate the sweet soy sauce-flavored nikujaga (meat and potato stew) my mother used to make. I have high blood pressure, so I'd like a low-sodium recipe." Such a system allows users to enjoy dishes that suit their preferences while also being mindful of their health.

[0672] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0673] Step 1:

[0674] Users input information about nostalgic dishes and their flavors using devices such as smartphones and personal computers. This input includes specific dish names, flavor characteristics (e.g., "sweetness and soy sauce flavor"), ingredients used, cooking methods, and health information (e.g., high blood pressure or diabetes). The input data is structured and handled in an organized format by the device. As output, a data package is generated on the device, encoded and ready for transmission.

[0675] Step 2:

[0676] The terminal encrypts the data package entered by the user and sends it to the server using a secure communication protocol (e.g., HTTPS). The input includes encoded data packages from the terminal. The data is sent in chunks and encrypted to maintain network security. The output is the highly secure data received by the server.

[0677] Step 3:

[0678] The server decodes the received data and analyzes the user's taste and health information. The input is decrypted user data. The server uses natural language processing technology to analyze the received text data and extract the taste characteristics desired by the user. After data analysis, a taste profile is generated through AI technology. The taste profile is generated as output and is then passed on to the next processing step.

[0679] Step 4:

[0680] The server uses the generated taste profile to search for the optimal cooking method from a database of past cooking information. The input is the generated taste profile. The AI ​​algorithm uses this profile to search for and select similar recipes. The output is a list of the optimal cooking methods and ingredients.

[0681] Step 5:

[0682] The server adjusts the selected cooking method and ingredients based on the user's health information. The input consists of a list of cooking methods and ingredients, and the user's health profile. The server utilizes an AI evaluation model to readjust ingredient quantities, considering nutritional balance. The output is a health-conscious recipe.

[0683] Step 6:

[0684] The final recipe data is sent from the server to the terminal. The input is health-conscious recipe information sent from the server. The terminal displays the received recipe through a user interface. The user can proceed with cooking according to the presented recipe, receiving visual support and step-by-step instructions. The output is the completed recipe information displayed on the terminal.

[0685] (Application Example 1)

[0686] 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".

[0687] Today's individual customers, despite various health constraints, want to enjoy meals that suit their individual preferences. However, in reality, it is difficult for typical restaurants to provide customized dishes that take into account each customer's detailed taste preferences and health information. Furthermore, the lack of immediate menu suggestions from restaurant staff makes improving customer satisfaction a challenge.

[0688] 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.

[0689] In this invention, the server includes data input means for receiving taste information and health information from the user, profile generation means for generating a taste profile based on the taste information, and evaluation means for identifying the optimal cooking method by referring to a past food database. This enables the generation of customized recipes that take into account the taste and health of individual customers, and rapid menu suggestions using a digital visualization device.

[0690] "Data input means" refers to methods for obtaining taste information and health information from users.

[0691] The "profile generation means" is a function that creates an optimal taste profile for each individual user based on the acquired taste information.

[0692] The "evaluation method" is a method that identifies the optimal cooking method by referring to a previously recorded food database based on the generated taste profile.

[0693] "Nutritional adjustment means" refers to a function that optimizes the selected cooking method and the nutrients of ingredients, taking into account the health information provided by the user.

[0694] "Information presentation means" refers to a method of displaying the final generated dish composition data on the user's terminal and making suggestions to the user.

[0695] "Visual presentation means" refers to the function of visually displaying the proposed dishes using digital visualization devices in the store.

[0696] "Natural language processing technology" is a technology that analyzes information from users and extracts necessary information from text data.

[0697] A "computational model" is an algorithm used to optimize nutrients based on a user's health information.

[0698] This invention is a system that enables restaurants to suggest customized dishes based on the individual preferences and health conditions of their customers. By using a server, terminals, and a digital visualization device, it achieves effective cooking suggestions.

[0699] The server receives taste and health information from the user via a data input device. This information is transmitted from the user's smart glasses or mobile device. The data is encrypted and transmitted to the server using a secure communication method. The server uses the taste information to generate a taste profile using a profile generation device. Then, using an evaluation device, it refers to a food database based on the generated profile to identify the optimal cooking method and ingredients for the user.

