system

Abstract

We provide the system. [Solution] A means for the user to select a recipe they want to make, A means of converting abstract expressions into concrete numerical values ​​using generative AI, A means of obtaining climate information for the current location using GPS and weather information, A means of adjusting recipe values ​​based on acquired climate information, A means of displaying the adjusted recipe 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 persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] General cooking recipes often contain many abstract expressions that are difficult for beginners to understand, so there is a problem of frequent cooking failures. Also, since temperature and humidity vary depending on location and season, cooking conditions are likely to change, which may affect the cooking results. It is required to solve such problems so that anyone can cook accurate and delicious dishes.

Means for Solving the Problems

[0005] The present invention is a system that includes means for a user to select a recipe they want to make, means for converting abstract expressions into concrete numerical values ​​using a generative AI, means for acquiring climate information of the current location using GPS and weather information, means for adjusting the numerical values ​​of the recipe based on the acquired climate information, and means for displaying the adjusted recipe to the user. This enables accurate cooking regardless of the environment or experience, and reduces cooking failures.

[0006] A "user" is an individual or entity that operates a system, selects a cooking recipe, and prepares a meal.

[0007] A "recipe" is information that describes, either specifically or abstractly, the steps, ingredients, and cooking methods for preparing a dish.

[0008] "Generative AI" is an artificial intelligence system that analyzes input information and converts abstract instructions into concrete numerical values.

[0009] GPS is a global positioning system used to determine any location on Earth.

[0010] "Weather information" refers to meteorological data such as temperature, humidity, and atmospheric pressure at a given location.

[0011] "Abstract expressions" are vague instructions used in typical cooking recipes, such as "one tablespoon" or "cook over medium heat for 5 minutes."

[0012] "Specific numerical values" refer to precise numerical values ​​obtained by converting abstract expressions into a format that is easy for machines and humans to understand (e.g., 15ml, heat level 3).

[0013] "Current location" refers to the geographical location information identified by the user using GPS.

[0014] "Climate information" refers to weather conditions at a specific point in time in the current location (e.g., temperature, humidity, precipitation, etc.).

[0015] "Adjustment" refers to numerical modification based on the acquired climate information to change the original recipe data and provide more optimal cooking conditions.

[0016] "Display" means visually presenting the adjusted recipe information so that the user can view it.

[0017] "Database" is an information system that systematically accumulates information and enables access as needed.

Brief Explanation of Drawings

[0018] [Figure 1] It is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] It shows an emotion map to which multiple emotions are mapped. [Figure 10] It shows an emotion map to which multiple emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of a 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 Embodiment 2 when the emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when the emotion engine is combined.

Mode for Carrying Out the Invention

[0019] 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.

[0020] First, the terms used in the following description will be explained.

[0021] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0022] 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.

[0023] [[ID=二十九]] 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.

[0024] 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).

[0025] 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."

[0026] [First Embodiment]

[0027] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0028] 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.

[0029] 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).

[0030] 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.

[0031] 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.

[0032] 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.

[0033] 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.

[0034] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0035] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0036] 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.

[0037] 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.

[0038] 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".

[0039] This invention is a cooking support system in which a user, a terminal, and a server work together to convert abstract recipe descriptions into concrete numerical values ​​and further adjust the recipe based on climate information of the user's current location. This system is implemented in the following steps.

[0040] Overall system flow

[0041] 1. The user selects a cooking recipe.

[0042] The user launches the application and displays the cooking recipe list screen.

[0043] The user selects the "Chicken Curry" recipe.

[0044] The device sends the selected recipe information to the server.

[0045] 2. The server receives and analyzes the recipe.

[0046] The server retrieves recipe information from the database based on the recipe ID received from the terminal.

[0047] The server analyzes the recipe content and detects abstract expressions such as "one tablespoon" and "cook over medium heat for 5 minutes."

[0048] The detected expressions are listed and prepared for conversion.

[0049] 3. The server uses AI to convert the data into specific numerical values.

[0050] The server passes the detected abstract representations to the generating AI as input.

[0051] The generating AI converts "one tablespoon" into specific numerical values ​​such as 15 ml, and "5 minutes over medium heat" into "5 minutes at heat level 3."

[0052] The server then reflects the specific numerical values ​​that have been converted into the recipe.

[0053] 4. The device acquires GPS information.

[0054] The device activates GPS to obtain the user's current location.

[0055] The device obtains its current location information and sends it to the server.

[0056] 5. The device obtains weather information for its current location.

[0057] The device calls a weather API to obtain weather information for its current location.

[0058] The device sends the weather information it has acquired to the server.

[0059] 6. The server adjusts the recipe based on weather information.

[0060] The server analyzes weather information and applies recipe adjustment rules (e.g., add 1 ml of seasoning if the temperature is below 15°C, or add 30 seconds to the cooking time if the humidity is above 60%).

[0061] The server adds adjustment values ​​based on weather information to recipes that have specific numerical values.

[0062] Update each step of the recipe and generate the adjusted recipe data.

[0063] 7. The device displays the adjusted recipe to the user.

[0064] The terminal receives the adjusted recipe data from the server.

[0065] The device displays the adjusted recipe on the cooking instructions screen. For example, it might say, "Add chicken and 16ml of seasoning, and heat over medium heat (heat level 3) for 5 minutes and 30 seconds."

[0066] 8. The user cooks according to the adjusted recipe.

[0067] The user cooks according to the specific and adjusted recipe displayed.

[0068] By following precise steps and numerical values, users can achieve a high success rate in cooking.

[0069] Specific example

[0070] For example, when making "chicken curry," a typical recipe might instruct you to "use one tablespoon of curry powder" or "sauté over medium heat for 5 minutes." The AI ​​then converts this into specific values ​​such as 15ml of curry powder and 5 minutes at heat level 3. Furthermore, it makes adjustments such as adding 1ml of seasoning if the current temperature is below 15℃, or adding 30 seconds to the cooking time if the humidity is above 60%, ensuring that delicious food can be consistently produced in any environment.

[0071] This system enables even cooking novices to achieve professional-level results while also offering the flexibility to adapt to different environmental conditions.

[0072] The following describes the processing flow.

[0073] Step 1:

[0074] The user launches the application and displays a list of cooking recipes. The user selects the "Chicken Curry" recipe from the list, and the device sends the selected recipe information to the server.

[0075] Step 2:

[0076] The server retrieves the corresponding recipe information from the database based on the recipe ID received from the terminal. The server analyzes the recipe content and detects abstract expressions such as "one tablespoon" or "cook over medium heat for 5 minutes." The detected expressions are then listed.

[0077] Step 3:

[0078] The server passes the detected abstract expressions to the generating AI as input. The generating AI converts "one tablespoon" into specific numerical values, such as 15 ml, and "5 minutes over medium heat" into "5 minutes at heat level 3." The server then incorporates these converted specific values ​​into the recipe.

[0079] Step 4:

[0080] The device activates GPS to obtain the user's current location. The device obtains the location information and sends the latitude and longitude data to the server.

[0081] Step 5:

[0082] The device calls a weather API to obtain weather information for its current location. Specifically, it retrieves meteorological data such as current temperature, humidity, and atmospheric pressure. The device then sends the obtained weather information to the server.

[0083] Step 6:

[0084] The server analyzes the weather information it has acquired. The server applies recipe adjustment rules (e.g., add 1 ml of seasoning if the temperature is below 15°C, add 30 seconds to the cooking time if the humidity is above 60%) to adjust the values ​​in the recipe. The server updates each step of the recipe based on the specific values ​​and generates the adjusted recipe data.

[0085] Step 7:

[0086] The terminal receives the adjusted recipe data from the server. The terminal displays the adjusted recipe to the user. Specifically, it displays "Add chicken and 16ml of seasoning, and heat over medium heat (heat level 3) for 5 minutes and 30 seconds."

[0087] Step 8:

[0088] The user cooks according to the specific and adjusted recipe displayed. By following the precise steps and numerical values, the user can achieve a high success rate in cooking.

[0089] Each of these steps helps the system to support users in consistently creating delicious meals, regardless of the environment.

[0090] (Example 1)

[0091] 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."

[0092] In recent years, there has been a growing need for easy-to-use cooking methods that allow even users with limited culinary knowledge and experience to prepare delicious meals. However, the results are often inconsistent due to abstract recipe instructions and environmental differences (such as climate variations). Furthermore, there is no clear method for incorporating acquired climate information into recipes. Therefore, there is a need to develop a system that enables anyone to consistently produce high-quality meals.

[0093] 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.

[0094] In this invention, the server includes means for the user to select a recipe they wish to make, means for converting abstract expressions into concrete numerical values ​​using a generative AI model, and means for acquiring environmental information of the current location using GPS and climate data. This allows the user to be provided with an adjusted recipe indicated by concrete numerical values, enabling the creation of consistently delicious dishes even under different climate conditions.

[0095] A "user" is someone who uses the system to create a dish.

[0096] A "recipe to be made" refers to the steps of a dish that the user selects and which the system then processes.

[0097] A "generative AI model" is an artificial intelligence model designed to give concrete form to abstract expressions that are input into it.

[0098] "Abstract expressions" refer to expressions in a recipe that do not specify concrete numerical values. Examples: "one tablespoon," "cook over medium heat for 5 minutes," etc.

[0099] "Specific numerical values" refer to numbers that accurately represent the cooking steps and quantities in a recipe. Examples: "15ml", "5 minutes at heat level 3", etc.

[0100] GPS is a global positioning system used to determine a user's current location.

[0101] "Climate data" refers to data that includes environmental information such as temperature, humidity, and precipitation.

[0102] "Environmental information" refers to climate data for the current location.

[0103] "Adjusting" means changing the numerical values ​​of a recipe based on the acquired environmental information.

[0104] "Data storage means" refers to a method or apparatus for saving and reusing the numerical values ​​of a modified recipe.

[0105] "Multiple rules" refers to several criteria or guidelines for adjusting recipe values ​​based on acquired environmental information.

[0106] A "prompt message" is a text input to a generative AI model, intended to prompt the model to transform abstract expressions.

[0107] This invention is a system in which a user, a terminal, and a server work together to convert cooking recipes into specific numerical values ​​and further adjust the recipes based on climate information of the current location. The following hardware and software are used to implement this system.

[0108] The user launches the application on a device such as a smartphone or tablet. This application allows the user to access a screen where they can select a cooking recipe. For example, if the user selects the "Chicken Curry" recipe, the device sends that information to the server. The server retrieves the recipe information from its database based on the recipe ID and analyzes the recipe content. From the analyzed recipe content, it detects abstract expressions such as "one tablespoon" and "cook over medium heat for 5 minutes."

[0109] The server uses a generative AI model (e.g., GPT-4) to convert this abstract expression into concrete numerical values. Input to this AI model is in the form of prompt statements. For example, if the prompt statement "Convert the expression 'one tablespoon' into a concrete numerical value" is passed to the generative AI model, "one tablespoon" will be converted to 15 ml, and "5 minutes over medium heat" will be converted to "5 minutes at heat level 3". The server then reflects these conversion results in the original recipe.

[0110] Furthermore, the device uses GPS functionality to obtain the user's current location. Based on this location information, the device calls a weather API (for example, the OpenWeatherMap API) to obtain climate data such as temperature and humidity at the current location. This climate data is sent to a server, which performs analysis. Based on the results of this analysis, the recipe is adjusted. For example, if the temperature is below 15°C, the amount of seasoning is increased by 1 ml, and if the humidity is above 60%, the cooking time is increased by 30 seconds, according to these rules.

[0111] The adjusted recipe is sent back from the server to the terminal and displayed on the terminal. The displayed recipe includes specific numerical values, such as "Add chicken and 16 ml of seasoning, and heat over medium heat (heat level 3) for 5 minutes and 30 seconds." The user then cooks according to these instructions.

[0112] This system enables even cooking novices to achieve professional-level results while also offering the flexibility to adapt to different environmental conditions.

[0113] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0114] Step 1:

[0115] The user selects a cooking recipe. The user launches the application on their device and selects "Chicken Curry" from the displayed list of cooking recipes. The device receives the selected recipe ID as input and sends it to the server. Specifically, the user performs a tap operation, and the device generates an HTTP request and sends the recipe ID to the server.

[0116] Step 2:

[0117] The server receives and analyzes the recipe. The server uses the recipe ID received from the terminal as input to retrieve the corresponding recipe information from the database. The retrieved recipe information includes abstract expressions such as "one tablespoon" and "5 minutes over medium heat." The server analyzes these abstract expressions and creates a list. Data processing involves string manipulation to extract abstract keywords.