[0700] This cooking method and ingredient information is further optimized based on the user's health information using a nutritional adjustment mechanism. The adjusted dish composition data is provided to the user terminal by an information presentation mechanism. A digital visualization device provides a visual suggestion of the dish as a visual presentation mechanism, displaying the steps and reasons for recommending the cooking in real time.

[0701] For example, if a user enters a prompt such as, "I would like a sweet and rich Japanese-style curry, but I want it to be low in calories," the system instantly identifies the curry recipe and cooking procedure that meets the request and displays it on a digital display. This allows store staff to quickly prepare and serve the dish.

[0702] The generative AI model optimizes recipe suggestions based on user input, adjusting specific ingredients and procedures using a computational model. To achieve this, it utilizes a cloud server, smart glasses, or mobile device as hardware, and a natural language processing library as software.

[0703] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0704] Step 1:

[0705] Users input taste and health information via smart glasses or mobile devices. This input includes prompts regarding specific taste characteristics and health restrictions. The information is encrypted and transmitted to the server using a secure communication protocol.

[0706] Step 2:

[0707] The server receives the received information through a data input mechanism and analyzes it using natural language processing technology. This extracts necessary taste and health-related elements from the text data. The extracted data is then used as material for generating taste profiles.

[0708] Step 3:

[0709] The server's profile generation mechanism generates a taste profile based on the extracted data. The taste profile represents a specific taste pattern that reflects the user's preferences. This profile is used in the next step.

[0710] Step 4:

[0711] The server uses evaluation tools to search a food database based on taste profiles and identify the optimal cooking method and ingredients. This process is performed by a database matching algorithm, which selects the most suitable recipe from among many.

[0712] Step 5:

[0713] The identified cooking methods and ingredients are adjusted to the user's health information using nutritional adjustment tools. This adjustment utilizes a computational model that optimizes nutrients. After this, the final dish composition data is generated.

[0714] Step 6:

[0715] The server transmits the generated dish composition data to the user's terminal via an information display device. The user can then use this as a reference for cooking.

[0716] Step 7:

[0717] Through visual presentation methods, cooking suggestions are visually displayed on a digital visualization device. Recommended cooking procedures and the reasons behind them are shown, assisting store staff in immediately serving the dishes.

[0718] 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.

[0719] This invention is a system that generates recipes that take into account the user's taste and health information, as well as their emotional state. The system is equipped with an emotion engine as an input means to accept user input, thereby reflecting the emotional aspects that the user seeks in cooking in the recipe generation.

[0720] First, the user uses a dedicated application to input information about the taste of the dish in question, as well as any health restrictions (e.g., sugar or calorie restrictions). Furthermore, because the system is equipped with an emotion engine, the user also provides information about their current emotions and mood. Emotional information is entered by selecting from a set of options, or it is acquired using voice or facial recognition technology.

[0721] The device that receives this information sends the data to the server. The information sent includes all of the following: taste information, health restrictions, and emotional state.

[0722] The server uses natural language processing technology to build a taste profile based on the received data. Furthermore, it analyzes the emotional value required for the recipe based on emotional information. Depending on the user's emotions, it makes adjustments such as adding aromatic ingredients that enhance relaxation or introducing cooking methods that contribute to stress reduction.

[0723] Next, the server references a database of past recipes, searching for similar recipes and optimizing them to be health- and emotionally stimulating. This process ensures that the generated recipes align with the user's taste preferences, health considerations, and current emotions.

[0724] Finally, the generated recipe is sent to the device in digital format. The device displays the recipe in a visually and emotionally easy-to-understand format, making it easy for the user to cook and providing emotional satisfaction during the process.

[0725] As a concrete example, suppose a user requests a meal that will "relax them after a busy day." The system receives the user's emotional information that they "want to relax" and suggests a recipe based on that. For example, it might generate a dessert using herbal tea or a stew using herbs with relaxing effects, allowing the user to have a pleasant and relaxing experience through the meal.

[0726] The following describes the processing flow.

[0727] Step 1:

[0728] Users input information about the taste of nostalgic dishes, health restrictions, and emotions through an app on their device. Emotional information includes stress levels and moods such as wanting to relax, and can be entered using multiple-choice options or voice input.

[0729] Step 2:

[0730] The terminal formats the input information as digital data, which includes taste information, health information, and emotional information. This data is then transmitted to the server in a secure manner.

[0731] Step 3:

[0732] The server analyzes the received data and uses natural language processing technology to construct a taste profile based on the user's taste information. Furthermore, it uses an emotion engine to analyze emotional information and understand the emotional values ​​the user seeks.

[0733] Step 4:

[0734] Based on the taste profile and emotional analysis results, the server searches a database of past recipes to identify recipes with similar tastes. In this process, it prioritizes selecting cooking methods and ingredients that match the user's emotions.

[0735] Step 5:

[0736] The server adjusts the selected recipe based on the user's health information. Specifically, it uses a nutritional assessment model to modify ingredients and adjust seasonings within a healthy range.

[0737] Step 6:

[0738] The server generates the final recipe and organizes it as digital data, taking into account emotional and health values.

[0739] Step 7:

[0740] The server sends the generated recipe data to the terminal. During this process, emotionally resonant food photos and supplementary information regarding the cooking procedure are automatically added.

[0741] Step 8:

[0742] The device displays the received recipe on its user interface. The user can then begin cooking based on this information, and the app also includes a function to record the cooking progress.

[0743] (Example 2)

[0744] 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".

[0745] Conventional recipe generation systems only consider the user's taste and health information, failing to provide recipes that reflect the user's current emotional state. As a result, users were unable to choose meals that suited their mood at the time, sometimes leading to low emotional satisfaction. Furthermore, there is a growing need for meal suggestions that consider both health and emotional value.

[0746] 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.

[0747] In this invention, the server includes means for receiving taste information, health information, and emotional information; means for generating a taste profile; and means for adjusting cooking methods and ingredients. This makes it possible to provide recipes that take into account the user's emotional state and also consider health aspects.

[0748] "Input means" refers to a device or method for receiving taste information, health information, and emotional information from a user.

[0749] "Generation means" refers to an apparatus or technology for generating a taste profile based on received information.

[0750] "Specification method" refers to a device or technology that identifies the optimal cooking method from a past database based on the generated taste profile.

[0751] "Adjustment means" refers to a device or method for appropriately modifying cooking methods and ingredients, taking into account the user's health information and emotional information.

[0752] "Output means" refers to a device or method for providing the user with the generated final recipe data.

[0753] "Natural language processing technology" is a technology that analyzes user input data and allows machines to understand and process human language.

[0754] A "generative AI model" is an artificial intelligence model that learns from large amounts of data and generates new information.

[0755] "Similar recipes" are highly relevant recipes found from a database of past recipes based on the user's taste, health, and emotional information.

[0756] An "evaluation model" is a computational model that evaluates and optimizes data for a specific purpose based on the input information.

[0757] "Emotional information" refers to information that indicates the user's current emotions and psychological state.

[0758] To implement this invention, the user, terminal, and server each play specific roles. The user inputs detailed information about the dish in question using a dedicated application. This information includes taste information, health restriction information, and current emotional information. Emotional information is input either in the form of multiple-choice options or through speech recognition or facial recognition technology.

[0759] The input data is received by the terminal and sent to the server via a communication protocol. The hardware used here primarily consists of communication-capable computer devices (smartphones and tablets). The server, upon receiving the data, analyzes it using natural language processing techniques to generate a user-specific taste profile. This profile is then used, leveraging a generative AI model, to search for similar recipes from a database of past recipes.

[0760] Furthermore, the server takes into account the user's emotional information and makes adjustments that incorporate emotional value. It considers health and adjusts cooking methods and ingredients according to the emotional state. For example, if relaxation is desired, it can provide appropriate herbs and spices.

[0761] The generated optimal recipe is sent from the server to the terminal. The terminal then presents this to the user in a visually easy-to-understand format. Specifically, it displays the ingredient list, cooking instructions, cooking time, and expected emotional effects step by step.

[0762] For example, if a user requests a meal to "calm down after a stressful day," the system will use this emotional information to suggest recipes such as a soup made with relaxing herbal tea or a mild-flavored stew.