[0118] Step 3:

[0119] The server uses a generative AI model to convert abstract expressions into concrete numerical values. The server generates a prompt and passes the abstract expression as input to the generative AI model. An example of such a prompt is, "Please convert the expression 'one tablespoon' into a concrete numerical value." Based on the prompt, the generative AI model converts "one tablespoon" to 15 ml and "5 minutes over medium heat" to "5 minutes at heat level 3," and outputs these to the server. The server then incorporates this output into the original recipe.

[0120] Step 4:

[0121] The device acquires GPS information. While the user carries the device, the device activates its internal GPS module and acquires current location information as input. The acquired latitude and longitude information is sent to the server in HTTP request format. Specifically, the device activates its GPS sensor, periodically captures location information, and sends it to the server.

[0122] Step 5:

[0123] The device obtains weather information for its current location. Based on this location information, the device calls a weather API to obtain environmental information such as temperature and humidity. For example, it uses the OpenWeatherMap API. This obtained climate data is sent to the server as input. The device generates an HTTP request, sends the request to the weather API, parses the resulting JSON data, extracts the necessary information, and sends it to the server.

[0124] Step 6:

[0125] The server analyzes weather information and adjusts recipes accordingly. The server analyzes environmental information such as temperature and humidity. Based on this analysis, the server applies recipe adjustment rules. For example, if the temperature is below 15°C, it increases the amount of seasoning by 1 ml, and if the humidity is above 60%, it increases the cooking time by 30 seconds. The server uses these adjusted values ​​to update the recipe and generate new recipe data. Data processing involves adding or updating numerical values ​​in the recipe data.

[0126] Step 7:

[0127] The terminal displays the adjusted recipe to the user. The terminal receives the adjusted recipe data from the server as input and displays specific numerical values ​​and instructions on the cooking procedure screen. For example, it might display, "Add chicken and 16ml of seasoning, and heat over medium heat (heat level 3) for 5 minutes and 30 seconds." The terminal parses the received JSON data and displays it on the screen.

[0128] Step 8:

[0129] The user cooks according to the adjusted recipe. The user follows the specific and adjusted recipe displayed on the device. The user performs specific actions such as using measuring cups and adjusting the heat, and completes the dish according to the instructions. By following the appropriate procedures and values, the user can produce dishes of consistent quality.

[0130] (Application Example 1)

[0131] 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."

[0132] Traditional cooking recipes often standardize factors such as climate conditions, location, specific ingredient amounts, and cooking times, which can lead to variations in the quality and final result of dishes. Furthermore, the consistency of dishes in physical restaurants is often inconsistent, due to differences among cooking staff, making it difficult to provide customers with consistent quality. To address these issues, a system is needed that adjusts recipes based on real-time climate information and provides cooking staff with specific and consistent instructions.

[0133] 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.

[0134] In this invention, the server includes means for the user to select a recipe they wish to make, means for converting abstract expressions into concrete numerical values ​​using a generative AI, means for acquiring climate information of the current location using GPS and weather information, means for adjusting the numerical values ​​of the recipe based on the acquired climate information, means for displaying the adjusted recipe to the user, and means for displaying the adjusted recipe on a display device in a physical store. This makes it possible to consistently provide high-quality food in any environment.

[0135] A "user" is an individual or a kitchen staff member at a physical restaurant who selects a recipe and uses the system to prepare a meal.

[0136] A "recipe" is a document that contains specific instructions and information about ingredients for preparing a dish.

[0137] "Generative AI" is an artificial intelligence model that converts abstract expressions into concrete numbers or instructions.

[0138] "Abstract expressions" are those that lack specific numerical values, such as "one tablespoon" or "5 minutes over medium heat."

[0139] "Specific numerical values" refer to clear, quantified instructions such as "15ml" or "5 minutes at heat level 3" that are converted by the generating AI.

[0140] GPS is a global positioning system used to determine one's location on Earth.

[0141] "Weather information" refers to meteorological data such as temperature, humidity, and wind speed at your current location.

[0142] "Climate information" refers to environmental data based on acquired weather information.

[0143] "Adjusting the values" means changing the amount of ingredients or cooking time in a recipe based on climate information.

[0144] A "display device" is a screen device used to display the adjusted recipe and cooking instructions.

[0145] This invention is a system for converting abstract recipe instructions into concrete numerical values ​​when a user is cooking, and further adjusting the recipe based on climate information of the user's current location. Specifically, it is implemented in the following steps.

[0146] First, the user launches the application using a device (such as a smartphone or tablet) and displays a list of cooking recipes. When the user selects a recipe they want to make, the recipe information is sent from the device to the server. The server retrieves the corresponding recipe information from the database based on the recipe ID and analyzes the recipe content. At this time, the server uses a generation AI to detect abstract expressions such as "one tablespoon" or "5 minutes over medium heat" and converts them into specific numerical values ​​such as "15 ml" or "5 minutes at heat level 3".

[0147] Next, the device uses GPS to obtain the user's current location and calls a weather API (e.g., WeatherAPI) to retrieve weather information for the current location. The obtained climate information is sent from the device to the server, which adjusts the recipe values ​​based on this information. Specifically, rules such as adding 1 ml of seasoning if the temperature is below 15°C, or adding 30 seconds to the cooking time if the humidity is above 60%, are applied.

[0148] The adjusted recipe is sent from the server to the terminal and displayed on the terminal's display device. This display is also shown on the display device in the physical store. By following the displayed, specific, and adjusted recipe instructions, the cooking staff can consistently provide high-quality dishes that are adapted to the environment.

[0149] The hardware required includes a smartphone, tablet, display device, GPS module, and an internet connection to obtain weather information. The software used includes a recipe database, a generative AI model, a weather API, and a dedicated cooking assistance application.

[0150] As a concrete example, if a user wants to make "chicken curry," and uses the application to confirm their current location is Tokyo, and obtains weather information indicating a temperature of 10°C and humidity of 70%, the original recipe instruction of "one tablespoon of curry powder" will be converted to "16 ml," and "sauté over medium heat for 5 minutes" will be adjusted to "sauté over medium heat (heat level 3) for 5 minutes and 30 seconds."

[0151] Examples of prompt statements to input into the generative AI model are as follows:

[0152] Example of a prompt:

[0153] Input: "One tablespoon"

[0154] Output: "15ml"

[0155] Input: "5 minutes over medium heat"

[0156] Output: "5 minutes with firepower level 3"

[0157] This will enable us to consistently provide high-quality food in our physical stores as well.

[0158] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0159] Step 1:

[0160] The user launches the application and displays a list of cooking recipes. When the user selects a cooking recipe they want to make, the recipe information is sent from the device to the server.

[0161] Input: User-selected recipe ID

[0162] Output: Recipe ID to send to the server

[0163] Specific operation: When the user selects "Chicken Curry," the device automatically sends its recipe ID to the server.

[0164] Step 2:

[0165] Based on the recipe ID received from the terminal, the server retrieves the corresponding recipe information from the database and analyzes the recipe content.

[0166] Input: Recipe ID sent to the server

[0167] Output: Recipe information (including abstract expressions)

[0168] Specific operation: The server executes a database query to retrieve recipe information corresponding to the recipe ID. It then analyzes the retrieved recipe content and lists abstract expressions such as "one tablespoon" or "cook over medium heat for 5 minutes."

[0169] Step 3:

[0170] The server takes abstract representations as input to the generated AI model and converts them into concrete numerical values.

[0171] Input: Abstract expressions (e.g., "one tablespoon", "cook over medium heat for 5 minutes")

[0172] Output: Specific numerical values ​​(e.g., "15ml", "5 minutes at heat level 3")

[0173] Specific operation: The server takes prompt text such as "one tablespoon" as input to the generating AI model and obtains "15ml" as output. This operation is performed for each abstract expression, converting them into concrete numerical values.

[0174] Step 4:

[0175] The device uses GPS to obtain its current location and then calls a weather API to retrieve weather information for that location.

[0176] Input: User's current location (GPS data)

[0177] Output: Weather information for your current location (e.g., temperature 10°C, humidity 70%)

[0178] Specific operation: The device activates its GPS module and obtains the latitude and longitude of its current location. Then, it sends a request to the weather API to obtain weather information such as temperature, humidity, and wind speed.

[0179] Step 5:

[0180] The device sends the acquired weather information to the server, which then adjusts the recipe values ​​based on this information.

[0181] Input: Weather information for your current location

[0182] Output: Adjusted recipe information

[0183] Specific operation: Based on the acquired climate information, the server applies specific rules to adjust the recipe values ​​(e.g., if the temperature is 15°C or below, add 1 ml of seasoning; if the humidity is 60% or above, add 30 seconds to the cooking time).

[0184] Step 6:

[0185] The adjusted recipe information is sent to the terminal and displayed on the terminal's display device. It is also displayed on the display devices in the physical store.

[0186] Input: Adjusted recipe information

[0187] Output: Screen showing the adjusted recipe instructions.

[0188] Specific operation: The terminal displays the adjusted recipe information received from the server on its screen and also transmits that information to the display device in the physical store.

[0189] Step 7:

[0190] The cooking staff will prepare the food according to the specific and adjusted recipe instructions provided.

[0191] Input: Adjusted recipe instructions

[0192] Output: High-quality cuisine

[0193] Specific actions: Cooking staff will follow the specific recipe instructions displayed on the terminal and display device to carry out the cooking procedure.

[0194] 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.

[0195] This invention is a cooking support system in which the user, terminal, server, and emotion engine work together to convert abstract recipe descriptions into concrete numerical values ​​and adjust the recipe based on the current location's climate information and the user's emotional state. This system is implemented in the following steps.

[0196] Overall system flow

[0197] 1. The user selects a cooking recipe.

[0198] The user launches the application and displays the cooking recipe list screen.

[0199] The user selects the "Chicken Curry" recipe.

[0200] The device sends the selected recipe information to the server.

[0201] 2. The server receives and analyzes the recipe.

[0202] The server retrieves the corresponding recipe information from the database based on the recipe ID received from the terminal.

[0203] The server analyzes the recipe content and detects abstract expressions such as "one tablespoon" or "cook over medium heat for 5 minutes."

[0204] List the detected expressions.

[0205] 3. The server uses AI to convert the data into specific numerical values.

[0206] The server passes the detected abstract representations to the generating AI as input.

[0207] The generating AI converts "one tablespoon" into specific numerical values ​​such as 15 ml, and "5 minutes over medium heat" into "5 minutes at heat level 3."

[0208] The server then reflects the specific numerical values ​​that have been converted into the recipe.

[0209] 4. The device acquires GPS information.

[0210] The device activates GPS to obtain the user's current location.

[0211] The device acquires its current location information and sends its latitude and longitude data to the server.

[0212] 5. The device obtains weather information for its current location.

[0213] The device calls a weather API to obtain weather information for its current location. Specifically, it retrieves meteorological data such as current temperature, humidity, and atmospheric pressure.

[0214] The device sends the weather information it has acquired to the server.

[0215] 6. The server adjusts the recipe based on weather information.

[0216] The server analyzes the weather information it has acquired.

[0217] The server adjusts the recipe values ​​by applying recipe adjustment rules (e.g., add 1 ml of seasoning if the temperature is below 15°C, add 30 seconds to the cooking time if the humidity is above 60%).

[0218] The server updates each step of the recipe based on specific numerical values ​​and generates the adjusted recipe data.

[0219] 7. The device uses an emotion engine.

[0220] The device activates an emotion engine to estimate the user's emotional state by analyzing their facial expressions, voice, and other data.

[0221] The emotion engine analyzes the user's emotional state and sends that information to the server.

[0222] 8. The server further adjusts the recipe based on emotional information.

[0223] The server receives emotional information and dynamically changes the cooking procedure and comments based on the user's emotional state.

[0224] For example, if a user is feeling stressed, simpler instructions or encouraging comments will be displayed.

[0225] 9. The device displays the adjusted recipe to the user.

[0226] The terminal receives the adjusted recipe data from the server.

[0227] The device displays the adjusted recipe on the cooking instructions screen. Specifically, it will display instructions such as, "Add chicken and 16ml of seasoning, and heat over medium heat (heat level 3) for 5 minutes and 30 seconds."

[0228] 10. The user cooks according to the adjusted recipe.

[0229] The user cooks according to the specific and adjusted recipe displayed.

[0230] By following precise steps and numerical values, users can achieve a high success rate in cooking.

[0231] Specific example

[0232] For example, when making "chicken curry," a typical recipe might instruct you to "use one tablespoon of curry powder" or "sauté over medium heat for 5 minutes." The AI ​​generator converts this into specific numbers, such as 15ml of curry powder or 5 minutes at heat level 3. Furthermore, if the current temperature is below 15℃, it adds 1ml of seasoning, and if the humidity is above 60%, it adds 30 seconds to the cooking time. In addition, if the emotion engine detects the user's stress level, it simplifies the cooking procedure and displays advice and encouraging comments such as "lower the temperature," allowing the user to cook in a relaxed state.

[0233] This system enables even cooking novices to achieve professional-level results while also offering the flexibility to adapt to different environmental conditions and the user's emotional state.

[0234] The following describes the processing flow.

[0235] Step 1:

[0236] The user launches the application and displays a list of cooking recipes. The user selects the "Chicken Curry" recipe from the list, and the device sends the selected recipe information to the server.

[0237] Step 2:

[0238] The server retrieves the corresponding recipe information from the database based on the recipe ID received from the terminal. The server analyzes the recipe content and detects abstract expressions such as "one tablespoon" or "cook over medium heat for 5 minutes." The detected expressions are then listed.

[0239] Step 3:

[0240] The server passes the detected abstract expressions to the generating AI as input. The generating AI converts "one tablespoon" into specific numerical values, such as 15 ml, and "5 minutes over medium heat" into "5 minutes at heat level 3." The server then incorporates these converted specific values ​​into the recipe.

[0241] Step 4:

[0242] The device activates GPS to obtain the user's current location. The device obtains the location information and sends the latitude and longitude data to the server.

[0243] Step 5:

[0244] The device calls a weather API to obtain weather information for its current location. Specifically, it retrieves meteorological data such as current temperature, humidity, and atmospheric pressure. The device then sends the obtained weather information to the server.

[0245] Step 6:

[0246] The server analyzes the weather information it has acquired. The server applies recipe adjustment rules (e.g., add 1 ml of seasoning if the temperature is below 15°C, add 30 seconds to the cooking time if the humidity is above 60%) to adjust the values ​​in the recipe. The server updates each step of the recipe based on the specific values ​​and generates the adjusted recipe data.

[0247] Step 7:

[0248] The device captures the user's facial expressions and voice, and uses an emotion engine to analyze the user's emotional state. The device then sends the results of the emotion engine's analysis to the server.

[0249] Step 8:

[0250] The server receives emotional information and further adjusts the recipe based on the user's emotional state. For example, if the user is feeling stressed, the server may simplify the cooking steps and add encouraging comments. Specifically, it might offer advice such as, "Lower the temperature and give yourself more time to relax."

[0251] Step 9:

[0252] The device receives the adjusted recipe data from the server. The device displays the adjusted recipe on the cooking procedure screen. Specifically, it displays "Add chicken and 16ml of seasoning, and heat over medium heat (heat level 3) for 5 minutes and 30 seconds."

[0253] Step 10:

[0254] The user cooks according to the displayed, specific, and adjusted recipe. By following the precise steps and numerical values, the user can achieve a high success rate in cooking. Furthermore, adjustments based on the user's emotional state can improve their satisfaction and sense of security during cooking.

[0255] Each of these steps helps the system to consistently create delicious dishes regardless of the user's environment or emotional state.

[0256] (Example 2)

[0257] 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".

[0258] Conventional cooking support systems have a problem in that abstract recipes are not translated into concrete numerical values, forcing users to rely on experience when cooking. Furthermore, because recipes are not adjusted to take into account the current climate information or the user's emotional state, there are issues such as inconsistent cooking quality and a failure to reduce user stress during cooking.

[0259] The identification processing performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for selecting a recipe that the user wants to cook, means for converting abstract expressions into concrete numerical values ​​using a generation AI, means for acquiring climate information of the current location using GPS and weather information, means for adjusting the numerical values ​​of the recipe based on the acquired climate information, means for analyzing the user's emotional state and further adjusting the recipe based on that information, and means for displaying the adjusted recipe to the user. As a result, even beginners in cooking can cook based on concrete numerical values, and by providing an optimal recipe that corresponds to climate information and emotional state, it is possible to improve the quality of the cooking and reduce user stress.

[0260] A "user" is the entity that uses the system to select a cooking recipe and then performs the cooking.

[0261] A "recipe" is information that describes the steps and ingredients for cooking, and includes specific numerical values ​​and instructions such as cooking time.

[0262] "Generative AI" refers to artificial intelligence technology that includes algorithms and models for converting abstract expressions into concrete numerical values.

[0263] "Abstract descriptions" are those that lack clear numerical values ​​or specific instructions, such as "one tablespoon" or "cook over medium heat for 5 minutes."

[0264] "Specific numerical values" refer to clear and measurable numbers such as 15ml or 5 minutes at heat level 3.

[0265] GPS is a global positioning system used by devices to obtain location information.

[0266] "Weather information" refers to meteorological data for a specific location, such as current temperature, humidity, and atmospheric pressure.

[0267] "Climate information" refers to data that indicates the environmental conditions of the current location based on GPS and weather information.

[0268] "Emotional state" refers to data that represents the user's stress level and emotional state, and is obtained through facial expression and voice analysis.

[0269] "Cooking instructions" refer to a set of steps and actions required to complete a dish.

[0270] The "adjusted recipe" refers to the final recipe, which has been refined and made concrete based on the acquired climate and sentiment data.

[0271] This invention is a system that improves the user's cooking experience through the cooperation of the user, terminal, server, and emotion analysis engine. This system converts abstract expressions of cooking recipes into concrete numerical values ​​and adjusts the recipes based on the user's current location's climate information and their emotional state.

[0272] Hardware and software to be used

[0273] This system utilizes devices such as smartphones and tablets, internet-connected servers, GPS modules, weather APIs, generative AI models, and sentiment analysis engines. Specifically, the following common hardware and software are used:

[0274] Devices: Smartphones, tablets, PCs

[0275] Server: A server on the internet

[0276] Generate AI model: AI algorithm (e.g., GPT-3)

[0277] Sentiment analysis engine: Facial expression recognition software, voice analysis tools

[0278] GPS module: Built-in GPS function of the terminal

[0279] Weather API: Meteorological data service (e.g., OpenWeatherMap API)

[0280] Data processing and data calculation

[0281] The user launches the application and selects a cooking recipe. The terminal sends the selected recipe information to the server. The server retrieves the recipe information from the database based on the received recipe ID and uses the generated AI model to convert the abstract representation into specific numerical values.

[0282] The terminal activates the GPS module to obtain the current location information and sends its latitude and longitude data to the server. The server calls the weather API based on this information to obtain the current weather information (temperature, humidity, atmospheric pressure, etc.). Then, based on the obtained weather information, the recipe numerical values are adjusted by applying pre-set rules.

[0283] Next, the terminal activates the sentiment analysis engine, analyzes the user's facial expressions and voice to estimate the emotional state, and sends the information to the server. The server further adjusts the recipe based on the received emotional information. At this time, if the user is feeling stressed, adjustments such as simplifying the procedure or adding encouraging comments are made.

[0284] Finally, the terminal displays the adjusted recipe to the user, and the user cooks according to it.

[0285] Specific examples and example prompt sentences

[0286] For example, when making "chicken curry", a normal recipe contains abstract instructions such as "use one tablespoon of curry powder" and "stir-fry over medium heat for 5 minutes". These are converted into specific numerical values by the generative AI model, resulting in forms such as 15 ml of curry powder and stir-fry for 5 minutes at heat level 3. Also, if the current local temperature is 15°C or lower, 1 ml of seasoning can be added, and if the humidity is 60% or higher, the cooking time can be increased by 30 seconds.

[0287] Furthermore, when the sentiment analysis engine detects the user's stress level, simple operations such as "lower the temperature" or encouraging comments are displayed to enable the user to cook in a relaxed state.

[0288] Examples of specific prompt sentences:

[0289] "Please convert the following abstract expressions in the recipe into specific numerical values. 'Put one tablespoon of curry powder', 'Stir-fry over medium heat for 5 minutes'"

[0290] "Adjust the recipe based on the current local climate information. The current temperature is 12°C and the humidity is 65%. Please apply the corresponding adjustment rules."

[0291] "The user is feeling stressed. Please simplify the cooking procedure and add encouraging comments."

[0292] With this system, even novice cooks can proceed with cooking based on accurate numerical values, obtain an optimal recipe corresponding to climate conditions and emotional states, and achieve higher-quality cooking.

[0293] The flow of the specific process in Example 2 will be described using FIG. 13.

[0294] Step 1:

[0295] The user selects a cooking recipe.

[0296] Specific operations:

[0297] The user launches the application and selects "Chicken Curry" from the list of cooking recipes.

[0298] The terminal obtains the selected recipe ID and sends a request containing the recipe ID to the server.

[0299] Input: Cooking recipe ID

[0300] Output: Request sent to the server

[0301] Step 2:

[0302] The server receives and analyzes the recipe.

[0303] Specific operations:

[0304] The server receives the recipe ID sent from the terminal.

[0305] The server obtains the corresponding recipe information from the database based on the recipe ID.

[0306] The server analyzes the recipe content and detects abstract expressions such as "one tablespoon" and "cook for 5 minutes over medium heat".

[0307] The server lists the detected abstract expressions.

[0308] Input: Recipe ID

[0309] Output: Listed abstract expressions

[0310] Step 3:

[0311] The server uses the generation AI to convert them into specific numerical values.

[0312] Specific operations:

[0313] The server inputs a list of abstract representations into an AI model that generates them.

[0314] The generating AI model converts "one tablespoon" into specific numerical values ​​such as 15 ml and "5 minutes over medium heat" into "5 minutes at heat level 3".

[0315] The server reflects the specific numerical values ​​it generates into the recipe data.

[0316] Input: Listed abstract expressions

[0317] Output: Recipe converted to specific numerical values

[0318] Step 4:

[0319] The device acquires GPS information.

[0320] Specific actions:

[0321] The device activates its GPS module and obtains the user's current location information.

[0322] The terminal sends the acquired latitude and longitude data to the server.

[0323] Input: None (Location information obtained via GPS module)

[0324] Output: Latitude and longitude data

[0325] Step 5:

[0326] The device obtains weather information for its current location.

[0327] Specific actions:

[0328] The device uses a weather API to obtain weather information (temperature, humidity, atmospheric pressure, etc.) for its current location.

[0329] The device sends the weather information it has acquired to the server.

[0330] Input: Latitude and longitude data

[0331] Output: Weather information

[0332] Step 6:

[0333] The server adjusts the recipe based on weather information.

[0334] Specific actions:

[0335] The server analyzes the weather information that has been sent.

[0336] The server adjusts the values ​​by applying recipe adjustment rules (e.g., add 1 ml of seasoning if the temperature is below 15°C, add 30 seconds to the cooking time if the humidity is above 60%).

[0337] The server generates the adjusted recipe data.

[0338] Input: Weather information

[0339] Output: Adjusted recipe data

[0340] Step 7:

[0341] The device uses an emotion engine.

[0342] Specific actions:

[0343] The device uses its camera and microphone to collect the user's facial expressions and voice in real time.

[0344] The emotion engine analyzes the user's emotional state and sends the data to the server.

[0345] Input: Facial expression data, audio data

[0346] Output: User sentiment information

[0347] Step 8:

[0348] The server further adjusts the recipe based on emotional information.

[0349] Specific actions:

[0350] The server analyzes the user's emotional information received from the emotion engine.

[0351] The server takes into account the user's stress level and makes changes such as simplifying the cooking procedure.

[0352] The server generates the recipe data after final adjustments.

[0353] Input: User sentiment information

[0354] Output: Recipe data after final adjustments

[0355] Step 9:

[0356] The device displays the adjusted recipe to the user.

[0357] Specific actions:

[0358] The terminal receives the finalized recipe data from the server.

[0359] Based on the data received by the device, the cooking procedure screen displays specific steps.

[0360] Input: Recipe data after final adjustments

[0361] Output: The final adjusted recipe displayed on the terminal.

[0362] Step 10:

[0363] The user cooks according to the adjusted recipe.

[0364] Specific actions:

[0365] The user reviews the specific and customized recipe displayed on their device.

[0366] The user follows the instructions to cook and ultimately complete a dish with a high success rate.

[0367] Input: Recipe after final adjustments

[0368] Output: Finished dish

[0369] (Application Example 2)

[0370] 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".

[0371] Conventional cooking support systems have the problem of being unable to reflect climate information or the user's emotional state, leading to decreased user satisfaction under certain conditions. Furthermore, when converting recipes and cooking procedures into concrete numerical values, they remain abstract, making it difficult for users to accurately reproduce the dishes. In addition, there is a lack of features for personalized cooking suggestions and adjustments, resulting in a failure to adequately meet user needs.