[0763] An example of a prompt message could be, "Please suggest a dish that would be suitable for a user who wants to relax." This could be used to instruct the generative AI model.

[0764] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0765] Step 1:

[0766] Users input information about food taste, health restrictions, and emotions through a dedicated application. Specifically, they select their preferred tastes (sweet, spicy, etc.), health restrictions (calories, sugar), and even emotional requests such as "I want to relax" within the app, or provide this information using voice or facial recognition. This input data is sent to the device as a collection of information reflecting the user's current needs.

[0767] Step 2:

[0768] The terminal combines the received user's taste information, health restrictions, and emotional information into a single digital data packet. This data packet serves as the basis for subsequent processing and is structured in a format such as JSON. The terminal then prepares to send this data to the server.

[0769] Step 3:

[0770] The terminal sends structured data packets to the server using a communication protocol (e.g., HTTPS). This transmission process ensures that all the necessary data for processing the user's request is delivered to the server.

[0771] Step 4:

[0772] The server generates a taste profile using natural language processing technology based on the received data. Specifically, it analyzes the taste and emotional information entered by the user and quantifies or categorizes each element. As a result, a taste profile that matches the user's preferences and emotions is generated.

[0773] Step 5:

[0774] The server uses a generative AI model to search a database of past recipes and extract recipes similar to the user's taste profile. During this process, the model is instructed using the prompt, "Suggest a dish that would be suitable if the user wants to relax." This selects recipes that are likely to meet the user's emotional and health requirements.

[0775] Step 6:

[0776] The server optimizes the extracted recipes based on the user's health and emotional information. For example, it might add herbs with relaxing effects or change cooking methods to reduce calories. This generates optimized recipes that are tailored to the user's needs.

[0777] Step 7:

[0778] The server then sends the final optimized recipe data to the terminal in a digital format. This recipe includes an ingredient list, cooking instructions, cooking time, and expected emotional effects.

[0779] Step 8:

[0780] The device displays received recipes in a user-friendly interface. Based on the displayed information, users can easily prepare meals and emotionally enjoy the process. Specifically, it includes visually easy-to-understand step-by-step guides and comments that evoke emotional impact.

[0781] (Application Example 2)

[0782] 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".

[0783] In modern society, users have dietary needs that respond to stress and emotional changes, but existing food delivery services lack the technology to adequately meet these needs. In particular, there is a demand for services that dynamically suggest meals tailored to each user's emotional and health state. However, conventional systems are unable to comprehensively analyze this information and fully reflect users' emotional and health needs.

[0784] 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.

[0785] In this invention, the server includes input means for receiving taste information, health information, and emotional information from a user; generation means for generating a taste profile and an emotional profile based on the taste information and emotional information; and identification means for identifying the optimal cooking method by referring to a past cooking database based on the profiles generated by the generation means. This makes it possible to quickly provide the optimal cooking method according to the user's emotional state and health state.

[0786] A "user" is an entity that uses the system to provide information about taste, health, and emotions.

[0787] "Taste information" refers to information about the user's preferences and desired taste of food.

[0788] "Health information" refers to information about the user's health status and dietary restrictions.

[0789] "Emotional information" refers to information about the user's current emotional state and mood.

[0790] "Input means" refers to an interface that receives taste, health, and emotional information from the user.

[0791] "Generation means" refers to a function that executes the process of creating taste profiles and emotion profiles based on taste information and emotion information.

[0792] A "taste profile" is a model of preferences created based on the taste information provided by the user.

[0793] An "emotional profile" is a psychological state model created based on a user's emotional information.

[0794] "Identification method" refers to the process of referring to the generated profile to identify the optimal cooking method.

[0795] "Adjustment mechanism" refers to a function that adjusts suggested cooking methods and ingredients, taking into account the user's health and emotional information.

[0796] The "output means" is an interface for providing the user with the final generated cooking instructions and recipe data.

[0797] The embodiment for carrying out this invention is a system comprising a user, a terminal, and a server as its main elements. The user inputs taste information, health information, and emotional information using a terminal such as a smartphone. Emotional information is acquired through facial recognition technology using the terminal's camera and voice input. This information is transmitted from the terminal to the server.