[0372] 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.

[0373] In this invention, the server includes means for selecting a recipe that the user wants to cook, means for converting abstract expressions into concrete numerical values ​​using a generative AI, means for acquiring climate information of the current location using GPS and weather information, means for adjusting the numerical values ​​of the recipe based on the acquired climate information, means for displaying the adjusted recipe to the user, means for analyzing the user's emotional state using an emotion analysis engine and acquiring the analysis results, means for dynamically adjusting the recipe and suggested comments based on the acquired emotional state, and means for suggesting dishes to the user based on the climate information and emotional state. As a result, the user can obtain specific cooking procedures that are appropriate to the climate information and emotional state, thereby increasing user satisfaction.

[0374] "A means for users to select a recipe they want to make" refers to a function that allows users to select a recipe for their desired dish through an interface.

[0375] "A method for converting abstract expressions into concrete numerical values ​​using generative AI" refers to a technology for converting abstract cooking expressions such as "one tablespoon" or "5 minutes over medium heat" into specific numerical values ​​such as milliliters or cooking time.

[0376] "Means for obtaining climate information for the current location using GPS and weather information" refers to a function that uses a GPS device and a weather API to obtain the user's current location's latitude, longitude, and climate information (temperature, humidity, etc.).

[0377] "Methods for adjusting recipe values ​​based on acquired climate information" refers to technologies that adjust recipe ingredients, cooking times, etc., based on acquired climate information for the current location.

[0378] "Means for displaying the adjusted recipe to the user" refers to a function for displaying the specific adjusted recipe content on the user's device (such as a smartphone or tablet).

[0379] "A means of analyzing a user's emotional state using an emotion analysis engine and obtaining the analysis results" refers to a technology that uses a smartphone's camera and microphone to analyze the user's facial expressions and voice, estimate their emotional state, and obtain the results.

[0380] "Means for dynamically adjusting recipes and suggested comments based on acquired emotional states" refers to a technology that dynamically changes and adjusts recipe procedures and provided comments based on the user's emotional state.

[0381] "Means for suggesting dishes to a user based on climate information and emotional state" refers to a function that automatically suggests dishes suitable for the user based on acquired climate information and emotional state.

[0382] This invention relates to a system that suggests and adjusts cooking recipes suitable for a user under specific conditions. This system operates using a smartphone, GPS module, weather API, sentiment analysis engine, generative AI model, and the like.

[0383] System Configuration

[0384] 1. User terminal

[0385] Smartphone: Used as a user interface to accept user input.

[0386] GPS module: Obtains the user's current location.

[0387] Camera and microphone: Use an emotion analysis engine to analyze the user's emotional state.

[0388] 2. Server

[0389] Database: Stores cooking recipes and related data.

[0390] Generative AI model: Converts abstract cooking instructions into concrete numerical values.

[0391] Weather API: Retrieves weather information for your current location from an external weather data service.

[0392] Emotion analysis engine: A software module for analyzing a user's emotional state.

[0393] Processing flow

[0394] 1. Recipe Selection

[0395] The server displays a list of cooking recipes through the user's terminal, and the user selects the recipe they want.

[0396] 2. Data Acquisition

[0397] The smartphone's GPS module is used to obtain current location information, and a weather API is used to obtain weather information (temperature, humidity, etc.) for the current location.

[0398] 3. Emotion analysis

[0399] Using the smartphone's camera and microphone, the emotion analysis engine analyzes the user's facial expressions and voice to estimate their emotional state.

[0400] 4. Recipe adjustment

[0401] The server uses a generated AI model to convert abstract recipe instructions into concrete numbers and adjusts the recipe content based on acquired weather information and emotional states.

[0402] 5. Presentation to the user

[0403] The adjusted recipe is displayed on the user's device, and specific cooking instructions and encouraging comments are provided to the user.

[0404] Examples of specific cases and prompt statements

[0405] For example, if a user wants to make "chicken curry," the initial recipe might include abstract instructions such as "use one tablespoon of curry powder" or "sauté over medium heat for 5 minutes." The server then uses a generative AI model to convert these into specific numerical values ​​(e.g., 15 ml of curry powder, 5 minutes at heat level 3).

[0406] Furthermore, if the current temperature is low, 1 ml of seasoning will be added, and if the humidity is high, 30 seconds will be added to the cooking time. In addition, if the emotion analysis engine determines that the user is stressed, the cooking procedure will be simplified and advice or encouraging comments such as "lower the temperature" will be displayed.

[0407] Example of a prompt:

[0408] "To suggest a cooking genre, please tell me what kind of dishes would be suitable when the current temperature is 25 degrees Celsius."

[0409] "Please suggest dishes that can help users relax when they are feeling stressed."

[0410] In this way, the system can suggest and adjust the optimal dish according to the user's environment and emotional state.

[0411] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0412] Step 1:

[0413] The user selects a recipe. The user terminal displays a list of cooking recipes provided by the server on its interface. The user selects the desired recipe, and the terminal sends that information to the server. The input is the recipe ID selected by the user, and the output is confirmation that the information has been sent to the server.

[0414] Step 2:

[0415] The server receives and analyzes recipe information. Based on the recipe ID received from the terminal, the server retrieves the corresponding recipe information from the database and identifies the abstract expression. It then calls a generative AI model to convert the abstract expression into concrete numerical values. The input is the recipe ID, and the output is the recipe information converted into concrete numerical values.

[0416] Step 3:

[0417] The terminal obtains the user's current location. It activates the GPS module on the user's terminal, acquires location information (latitude and longitude), and sends this information to the server. The input is GPS data, and the output is latitude and longitude information.

[0418] Step 4:

[0419] The device obtains weather information for its current location. The device calls a weather API to retrieve weather information such as temperature, humidity, and atmospheric pressure for its current location, and sends it to the server. The input is latitude and longitude information, and the output is weather information.

[0420] Step 5:

[0421] The server adjusts the recipe based on weather information. The server analyzes the acquired weather information and applies recipe adjustment rules to adjust specific values. The input is weather information, and the output is the adjusted recipe information.

[0422] Step 6:

[0423] The device activates the emotion analysis engine. The device uses its camera and microphone to capture the user's facial expressions and voice, and uses the emotion analysis engine to analyze the user's emotional state. The analysis results are sent to the server. The input is the user's facial expressions and voice data, and the output is the emotional state.

[0424] Step 7:

[0425] The server further adjusts the recipe based on emotional information. The server receives emotional information and dynamically changes the recipe content and cooking procedure according to the user's emotional state. The input is emotional information, and the output is the final adjusted recipe information.

[0426] Step 8:

[0427] The terminal displays the adjusted recipe to the user. The terminal displays the adjusted recipe information received from the server on its interface, providing the user with specific cooking instructions and suggested comments. The input is the final adjusted recipe information, and the output is what is displayed to the user.

[0428] 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.

[0429] 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.

[0430] 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.

[0431] [Second Embodiment]

[0432] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0433] 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.

[0434] 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).

[0435] 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.

[0436] 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.

[0437] 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).

[0438] 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.

[0439] 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.

[0440] 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.

[0441] 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.

[0442] 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.

[0443] 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".

[0444] This invention is a cooking support system in which a user, a terminal, and a server work together to convert abstract recipe descriptions into concrete numerical values ​​and further adjust the recipe based on climate information of the user's current location. This system is implemented in the following steps.

[0445] Overall system flow

[0446] 1. The user selects a cooking recipe.

[0447] The user launches the application and displays the cooking recipe list screen.

[0448] The user selects the "Chicken Curry" recipe.

[0449] The device sends the selected recipe information to the server.

[0450] 2. The server receives and analyzes the recipe.

[0451] The server retrieves recipe information from the database based on the recipe ID received from the terminal.

[0452] The server analyzes the recipe content and detects abstract expressions such as "one tablespoon" and "cook over medium heat for 5 minutes."

[0453] The detected expressions are listed and prepared for conversion.

[0454] 3. The server uses AI to convert the data into specific numerical values.

[0455] The server passes the detected abstract representations to the generating AI as input.

[0456] The generating AI converts "one tablespoon" into specific numerical values ​​such as 15 ml, and "5 minutes over medium heat" into "5 minutes at heat level 3."

[0457] The server then reflects the specific numerical values ​​that have been converted into the recipe.

[0458] 4. The device acquires GPS information.

[0459] The device activates GPS to obtain the user's current location.

[0460] The device obtains its current location information and sends it to the server.

[0461] 5. The device obtains weather information for its current location.

[0462] The device calls a weather API to obtain weather information for its current location.

[0463] The device sends the weather information it has acquired to the server.

[0464] 6. The server adjusts the recipe based on weather information.

[0465] The server analyzes weather information and applies recipe adjustment rules (e.g., add 1 ml of seasoning if the temperature is below 15°C, or add 30 seconds to the cooking time if the humidity is above 60%).

[0466] The server adds adjustment values ​​based on weather information to recipes that have specific numerical values.

[0467] Update each step of the recipe and generate the adjusted recipe data.

[0468] 7. The device displays the adjusted recipe to the user.

[0469] The terminal receives the adjusted recipe data from the server.

[0470] The device displays the adjusted recipe on the cooking instructions screen. For example, it might say, "Add chicken and 16ml of seasoning, and heat over medium heat (heat level 3) for 5 minutes and 30 seconds."

[0471] 8. The user cooks according to the adjusted recipe.

[0472] The user cooks according to the specific and adjusted recipe displayed.

[0473] By following precise steps and numerical values, users can achieve a high success rate in cooking.

[0474] Specific example

[0475] For example, when making "chicken curry," a typical recipe might instruct you to "use one tablespoon of curry powder" or "sauté over medium heat for 5 minutes." The AI ​​then converts this into specific values ​​such as 15ml of curry powder and 5 minutes at heat level 3. Furthermore, it makes adjustments such as adding 1ml of seasoning if the current temperature is below 15℃, or adding 30 seconds to the cooking time if the humidity is above 60%, ensuring that delicious food can be consistently produced in any environment.

[0476] This system enables even cooking novices to achieve professional-level results while also offering the flexibility to adapt to different environmental conditions.

[0477] The following describes the processing flow.

[0478] Step 1:

[0479] The user launches the application and displays a list of cooking recipes. The user selects the "Chicken Curry" recipe from the list, and the device sends the selected recipe information to the server.

[0480] Step 2:

[0481] The server retrieves the corresponding recipe information from the database based on the recipe ID received from the terminal. The server analyzes the recipe content and detects abstract expressions such as "one tablespoon" or "cook over medium heat for 5 minutes." The detected expressions are then listed.

[0482] Step 3:

[0483] The server passes the detected abstract expressions to the generating AI as input. The generating AI converts "one tablespoon" into specific numerical values, such as 15 ml, and "5 minutes over medium heat" into "5 minutes at heat level 3." The server then incorporates these converted specific values ​​into the recipe.

[0484] Step 4:

[0485] The device activates GPS to obtain the user's current location. The device obtains the location information and sends the latitude and longitude data to the server.

[0486] Step 5:

[0487] The device calls a weather API to obtain weather information for its current location. Specifically, it retrieves meteorological data such as current temperature, humidity, and atmospheric pressure. The device then sends the obtained weather information to the server.

[0488] Step 6:

[0489] The server analyzes the weather information it has acquired. The server applies recipe adjustment rules (e.g., add 1 ml of seasoning if the temperature is below 15°C, add 30 seconds to the cooking time if the humidity is above 60%) to adjust the values ​​in the recipe. The server updates each step of the recipe based on the specific values ​​and generates the adjusted recipe data.

[0490] Step 7:

[0491] The terminal receives the adjusted recipe data from the server. The terminal displays the adjusted recipe to the user. Specifically, it displays "Add chicken and 16ml of seasoning, and heat over medium heat (heat level 3) for 5 minutes and 30 seconds."

[0492] Step 8:

[0493] The user cooks according to the specific and adjusted recipe displayed. By following the precise steps and numerical values, the user can achieve a high success rate in cooking.

[0494] Each of these steps helps the system to support users in consistently creating delicious meals, regardless of the environment.

[0495] (Example 1)

[0496] 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."

[0497] In recent years, there has been a growing need for easy-to-use cooking methods that allow even users with limited culinary knowledge and experience to prepare delicious meals. However, the results are often inconsistent due to abstract recipe instructions and environmental differences (such as climate variations). Furthermore, there is no clear method for incorporating acquired climate information into recipes. Therefore, there is a need to develop a system that enables anyone to consistently produce high-quality meals.

[0498] 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.

[0499] In this invention, the server includes means for the user to select a recipe they wish to make, means for converting abstract expressions into concrete numerical values ​​using a generative AI model, and means for acquiring environmental information of the current location using GPS and climate data. This allows the user to be provided with an adjusted recipe indicated by concrete numerical values, enabling the creation of consistently delicious dishes even under different climate conditions.

[0500] A "user" is someone who uses the system to create a dish.

[0501] A "recipe to be made" refers to the steps of a dish that the user selects and which the system then processes.

[0502] A "generative AI model" is an artificial intelligence model designed to give concrete form to abstract expressions that are input into it.

[0503] "Abstract expressions" refer to expressions in a recipe that do not specify concrete numerical values. Examples: "one tablespoon," "cook over medium heat for 5 minutes," etc.

[0504] "Specific numerical values" refer to numbers that accurately represent the cooking steps and quantities in a recipe. Examples: "15ml", "5 minutes at heat level 3", etc.

[0505] GPS is a global positioning system used to determine a user's current location.

[0506] "Climate data" refers to data that includes environmental information such as temperature, humidity, and precipitation.

[0507] "Environmental information" refers to climate data for the current location.

[0508] "Adjusting" means changing the numerical values ​​of a recipe based on the acquired environmental information.

[0509] "Data storage means" refers to a method or apparatus for saving and reusing the numerical values ​​of a modified recipe.

[0510] "Multiple rules" refers to several criteria or guidelines for adjusting recipe values ​​based on acquired environmental information.

[0511] A "prompt message" is a text input to a generative AI model, intended to prompt the model to transform abstract expressions.

[0512] This invention is a system in which a user, a terminal, and a server work together to convert cooking recipes into specific numerical values ​​and further adjust the recipes based on climate information of the current location. The following hardware and software are used to implement this system.

[0513] The user launches the application on a device such as a smartphone or tablet. This application allows the user to access a screen where they can select a cooking recipe. For example, if the user selects the "Chicken Curry" recipe, the device sends that information to the server. The server retrieves the recipe information from its database based on the recipe ID and analyzes the recipe content. From the analyzed recipe content, it detects abstract expressions such as "one tablespoon" and "cook over medium heat for 5 minutes."

[0514] The server uses a generative AI model (e.g., GPT-4) to convert this abstract expression into concrete numerical values. Input to this AI model is in the form of prompt statements. For example, if the prompt statement "Convert the expression 'one tablespoon' into a concrete numerical value" is passed to the generative AI model, "one tablespoon" will be converted to 15 ml, and "5 minutes over medium heat" will be converted to "5 minutes at heat level 3". The server then reflects these conversion results in the original recipe.

[0515] Furthermore, the device uses GPS functionality to obtain the user's current location. Based on this location information, the device calls a weather API (for example, the OpenWeatherMap API) to obtain climate data such as temperature and humidity at the current location. This climate data is sent to a server, which performs analysis. Based on the results of this analysis, the recipe is adjusted. For example, if the temperature is below 15°C, the amount of seasoning is increased by 1 ml, and if the humidity is above 60%, the cooking time is increased by 30 seconds, according to these rules.

[0516] The adjusted recipe is sent back from the server to the terminal and displayed on the terminal. The displayed recipe includes specific numerical values, such as "Add chicken and 16 ml of seasoning, and heat over medium heat (heat level 3) for 5 minutes and 30 seconds." The user then cooks according to these instructions.

[0517] This system enables even cooking novices to achieve professional-level results while also offering the flexibility to adapt to different environmental conditions.

[0518] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0519] Step 1:

[0520] The user selects a cooking recipe. The user launches the application on their device and selects "Chicken Curry" from the displayed list of cooking recipes. The device receives the selected recipe ID as input and sends it to the server. Specifically, the user performs a tap operation, and the device generates an HTTP request and sends the recipe ID to the server.

[0521] Step 2:

[0522] The server receives and analyzes the recipe. The server uses the recipe ID received from the terminal as input to retrieve the corresponding recipe information from the database. The retrieved recipe information includes abstract expressions such as "one tablespoon" and "5 minutes over medium heat." The server analyzes these abstract expressions and creates a list. Data processing involves string manipulation to extract abstract keywords.

[0523] Step 3:

[0524] The server uses a generative AI model to convert abstract expressions into concrete numerical values. The server generates a prompt and passes the abstract expression as input to the generative AI model. An example of such a prompt is, "Please convert the expression 'one tablespoon' into a concrete numerical value." Based on the prompt, the generative AI model converts "one tablespoon" to 15 ml and "5 minutes over medium heat" to "5 minutes at heat level 3," and outputs these to the server. The server then incorporates this output into the original recipe.

[0525] Step 4:

[0526] The device acquires GPS information. While the user carries the device, the device activates its internal GPS module and acquires current location information as input. The acquired latitude and longitude information is sent to the server in HTTP request format. Specifically, the device activates its GPS sensor, periodically captures location information, and sends it to the server.

[0527] Step 5:

[0528] The device obtains weather information for its current location. Based on this location information, the device calls a weather API to obtain environmental information such as temperature and humidity. For example, it uses the OpenWeatherMap API. This obtained climate data is sent to the server as input. The device generates an HTTP request, sends the request to the weather API, parses the resulting JSON data, extracts the necessary information, and sends it to the server.

[0529] Step 6:

[0530] The server analyzes weather information and adjusts recipes accordingly. The server analyzes environmental information such as temperature and humidity. Based on this analysis, the server applies recipe adjustment rules. For example, if the temperature is below 15°C, it increases the amount of seasoning by 1 ml, and if the humidity is above 60%, it increases the cooking time by 30 seconds. The server uses these adjusted values ​​to update the recipe and generate new recipe data. Data processing involves adding or updating numerical values ​​in the recipe data.

[0531] Step 7:

[0532] The terminal displays the adjusted recipe to the user. The terminal receives the adjusted recipe data from the server as input and displays specific numerical values ​​and instructions on the cooking procedure screen. For example, it might display, "Add chicken and 16ml of seasoning, and heat over medium heat (heat level 3) for 5 minutes and 30 seconds." The terminal parses the received JSON data and displays it on the screen.

[0533] Step 8:

[0534] The user cooks according to the adjusted recipe. The user follows the specific and adjusted recipe displayed on the device. The user performs specific actions such as using measuring cups and adjusting the heat, and completes the dish according to the instructions. By following the appropriate procedures and values, the user can produce dishes of consistent quality.

[0535] (Application Example 1)

[0536] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0537] Traditional cooking recipes often standardize factors such as climate conditions, location, specific ingredient amounts, and cooking times, which can lead to variations in the quality and final result of dishes. Furthermore, the consistency of dishes in physical restaurants is often inconsistent, due to differences among cooking staff, making it difficult to provide customers with consistent quality. To address these issues, a system is needed that adjusts recipes based on real-time climate information and provides cooking staff with specific and consistent instructions.

[0538] 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.

[0539] In this invention, the server includes means for the user to select a recipe they wish to make, means for converting abstract expressions into concrete numerical values ​​using a generative AI, means for acquiring climate information of the current location using GPS and weather information, means for adjusting the numerical values ​​of the recipe based on the acquired climate information, means for displaying the adjusted recipe to the user, and means for displaying the adjusted recipe on a display device in a physical store. This makes it possible to consistently provide high-quality food in any environment.

[0540] A "user" is an individual or a kitchen staff member at a physical restaurant who selects a recipe and uses the system to prepare a meal.

[0541] A "recipe" is a document that contains specific instructions and information about ingredients for preparing a dish.

[0542] "Generative AI" is an artificial intelligence model that converts abstract expressions into concrete numbers or instructions.

[0543] "Abstract expressions" are those that lack specific numerical values, such as "one tablespoon" or "5 minutes over medium heat."

[0544] "Specific numerical values" refer to clear, quantified instructions such as "15ml" or "5 minutes at heat level 3" that are converted by the generating AI.

[0545] GPS is a global positioning system used to determine one's location on Earth.

[0546] "Weather information" refers to meteorological data such as temperature, humidity, and wind speed at your current location.

[0547] "Climate information" refers to environmental data based on acquired weather information.

[0548] "Adjusting the values" means changing the amount of ingredients or cooking time in a recipe based on climate information.

[0549] A "display device" is a screen device used to display the adjusted recipe and cooking instructions.

[0550] This invention is a system for converting abstract recipe instructions into concrete numerical values ​​when a user is cooking, and further adjusting the recipe based on climate information of the user's current location. Specifically, it is implemented in the following steps.

[0551] First, the user launches the application using a device (such as a smartphone or tablet) and displays a list of cooking recipes. When the user selects a recipe they want to make, the recipe information is sent from the device to the server. The server retrieves the corresponding recipe information from the database based on the recipe ID and analyzes the recipe content. At this time, the server uses a generation AI to detect abstract expressions such as "one tablespoon" or "5 minutes over medium heat" and converts them into specific numerical values ​​such as "15 ml" or "5 minutes at heat level 3".

[0552] Next, the device uses GPS to obtain the user's current location and calls a weather API (e.g., WeatherAPI) to retrieve weather information for the current location. The obtained climate information is sent from the device to the server, which adjusts the recipe values ​​based on this information. Specifically, rules such as adding 1 ml of seasoning if the temperature is below 15°C, or adding 30 seconds to the cooking time if the humidity is above 60%, are applied.

[0553] The adjusted recipe is sent from the server to the terminal and displayed on the terminal's display device. This display is also shown on the display device in the physical store. By following the displayed, specific, and adjusted recipe instructions, the cooking staff can consistently provide high-quality dishes that are adapted to the environment.

[0554] The hardware required includes a smartphone, tablet, display device, GPS module, and an internet connection to obtain weather information. The software used includes a recipe database, a generative AI model, a weather API, and a dedicated cooking assistance application.

[0555] As a concrete example, if a user wants to make "chicken curry," and uses the application to confirm their current location is Tokyo, and obtains weather information indicating a temperature of 10°C and humidity of 70%, the original recipe instruction of "one tablespoon of curry powder" will be converted to "16 ml," and "sauté over medium heat for 5 minutes" will be adjusted to "sauté over medium heat (heat level 3) for 5 minutes and 30 seconds."

[0556] Examples of prompt statements to input into the generative AI model are as follows:

[0557] Example of a prompt:

[0558] Input: "One tablespoon"

[0559] Output: "15ml"

[0560] Input: "5 minutes over medium heat"

[0561] Output: "5 minutes with firepower level 3"

[0562] This will enable us to consistently provide high-quality food in our physical stores as well.

[0563] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0564] Step 1:

[0565] The user launches the application and displays a list of cooking recipes. When the user selects a cooking recipe they want to make, the recipe information is sent from the device to the server.

[0566] Input: User-selected recipe ID

[0567] Output: Recipe ID to send to the server

[0568] Specific operation: When the user selects "Chicken Curry," the device automatically sends its recipe ID to the server.

[0569] Step 2:

[0570] Based on the recipe ID received from the terminal, the server retrieves the corresponding recipe information from the database and analyzes the recipe content.

[0571] Input: Recipe ID sent to the server

[0572] Output: Recipe information (including abstract expressions)

[0573] Specific operation: The server executes a database query to retrieve recipe information corresponding to the recipe ID. It then analyzes the retrieved recipe content and lists abstract expressions such as "one tablespoon" or "cook over medium heat for 5 minutes."

[0574] Step 3:

[0575] The server takes abstract representations as input to the generated AI model and converts them into concrete numerical values.

[0576] Input: Abstract expressions (e.g., "one tablespoon", "cook over medium heat for 5 minutes")

[0577] Output: Specific numerical values ​​(e.g., "15ml", "5 minutes at heat level 3")

[0578] Specific operation: The server takes prompt text such as "one tablespoon" as input to the generating AI model and obtains "15ml" as output. This operation is performed for each abstract expression, converting them into concrete numerical values.

[0579] Step 4:

[0580] The device uses GPS to obtain its current location and then calls a weather API to retrieve weather information for that location.

[0581] Input: User's current location (GPS data)

[0582] Output: Weather information for your current location (e.g., temperature 10°C, humidity 70%)

[0583] Specific operation: The device activates its GPS module and obtains the latitude and longitude of its current location. Then, it sends a request to the weather API to obtain weather information such as temperature, humidity, and wind speed.

[0584] Step 5:

[0585] The device sends the acquired weather information to the server, which then adjusts the recipe values ​​based on this information.

[0586] Input: Weather information for your current location

[0587] Output: Adjusted recipe information

[0588] Specific operation: Based on the acquired climate information, the server applies specific rules to adjust the recipe values ​​(e.g., if the temperature is 15°C or below, add 1 ml of seasoning; if the humidity is 60% or above, add 30 seconds to the cooking time).