[0798] The server constructs taste and emotion profiles using generation methods based on the user's received taste and emotion information. This involves a process in which a generation AI model analyzes the user's preferences using natural language processing technology. Based on the generated profiles, the server identifies optimized cooking methods and makes adjustments according to the user's health and emotion information. For example, considering the emotion information that the user wants to relax, it can suggest dishes with relaxing effects.

[0799] The terminal receives the final cooking instructions sent from the server and presents them to the user in an easy-to-understand manner. The adjusted menu is presented to the user through a visually intuitive interface. Specifically, for a user who wants a relaxing meal at the end of a busy day, a dish using relaxing herbs is suggested.

[0800] An example of a prompt message could be: "The user wants to feel better. Please suggest a healthy and delicious menu that matches this feeling." Based on this prompt message, the system can suggest cooking methods that meet the user's request.

[0801] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0802] Step 1:

[0803] The user inputs taste, health, and emotional information using a device. Emotional information is acquired through a selection method, facial recognition technology using the device's camera, or speech recognition. Once this information is input, the device converts it into data packets and prepares to send them to the server. The input data consists of text information about taste and health constraints, and numerical data indicating emotional states. The output is a data packet ready for transmission.

[0804] Step 2:

[0805] The terminal sends the input taste information, health information, and emotional information to the server as data packets. The transmitted data packets arrive at the server via the internet. The input is the data packets generated in step 1, and the output is the data as it arrives at the server.

[0806] Step 3:

[0807] The server analyzes the received data packets and generates taste and emotion profiles using a generative AI model. The server uses natural language processing techniques to analyze text information and create individual profiles. The input is taste, health, and emotion information received from the user, and the output is a taste and emotion profile specific to each user.

[0808] Step 4:

[0809] The server identifies appropriate cooking methods by referencing a database of past cooking techniques based on the generated taste and emotion profiles. It searches for the most matching cooking method from past data and, if necessary, tunes it to fit the profile. The input is the profile generated in step 3 and the existing recipe database, and the output is the identified cooking method and ingredient information.

[0810] Step 5:

[0811] The server adjusts the identified cooking methods and ingredients as needed, taking into account the user's health information and emotional state. It creates the final cooking instructions by making nutritional or psychological adjustments to the suggested recipe based on the user's health restrictions and emotional state. The input is the cooking methods identified in step 4 and the user's health information, and the output is the adjusted final recipe information.

[0812] Step 6:

[0813] The server sends the final recipe information to the terminal. The terminal receives this information and displays it in a user-friendly format. Cooking details are presented through a user interface that provides visual information and emotional feedback. The input is the recipe information adjusted in step 5, and the output is the cooking instructions displayed on the terminal screen.

[0814] 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.

[0815] 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.

[0816] 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.

[0817] 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.

[0818] 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.

[0819] 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.

[0820] 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.

[0821] 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.

[0822] 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."

[0823] 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.

[0824] 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.

[0825] 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.

[0826] 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.

[0827] 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.

[0828] 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.

[0829] 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.

[0830] 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.

[0831] 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.

[0832] 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.

[0833] 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.

[0834] 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.

[0835] The following is further disclosed regarding the embodiments described above.

[0836] (Claim 1)

[0837] An input means for receiving taste information and health information from the user,

[0838] A generation means for generating a taste profile based on the aforementioned taste information,

[0839] Based on the taste profile generated by the generation means, an identification means identifies the optimal cooking method by referring to a past recipe database,

[0840] A means for adjusting cooking methods and ingredients, taking into account the user's health information,

[0841] An output means for generating and providing final recipe data to the user,

[0842] A system that includes this.

[0843] (Claim 2)

[0844] The system according to claim 1, further comprising means for analyzing user input data using natural language processing technology in generating the aforementioned taste profile.

[0845] (Claim 3)

[0846] The system according to claim 1, characterized in that the adjustment means utilizes an evaluation model for optimizing nutritional components based on the user's health information.