[0589] Step 6:

[0590] The adjusted recipe information is sent to the terminal and displayed on the terminal's display device. It is also displayed on the display devices in the physical store.

[0591] Input: Adjusted recipe information

[0592] Output: Screen showing the adjusted recipe instructions.

[0593] Specific operation: The terminal displays the adjusted recipe information received from the server on its screen and also transmits that information to the display device in the physical store.

[0594] Step 7:

[0595] The cooking staff will prepare the food according to the specific and adjusted recipe instructions provided.

[0596] Input: Adjusted recipe instructions

[0597] Output: High-quality cuisine

[0598] Specific actions: Cooking staff will follow the specific recipe instructions displayed on the terminal and display device to carry out the cooking procedure.

[0599] 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.

[0600] This invention is a cooking support system in which the user, terminal, server, and emotion engine work together to convert abstract recipe descriptions into concrete numerical values ​​and adjust the recipe based on the current location's climate information and the user's emotional state. This system is implemented in the following steps.

[0601] Overall system flow

[0602] 1. The user selects a cooking recipe.

[0603] The user launches the application and displays the cooking recipe list screen.

[0604] The user selects the "Chicken Curry" recipe.

[0605] The device sends the selected recipe information to the server.

[0606] 2. The server receives and analyzes the recipe.

[0607] The server retrieves the corresponding recipe information from the database based on the recipe ID received from the terminal.

[0608] The server analyzes the recipe content and detects abstract expressions such as "one tablespoon" or "cook over medium heat for 5 minutes."

[0609] List the detected expressions.

[0610] 3. The server uses AI to convert the data into specific numerical values.

[0611] The server passes the detected abstract representations to the generating AI as input.

[0612] The generating AI converts "one tablespoon" into specific numerical values ​​such as 15 ml, and "5 minutes over medium heat" into "5 minutes at heat level 3."

[0613] The server then reflects the specific numerical values ​​that have been converted into the recipe.

[0614] 4. The device acquires GPS information.

[0615] The device activates GPS to obtain the user's current location.

[0616] The device acquires its current location information and sends its latitude and longitude data to the server.

[0617] 5. The device obtains weather information for its current location.

[0618] The device calls a weather API to obtain weather information for its current location. Specifically, it retrieves meteorological data such as current temperature, humidity, and atmospheric pressure.

[0619] The device sends the weather information it has acquired to the server.

[0620] 6. The server adjusts the recipe based on weather information.

[0621] The server analyzes the weather information it has acquired.

[0622] The server adjusts the recipe values ​​by applying recipe adjustment rules (e.g., add 1 ml of seasoning if the temperature is below 15°C, add 30 seconds to the cooking time if the humidity is above 60%).

[0623] The server updates each step of the recipe based on specific numerical values ​​and generates the adjusted recipe data.

[0624] 7. The device uses an emotion engine.

[0625] The device activates an emotion engine to estimate the user's emotional state by analyzing their facial expressions, voice, and other data.

[0626] The emotion engine analyzes the user's emotional state and sends that information to the server.

[0627] 8. The server further adjusts the recipe based on emotional information.

[0628] The server receives emotional information and dynamically changes the cooking procedure and comments based on the user's emotional state.

[0629] For example, if a user is feeling stressed, simpler instructions or encouraging comments will be displayed.

[0630] 9. The device displays the adjusted recipe to the user.

[0631] The terminal receives the adjusted recipe data from the server.

[0632] The device displays the adjusted recipe on the cooking instructions screen. Specifically, it will display instructions such as, "Add chicken and 16ml of seasoning, and heat over medium heat (heat level 3) for 5 minutes and 30 seconds."

[0633] 10. The user cooks according to the adjusted recipe.

[0634] The user cooks according to the specific and adjusted recipe displayed.

[0635] By following precise steps and numerical values, users can achieve a high success rate in cooking.

[0636] Specific example

[0637] For example, when making "chicken curry," a typical recipe might instruct you to "use one tablespoon of curry powder" or "sauté over medium heat for 5 minutes." The AI ​​generator converts this into specific numbers, such as 15ml of curry powder or 5 minutes at heat level 3. Furthermore, if the current temperature is below 15℃, it adds 1ml of seasoning, and if the humidity is above 60%, it adds 30 seconds to the cooking time. In addition, if the emotion engine detects the user's stress level, it simplifies the cooking procedure and displays advice and encouraging comments such as "lower the temperature," allowing the user to cook in a relaxed state.

[0638] This system enables even cooking novices to achieve professional-level results while also offering the flexibility to adapt to different environmental conditions and the user's emotional state.

[0639] The following describes the processing flow.

[0640] Step 1:

[0641] The user launches the application and displays a list of cooking recipes. The user selects the "Chicken Curry" recipe from the list, and the device sends the selected recipe information to the server.

[0642] Step 2:

[0643] The server retrieves the corresponding recipe information from the database based on the recipe ID received from the terminal. The server analyzes the recipe content and detects abstract expressions such as "one tablespoon" or "cook over medium heat for 5 minutes." The detected expressions are then listed.

[0644] Step 3:

[0645] The server passes the detected abstract expressions to the generating AI as input. The generating AI converts "one tablespoon" into specific numerical values, such as 15 ml, and "5 minutes over medium heat" into "5 minutes at heat level 3." The server then incorporates these converted specific values ​​into the recipe.

[0646] Step 4:

[0647] The device activates GPS to obtain the user's current location. The device obtains the location information and sends the latitude and longitude data to the server.

[0648] Step 5:

[0649] The device calls a weather API to obtain weather information for its current location. Specifically, it retrieves meteorological data such as current temperature, humidity, and atmospheric pressure. The device then sends the obtained weather information to the server.

[0650] Step 6:

[0651] The server analyzes the weather information it has acquired. The server applies recipe adjustment rules (e.g., add 1 ml of seasoning if the temperature is below 15°C, add 30 seconds to the cooking time if the humidity is above 60%) to adjust the values ​​in the recipe. The server updates each step of the recipe based on the specific values ​​and generates the adjusted recipe data.

[0652] Step 7:

[0653] The device captures the user's facial expressions and voice, and uses an emotion engine to analyze the user's emotional state. The device then sends the results of the emotion engine's analysis to the server.

[0654] Step 8:

[0655] The server receives emotional information and further adjusts the recipe based on the user's emotional state. For example, if the user is feeling stressed, the server may simplify the cooking steps and add encouraging comments. Specifically, it might offer advice such as, "Lower the temperature and give yourself more time to relax."

[0656] Step 9:

[0657] The device receives the adjusted recipe data from the server. The device displays the adjusted recipe on the cooking procedure screen. Specifically, it displays "Add chicken and 16ml of seasoning, and heat over medium heat (heat level 3) for 5 minutes and 30 seconds."

[0658] Step 10:

[0659] The user cooks according to the displayed, specific, and adjusted recipe. By following the precise steps and numerical values, the user can achieve a high success rate in cooking. Furthermore, adjustments based on the user's emotional state can improve their satisfaction and sense of security during cooking.

[0660] Each of these steps helps the system to consistently create delicious dishes regardless of the user's environment or emotional state.

[0661] (Example 2)

[0662] 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".

[0663] Conventional cooking support systems have a problem in that abstract recipes are not translated into concrete numerical values, forcing users to rely on experience when cooking. Furthermore, because recipes are not adjusted to take into account the current climate information or the user's emotional state, there are issues such as inconsistent cooking quality and a failure to reduce user stress during cooking.

[0664] The identification processing performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for selecting a recipe that the user wants to cook, means for converting abstract expressions into concrete numerical values ​​using a generation AI, means for acquiring climate information of the current location using GPS and weather information, means for adjusting the numerical values ​​of the recipe based on the acquired climate information, means for analyzing the user's emotional state and further adjusting the recipe based on that information, and means for displaying the adjusted recipe to the user. As a result, even beginners in cooking can cook based on concrete numerical values, and by providing an optimal recipe that corresponds to climate information and emotional state, it is possible to improve the quality of the cooking and reduce user stress.

[0665] A "user" is the entity that uses the system to select a cooking recipe and then performs the cooking.

[0666] A "recipe" is information that describes the steps and ingredients for cooking, and includes specific numerical values ​​and instructions such as cooking time.

[0667] "Generative AI" refers to artificial intelligence technology that includes algorithms and models for converting abstract expressions into concrete numerical values.

[0668] "Abstract descriptions" are those that lack clear numerical values ​​or specific instructions, such as "one tablespoon" or "cook over medium heat for 5 minutes."

[0669] "Specific numerical values" refer to clear and measurable numbers such as 15ml or 5 minutes at heat level 3.

[0670] GPS is a global positioning system used by devices to obtain location information.

[0671] "Weather information" refers to meteorological data for a specific location, such as current temperature, humidity, and atmospheric pressure.

[0672] "Climate information" refers to data that indicates the environmental conditions of the current location based on GPS and weather information.

[0673] "Emotional state" refers to data that represents the user's stress level and emotional state, and is obtained through facial expression and voice analysis.

[0674] "Cooking instructions" refer to a set of steps and actions required to complete a dish.

[0675] The "adjusted recipe" refers to the final recipe, which has been refined and made concrete based on the acquired climate and sentiment data.

[0676] This invention is a system that improves the user's cooking experience through the cooperation of the user, terminal, server, and emotion analysis engine. This system converts abstract expressions of cooking recipes into concrete numerical values ​​and adjusts the recipes based on the user's current location's climate information and their emotional state.

[0677] Hardware and software to be used

[0678] This system utilizes devices such as smartphones and tablets, internet-connected servers, GPS modules, weather APIs, generative AI models, and sentiment analysis engines. Specifically, the following common hardware and software are used:

[0679] Devices: Smartphones, tablets, PCs

[0680] Server: A server on the internet

[0681] Generative AI models: AI algorithms (e.g., GPT-3)

[0682] Emotion analysis engine: Facial expression recognition software, voice analysis tool

[0683] GPS module: GPS function built into the device

[0684] Weather API: Weather data service (e.g., OpenWeatherMap API)

[0685] Data processing and data calculation

[0686] The user launches the application and selects a cooking recipe. The device sends the selected recipe information to the server. The server retrieves the recipe information from its database based on the received recipe ID and uses a generative AI model to convert the abstract representation into concrete numerical values.

[0687] The device activates its GPS module to obtain its current location and sends the latitude and longitude data to the server. The server then uses this information to call a weather API to obtain weather information for the current location (temperature, humidity, atmospheric pressure, etc.). Based on the obtained weather information, it adjusts the recipe values ​​by applying pre-configured rules.

[0688] Next, the device activates its emotion analysis engine, analyzes the user's facial expressions and voice to estimate their emotional state, and sends this information to the server. The server then adjusts the recipe based on the received emotional information. If the user is experiencing stress, the server may simplify the procedure or add encouraging comments.

[0689] Finally, the device displays the adjusted recipe to the user, who then cooks according to it.

[0690] Examples of specific cases and prompt statements

[0691] For example, when making "chicken curry," a typical recipe includes abstract instructions such as "use one tablespoon of curry powder" or "sauté over medium heat for 5 minutes." The generative AI model converts these into specific numerical values, such as "15 ml of curry powder" or "5 minutes at heat level 3." Additionally, if the current temperature is below 15°C, 1 ml of seasoning is added, and if the humidity is above 60%, 30 seconds of cooking time is added.

[0692] Furthermore, if the emotion analysis engine detects a user's stress level, it will display simple actions such as "lower the temperature" or encouraging comments, allowing the user to cook in a relaxed state.

[0693] Examples of specific prompt messages:

[0694] "Please translate the following abstract phrases in the recipe into specific numbers: 'Add one tablespoon of curry powder,' 'Sauté over medium heat for 5 minutes.'"

[0695] "The recipe will be adjusted based on your current location's climate information. The current temperature is 12°C and the humidity is 65%. Please apply the corresponding adjustment rules."

[0696] "Users are feeling stressed. Please simplify the cooking instructions and add encouraging comments."

[0697] This system allows even cooking beginners to proceed with cooking based on precise numerical data, obtain optimal recipes that take into account climate conditions and emotional states, and ultimately achieve higher quality dishes.

[0698] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0699] Step 1:

[0700] The user selects a cooking recipe.

[0701] Specific actions:

[0702] The user launches the application and selects "Chicken Curry" from the list of recipes.

[0703] The device retrieves the selected recipe ID and sends a request containing that recipe ID to the server.

[0704] Input: Recipe ID

[0705] Output: Request sent to the server

[0706] Step 2:

[0707] The server receives and analyzes the recipe.

[0708] Specific actions:

[0709] The server receives the recipe ID sent from the terminal.

[0710] The server retrieves the corresponding recipe information from the database based on the recipe ID.

[0711] The server analyzes the recipe content and detects abstract expressions such as "one tablespoon" or "cook over medium heat for 5 minutes."

[0712] The server lists the detected abstract representations.

[0713] Input: Recipe ID

[0714] Output: Listed abstraction

[0715] Step 3:

[0716] The server uses AI generation to convert the data into specific numerical values.

[0717] Specific actions:

[0718] The server inputs a list of abstract representations into an AI model that generates them.

[0719] The generating AI model converts "one tablespoon" into specific numerical values ​​such as 15 ml and "5 minutes over medium heat" into "5 minutes at heat level 3".

[0720] The server reflects the specific numerical values ​​it generates into the recipe data.

[0721] Input: Listed abstract expressions

[0722] Output: Recipe converted to specific numerical values

[0723] Step 4:

[0724] The device acquires GPS information.

[0725] Specific actions:

[0726] The device activates its GPS module and obtains the user's current location information.

[0727] The terminal sends the acquired latitude and longitude data to the server.

[0728] Input: None (Location information obtained via GPS module)

[0729] Output: Latitude and longitude data

[0730] Step 5:

[0731] The device obtains weather information for its current location.

[0732] Specific actions:

[0733] The device uses a weather API to obtain weather information (temperature, humidity, atmospheric pressure, etc.) for its current location.

[0734] The device sends the weather information it has acquired to the server.

[0735] Input: Latitude and longitude data

[0736] Output: Weather information

[0737] Step 6:

[0738] The server adjusts the recipe based on weather information.

[0739] Specific actions:

[0740] The server analyzes the weather information that has been sent.

[0741] The server adjusts the values ​​by applying recipe adjustment rules (e.g., add 1 ml of seasoning if the temperature is below 15°C, add 30 seconds to the cooking time if the humidity is above 60%).

[0742] The server generates the adjusted recipe data.

[0743] Input: Weather information

[0744] Output: Adjusted recipe data

[0745] Step 7:

[0746] The device uses an emotion engine.

[0747] Specific actions:

[0748] The device uses its camera and microphone to collect the user's facial expressions and voice in real time.

[0749] The emotion engine analyzes the user's emotional state and sends the data to the server.

[0750] Input: Facial expression data, audio data

[0751] Output: User sentiment information

[0752] Step 8:

[0753] The server further adjusts the recipe based on emotional information.

[0754] Specific actions:

[0755] The server analyzes the user's emotional information received from the emotion engine.

[0756] The server takes into account the user's stress level and makes changes such as simplifying the cooking procedure.

[0757] The server generates the recipe data after final adjustments.

[0758] Input: User sentiment information

[0759] Output: Recipe data after final adjustments

[0760] Step 9:

[0761] The device displays the adjusted recipe to the user.

[0762] Specific actions:

[0763] The terminal receives the finalized recipe data from the server.

[0764] Based on the data received by the device, the cooking procedure screen displays specific steps.

[0765] Input: Recipe data after final adjustments

[0766] Output: The final adjusted recipe displayed on the terminal.

[0767] Step 10:

[0768] The user cooks according to the adjusted recipe.

[0769] Specific actions:

[0770] The user reviews the specific and customized recipe displayed on their device.

[0771] The user follows the instructions to cook and ultimately complete a dish with a high success rate.

[0772] Input: Recipe after final adjustments

[0773] Output: Finished dish

[0774] (Application Example 2)

[0775] 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."

[0776] Conventional cooking support systems have the problem of being unable to reflect climate information or the user's emotional state, leading to decreased user satisfaction under certain conditions. Furthermore, when converting recipes and cooking procedures into concrete numerical values, they remain abstract, making it difficult for users to accurately reproduce the dishes. In addition, there is a lack of features for personalized cooking suggestions and adjustments, resulting in a failure to adequately meet user needs.

[0777] 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.

[0778] In this invention, the server includes means for selecting a recipe that the user wants to cook, means for converting abstract expressions into concrete numerical values ​​using a generative AI, means for acquiring climate information of the current location using GPS and weather information, means for adjusting the numerical values ​​of the recipe based on the acquired climate information, means for displaying the adjusted recipe to the user, means for analyzing the user's emotional state using an emotion analysis engine and acquiring the analysis results, means for dynamically adjusting the recipe and suggested comments based on the acquired emotional state, and means for suggesting dishes to the user based on the climate information and emotional state. As a result, the user can obtain specific cooking procedures that are appropriate to the climate information and emotional state, thereby increasing user satisfaction.

[0779] "A means for users to select a recipe they want to make" refers to a function that allows users to select a recipe for their desired dish through an interface.

[0780] "A method for converting abstract expressions into concrete numerical values ​​using generative AI" refers to a technology for converting abstract cooking expressions such as "one tablespoon" or "5 minutes over medium heat" into specific numerical values ​​such as milliliters or cooking time.

[0781] "Means for obtaining climate information for the current location using GPS and weather information" refers to a function that uses a GPS device and a weather API to obtain the user's current location's latitude, longitude, and climate information (temperature, humidity, etc.).

[0782] "Methods for adjusting recipe values ​​based on acquired climate information" refers to technologies that adjust recipe ingredients, cooking times, etc., based on acquired climate information for the current location.

[0783] "Means for displaying the adjusted recipe to the user" refers to a function for displaying the specific adjusted recipe content on the user's device (such as a smartphone or tablet).

[0784] "A means of analyzing a user's emotional state using an emotion analysis engine and obtaining the analysis results" refers to a technology that uses a smartphone's camera and microphone to analyze the user's facial expressions and voice, estimate their emotional state, and obtain the results.

[0785] "Means for dynamically adjusting recipes and suggested comments based on acquired emotional states" refers to a technology that dynamically changes and adjusts recipe procedures and provided comments based on the user's emotional state.

[0786] "Means for suggesting dishes to a user based on climate information and emotional state" refers to a function that automatically suggests dishes suitable for the user based on acquired climate information and emotional state.

[0787] This invention relates to a system that suggests and adjusts cooking recipes suitable for a user under specific conditions. This system operates using a smartphone, GPS module, weather API, sentiment analysis engine, generative AI model, and the like.

[0788] System Configuration

[0789] 1. User terminal

[0790] Smartphone: Used as a user interface to accept user input.

[0791] GPS module: Obtains the user's current location.

[0792] Camera and microphone: Use an emotion analysis engine to analyze the user's emotional state.

[0793] 2. Server

[0794] Database: Stores cooking recipes and related data.

[0795] Generative AI model: Converts abstract cooking instructions into concrete numerical values.

[0796] Weather API: Retrieves weather information for your current location from an external weather data service.

[0797] Emotion analysis engine: A software module for analyzing a user's emotional state.

[0798] Processing flow

[0799] 1. Recipe Selection

[0800] The server displays a list of cooking recipes through the user's terminal, and the user selects the recipe they want.

[0801] 2. Data Acquisition

[0802] The smartphone's GPS module is used to obtain current location information, and a weather API is used to obtain weather information (temperature, humidity, etc.) for the current location.

[0803] 3. Emotion analysis

[0804] Using the smartphone's camera and microphone, the emotion analysis engine analyzes the user's facial expressions and voice to estimate their emotional state.

[0805] 4. Recipe adjustment

[0806] The server uses a generated AI model to convert abstract recipe instructions into concrete numbers and adjusts the recipe content based on acquired weather information and emotional states.

[0807] 5. Presentation to the user

[0808] The adjusted recipe is displayed on the user's device, and specific cooking instructions and encouraging comments are provided to the user.

[0809] Examples of specific cases and prompt statements

[0810] For example, if a user wants to make "chicken curry," the initial recipe might include abstract instructions such as "use one tablespoon of curry powder" or "sauté over medium heat for 5 minutes." The server then uses a generative AI model to convert these into specific numerical values ​​(e.g., 15 ml of curry powder, 5 minutes at heat level 3).

[0811] Furthermore, if the current temperature is low, 1 ml of seasoning will be added, and if the humidity is high, 30 seconds will be added to the cooking time. In addition, if the emotion analysis engine determines that the user is stressed, the cooking procedure will be simplified and advice or encouraging comments such as "lower the temperature" will be displayed.

[0812] Example of a prompt:

[0813] "To suggest a cooking genre, please tell me what kind of dishes would be suitable when the current temperature is 25 degrees Celsius."

[0814] "Please suggest dishes that can help users relax when they are feeling stressed."

[0815] In this way, the system can suggest and adjust the optimal dish according to the user's environment and emotional state.

[0816] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0817] Step 1:

[0818] The user selects a recipe. The user terminal displays a list of cooking recipes provided by the server on its interface. The user selects the desired recipe, and the terminal sends that information to the server. The input is the recipe ID selected by the user, and the output is confirmation that the information has been sent to the server.

[0819] Step 2:

[0820] The server receives and analyzes recipe information. Based on the recipe ID received from the terminal, the server retrieves the corresponding recipe information from the database and identifies the abstract expression. It then calls a generative AI model to convert the abstract expression into concrete numerical values. The input is the recipe ID, and the output is the recipe information converted into concrete numerical values.

[0821] Step 3:

[0822] The terminal obtains the user's current location. It activates the GPS module on the user's terminal, acquires location information (latitude and longitude), and sends this information to the server. The input is GPS data, and the output is latitude and longitude information.

[0823] Step 4:

[0824] The device obtains weather information for its current location. The device calls a weather API to retrieve weather information such as temperature, humidity, and atmospheric pressure for its current location, and sends it to the server. The input is latitude and longitude information, and the output is weather information.

[0825] Step 5:

[0826] The server adjusts the recipe based on weather information. The server analyzes the acquired weather information and applies recipe adjustment rules to adjust specific values. The input is weather information, and the output is the adjusted recipe information.

[0827] Step 6:

[0828] The device activates the emotion analysis engine. The device uses its camera and microphone to capture the user's facial expressions and voice, and uses the emotion analysis engine to analyze the user's emotional state. The analysis results are sent to the server. The input is the user's facial expressions and voice data, and the output is the emotional state.

[0829] Step 7:

[0830] The server further adjusts the recipe based on emotional information. The server receives emotional information and dynamically changes the recipe content and cooking procedure according to the user's emotional state. The input is emotional information, and the output is the final adjusted recipe information.

[0831] Step 8:

[0832] The terminal displays the adjusted recipe to the user. The terminal displays the adjusted recipe information received from the server on its interface, providing the user with specific cooking instructions and suggested comments. The input is the final adjusted recipe information, and the output is what is displayed to the user.

[0833] 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.

[0834] 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.

[0835] 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.

[0836] [Third Embodiment]

[0837] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0838] 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.

[0839] 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).

[0840] 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.

[0841] 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.

[0842] 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).

[0843] 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.

[0844] 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.

[0845] 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.

[0846] 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.

[0847] 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.

[0848] 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".

[0849] This invention is a cooking support system in which a user, a terminal, and a server work together to convert abstract recipe descriptions into concrete numerical values ​​and further adjust the recipe based on climate information of the user's current location. This system is implemented in the following steps.

[0850] Overall system flow

[0851] 1. The user selects a cooking recipe.

[0852] The user launches the application and displays the cooking recipe list screen.

[0853] The user selects the "Chicken Curry" recipe.

[0854] The device sends the selected recipe information to the server.

[0855] 2. The server receives and analyzes the recipe.

[0856] The server retrieves recipe information from the database based on the recipe ID received from the terminal.

[0857] The server analyzes the recipe content and detects abstract expressions such as "one tablespoon" and "cook over medium heat for 5 minutes."

[0858] The detected expressions are listed and prepared for conversion.

[0859] 3. The server uses AI to convert the data into specific numerical values.

[0860] The server passes the detected abstract representations to the generating AI as input.

[0861] The generating AI converts "one tablespoon" into specific numerical values ​​such as 15 ml, and "5 minutes over medium heat" into "5 minutes at heat level 3."

[0862] The server then reflects the specific numerical values ​​that have been converted into the recipe.

[0863] 4. The device acquires GPS information.

[0864] The device activates GPS to obtain the user's current location.

[0865] The device obtains its current location information and sends it to the server.

[0866] 5. The device obtains weather information for its current location.

[0867] The device calls a weather API to obtain weather information for its current location.

[0868] The device sends the weather information it has acquired to the server.

[0869] 6. The server adjusts the recipe based on weather information.

[0870] The server analyzes weather information and applies recipe adjustment rules (e.g., add 1 ml of seasoning if the temperature is below 15°C, or add 30 seconds to the cooking time if the humidity is above 60%).