[0847] "Example 1"

[0848] (Claim 1)

[0849] A data input means for receiving taste information and health information from the user,

[0850] A profile generation means that generates a taste profile based on the aforementioned taste information,

[0851] A processing means that identifies the optimal cooking method for a meal based on the taste profile generated by the profile generation means by referring to a database of past cooking information,

[0852] A meal preparation means that adjusts the meal preparation method and ingredients, taking into consideration the user's health information,

[0853] A means for generating final meal data and providing it to the user,

[0854] A system that includes this.

[0855] (Claim 2)

[0856] The system according to claim 1, further comprising analysis means for analyzing user input data using natural language processing technology in generating the aforementioned taste profile.

[0857] (Claim 3)

[0858] The system according to claim 1, characterized in that the meal adjustment means utilizes an evaluation model for optimizing nutritional composition based on the user's health information.

[0859] "Application Example 1"

[0860] (Claim 1)

[0861] A data input means for receiving taste information and health information from the user,

[0862] A profile generation means that generates a taste profile based on the aforementioned taste information,

[0863] An evaluation means that identifies the optimal cooking method by referring to a past food database based on the taste profile generated by the generation means,

[0864] A nutritional adjustment means that adjusts cooking methods and ingredients in consideration of the user's health information,

[0865] An information presentation means that generates the final dish composition data and provides it to the user terminal,

[0866] A visual presentation means that visually displays proposed dishes using a digital visualization device in a store environment,

[0867] A system that includes this.

[0868] (Claim 2)

[0869] The system according to claim 1, further comprising an analysis means for analyzing user information input data using natural language processing technology in generating the aforementioned taste profile.

[0870] (Claim 3)

[0871] The system according to claim 1, characterized in that the nutrition adjustment means utilizes a computational model for optimizing nutrients based on the user's health information, and further displays cooking procedures and reasons for recommendations using a digital visualization device.

[0872] "Example 2 of combining an emotion engine"

[0873] (Claim 1)

[0874] An input means for receiving taste information, health information, and emotional information from the user,

[0875] A generation means for generating a taste profile based on the aforementioned taste information and emotional information,

[0876] Based on the taste profile generated by the generation means, an identification means identifies the optimal cooking method by referring to a past recipe database,

[0877] An adjustment means for adjusting cooking methods and ingredients, taking into account the user's health information and emotional information,

[0878] An output means for generating and providing final recipe data to the user,

[0879] A system that includes this.

[0880] (Claim 2)

[0881] The system according to claim 1, comprising means for analyzing user input data using natural language processing technology in generating the aforementioned taste profile, and further using a generation AI model to search for similar recipes.

[0882] (Claim 3)

[0883] The system according to claim 1, characterized in that the adjustment means utilizes an evaluation model for optimizing nutritional components and emotional satisfaction based on the user's health information and emotional information.

[0884] "Application example 2 when combining with an emotional engine"

[0885] (Claim 1)

[0886] An input means for receiving taste information, health information, and emotional information from the user,

[0887] A generation means for generating a taste profile and an emotional profile based on the aforementioned taste information and emotional information,

[0888] Based on the profile generated by the generation means, an identification means identifies the optimal cooking method by referring to a past cooking database,

[0889] Adjustment means for adjusting cooking methods and ingredients, taking into account the user's health information and emotional information,

[0890] An output means that generates and provides the final processed data to the user,

[0891] A system that includes this.

[0892] (Claim 2)

[0893] The system according to claim 1, further comprising means for analyzing user input data using natural language processing technology in generating the aforementioned profile.

[0894] (Claim 3)

[0895] The system according to claim 1, characterized in that the adjustment means utilizes an evaluation model for optimizing nutritional components based on the user's health information and emotional information. [Explanation of Symbols]

[0896] 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. An input means for receiving taste information and health information from the user, A generation means for generating a taste profile based on the aforementioned taste information, Based on the taste profile generated by the generation means, an identification means identifies the optimal cooking method by referring to a past recipe database, A means for adjusting cooking methods and ingredients, taking into account the user's health information, An output means for generating and providing final recipe data to the user, A system that includes this.

2. The system according to claim 1, further comprising means for analyzing user input data using natural language processing technology in generating the aforementioned taste profile.

3. The system according to claim 1, characterized in that the adjustment means utilizes an evaluation model for optimizing nutritional components based on the user's health information.

Images