[0871] The server adds adjustment values ​​based on weather information to recipes that have specific numerical values.

[0872] Update each step of the recipe and generate the adjusted recipe data.

[0873] 7. The device displays the adjusted recipe to the user.

[0874] The terminal receives the adjusted recipe data from the server.

[0875] The device displays the adjusted recipe on the cooking instructions screen. For example, it might say, "Add chicken and 16ml of seasoning, and heat over medium heat (heat level 3) for 5 minutes and 30 seconds."

[0876] 8. The user cooks according to the adjusted recipe.

[0877] The user cooks according to the specific and adjusted recipe displayed.

[0878] By following precise steps and numerical values, users can achieve a high success rate in cooking.

[0879] Specific example

[0880] For example, when making "chicken curry," a typical recipe might instruct you to "use one tablespoon of curry powder" or "sauté over medium heat for 5 minutes." The AI ​​then converts this into specific values ​​such as 15ml of curry powder and 5 minutes at heat level 3. Furthermore, it makes adjustments such as adding 1ml of seasoning if the current temperature is below 15℃, or adding 30 seconds to the cooking time if the humidity is above 60%, ensuring that delicious food can be consistently produced in any environment.

[0881] This system enables even cooking novices to achieve professional-level results while also offering the flexibility to adapt to different environmental conditions.

[0882] The following describes the processing flow.

[0883] Step 1:

[0884] The user launches the application and displays a list of cooking recipes. The user selects the "Chicken Curry" recipe from the list, and the device sends the selected recipe information to the server.

[0885] Step 2:

[0886] The server retrieves the corresponding recipe information from the database based on the recipe ID received from the terminal. The server analyzes the recipe content and detects abstract expressions such as "one tablespoon" or "cook over medium heat for 5 minutes." The detected expressions are then listed.

[0887] Step 3:

[0888] The server passes the detected abstract expressions to the generating AI as input. The generating AI converts "one tablespoon" into specific numerical values, such as 15 ml, and "5 minutes over medium heat" into "5 minutes at heat level 3." The server then incorporates these converted specific values ​​into the recipe.

[0889] Step 4:

[0890] The device activates GPS to obtain the user's current location. The device obtains the location information and sends the latitude and longitude data to the server.

[0891] Step 5:

[0892] The device calls a weather API to obtain weather information for its current location. Specifically, it retrieves meteorological data such as current temperature, humidity, and atmospheric pressure. The device then sends the obtained weather information to the server.

[0893] Step 6:

[0894] The server analyzes the weather information it has acquired. The server applies recipe adjustment rules (e.g., add 1 ml of seasoning if the temperature is below 15°C, add 30 seconds to the cooking time if the humidity is above 60%) to adjust the values ​​in the recipe. The server updates each step of the recipe based on the specific values ​​and generates the adjusted recipe data.

[0895] Step 7:

[0896] The terminal receives the adjusted recipe data from the server. The terminal displays the adjusted recipe to the user. Specifically, it displays "Add chicken and 16ml of seasoning, and heat over medium heat (heat level 3) for 5 minutes and 30 seconds."

[0897] Step 8:

[0898] The user cooks according to the specific and adjusted recipe displayed. By following the precise steps and numerical values, the user can achieve a high success rate in cooking.

[0899] Each of these steps helps the system to support users in consistently creating delicious meals, regardless of the environment.

[0900] (Example 1)

[0901] 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."

[0902] In recent years, there has been a growing need for easy-to-use cooking methods that allow even users with limited culinary knowledge and experience to prepare delicious meals. However, the results are often inconsistent due to abstract recipe instructions and environmental differences (such as climate variations). Furthermore, there is no clear method for incorporating acquired climate information into recipes. Therefore, there is a need to develop a system that enables anyone to consistently produce high-quality meals.

[0903] 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.

[0904] In this invention, the server includes means for the user to select a recipe they wish to make, means for converting abstract expressions into concrete numerical values ​​using a generative AI model, and means for acquiring environmental information of the current location using GPS and climate data. This allows the user to be provided with an adjusted recipe indicated by concrete numerical values, enabling the creation of consistently delicious dishes even under different climate conditions.

[0905] A "user" is someone who uses the system to create a dish.

[0906] A "recipe to be made" refers to the steps of a dish that the user selects and which the system then processes.

[0907] A "generative AI model" is an artificial intelligence model designed to give concrete form to abstract expressions that are input into it.

[0908] "Abstract expressions" refer to expressions in a recipe that do not specify concrete numerical values. Examples: "one tablespoon," "cook over medium heat for 5 minutes," etc.

[0909] "Specific numerical values" refer to numbers that accurately represent the cooking steps and quantities in a recipe. Examples: "15ml", "5 minutes at heat level 3", etc.

[0910] GPS is a global positioning system used to determine a user's current location.

[0911] "Climate data" refers to data that includes environmental information such as temperature, humidity, and precipitation.

[0912] "Environmental information" refers to climate data for the current location.

[0913] "Adjusting" means changing the numerical values ​​of a recipe based on the acquired environmental information.

[0914] "Data storage means" refers to a method or apparatus for saving and reusing the numerical values ​​of a modified recipe.

[0915] "Multiple rules" refers to several criteria or guidelines for adjusting recipe values ​​based on acquired environmental information.

[0916] A "prompt message" is a text input to a generative AI model, intended to prompt the model to transform abstract expressions.

[0917] This invention is a system in which a user, a terminal, and a server work together to convert cooking recipes into specific numerical values ​​and further adjust the recipes based on climate information of the current location. The following hardware and software are used to implement this system.

[0918] The user launches the application on a device such as a smartphone or tablet. This application allows the user to access a screen where they can select a cooking recipe. For example, if the user selects the "Chicken Curry" recipe, the device sends that information to the server. The server retrieves the recipe information from its database based on the recipe ID and analyzes the recipe content. From the analyzed recipe content, it detects abstract expressions such as "one tablespoon" and "cook over medium heat for 5 minutes."

[0919] The server uses a generative AI model (e.g., GPT-4) to convert this abstract expression into concrete numerical values. Input to this AI model is in the form of prompt statements. For example, if the prompt statement "Convert the expression 'one tablespoon' into a concrete numerical value" is passed to the generative AI model, "one tablespoon" will be converted to 15 ml, and "5 minutes over medium heat" will be converted to "5 minutes at heat level 3". The server then reflects these conversion results in the original recipe.

[0920] Furthermore, the device uses GPS functionality to obtain the user's current location. Based on this location information, the device calls a weather API (for example, the OpenWeatherMap API) to obtain climate data such as temperature and humidity at the current location. This climate data is sent to a server, which performs analysis. Based on the results of this analysis, the recipe is adjusted. For example, if the temperature is below 15°C, the amount of seasoning is increased by 1 ml, and if the humidity is above 60%, the cooking time is increased by 30 seconds, according to these rules.

[0921] The adjusted recipe is sent back from the server to the terminal and displayed on the terminal. The displayed recipe includes specific numerical values, such as "Add chicken and 16 ml of seasoning, and heat over medium heat (heat level 3) for 5 minutes and 30 seconds." The user then cooks according to these instructions.

[0922] This system enables even cooking novices to achieve professional-level results while also offering the flexibility to adapt to different environmental conditions.

[0923] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0924] Step 1:

[0925] The user selects a cooking recipe. The user launches the application on their device and selects "Chicken Curry" from the displayed list of cooking recipes. The device receives the selected recipe ID as input and sends it to the server. Specifically, the user performs a tap operation, and the device generates an HTTP request and sends the recipe ID to the server.

[0926] Step 2:

[0927] The server receives and analyzes the recipe. The server uses the recipe ID received from the terminal as input to retrieve the corresponding recipe information from the database. The retrieved recipe information includes abstract expressions such as "one tablespoon" and "5 minutes over medium heat." The server analyzes these abstract expressions and creates a list. Data processing involves string manipulation to extract abstract keywords.

[0928] Step 3:

[0929] The server uses a generative AI model to convert abstract expressions into concrete numerical values. The server generates a prompt and passes the abstract expression as input to the generative AI model. An example of such a prompt is, "Please convert the expression 'one tablespoon' into a concrete numerical value." Based on the prompt, the generative AI model converts "one tablespoon" to 15 ml and "5 minutes over medium heat" to "5 minutes at heat level 3," and outputs these to the server. The server then incorporates this output into the original recipe.

[0930] Step 4:

[0931] The device acquires GPS information. While the user carries the device, the device activates its internal GPS module and acquires current location information as input. The acquired latitude and longitude information is sent to the server in HTTP request format. Specifically, the device activates its GPS sensor, periodically captures location information, and sends it to the server.

[0932] Step 5:

[0933] The device obtains weather information for its current location. Based on this location information, the device calls a weather API to obtain environmental information such as temperature and humidity. For example, it uses the OpenWeatherMap API. This obtained climate data is sent to the server as input. The device generates an HTTP request, sends the request to the weather API, parses the resulting JSON data, extracts the necessary information, and sends it to the server.

[0934] Step 6:

[0935] The server analyzes weather information and adjusts recipes accordingly. The server analyzes environmental information such as temperature and humidity. Based on this analysis, the server applies recipe adjustment rules. For example, if the temperature is below 15°C, it increases the amount of seasoning by 1 ml, and if the humidity is above 60%, it increases the cooking time by 30 seconds. The server uses these adjusted values ​​to update the recipe and generate new recipe data. Data processing involves adding or updating numerical values ​​in the recipe data.

[0936] Step 7:

[0937] The terminal displays the adjusted recipe to the user. The terminal receives the adjusted recipe data from the server as input and displays specific numerical values ​​and instructions on the cooking procedure screen. For example, it might display, "Add chicken and 16ml of seasoning, and heat over medium heat (heat level 3) for 5 minutes and 30 seconds." The terminal parses the received JSON data and displays it on the screen.

[0938] Step 8:

[0939] The user cooks according to the adjusted recipe. The user follows the specific and adjusted recipe displayed on the device. The user performs specific actions such as using measuring cups and adjusting the heat, and completes the dish according to the instructions. By following the appropriate procedures and values, the user can produce dishes of consistent quality.

[0940] (Application Example 1)

[0941] 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."

[0942] Traditional cooking recipes often standardize factors such as climate conditions, location, specific ingredient amounts, and cooking times, which can lead to variations in the quality and final result of dishes. Furthermore, the consistency of dishes in physical restaurants is often inconsistent, due to differences among cooking staff, making it difficult to provide customers with consistent quality. To address these issues, a system is needed that adjusts recipes based on real-time climate information and provides cooking staff with specific and consistent instructions.

[0943] 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.

[0944] In this invention, the server includes means for the user to select a recipe they wish to make, means for converting abstract expressions into concrete numerical values ​​using a generative AI, means for acquiring climate information of the current location using GPS and weather information, means for adjusting the numerical values ​​of the recipe based on the acquired climate information, means for displaying the adjusted recipe to the user, and means for displaying the adjusted recipe on a display device in a physical store. This makes it possible to consistently provide high-quality food in any environment.

[0945] A "user" is an individual or a kitchen staff member at a physical restaurant who selects a recipe and uses the system to prepare a meal.

[0946] A "recipe" is a document that contains specific instructions and information about ingredients for preparing a dish.

[0947] "Generative AI" is an artificial intelligence model that converts abstract expressions into concrete numbers or instructions.

[0948] "Abstract expressions" are those that lack specific numerical values, such as "one tablespoon" or "5 minutes over medium heat."

[0949] "Specific numerical values" refer to clear, quantified instructions such as "15ml" or "5 minutes at heat level 3" that are converted by the generating AI.

[0950] GPS is a global positioning system used to determine one's location on Earth.

[0951] "Weather information" refers to meteorological data such as temperature, humidity, and wind speed at your current location.

[0952] "Climate information" refers to environmental data based on acquired weather information.

[0953] "Adjusting the values" means changing the amount of ingredients or cooking time in a recipe based on climate information.

[0954] A "display device" is a screen device used to display the adjusted recipe and cooking instructions.

[0955] This invention is a system for converting abstract recipe instructions into concrete numerical values ​​when a user is cooking, and further adjusting the recipe based on climate information of the user's current location. Specifically, it is implemented in the following steps.

[0956] First, the user launches the application using a device (such as a smartphone or tablet) and displays a list of cooking recipes. When the user selects a recipe they want to make, the recipe information is sent from the device to the server. The server retrieves the corresponding recipe information from the database based on the recipe ID and analyzes the recipe content. At this time, the server uses a generation AI to detect abstract expressions such as "one tablespoon" or "5 minutes over medium heat" and converts them into specific numerical values ​​such as "15 ml" or "5 minutes at heat level 3".

[0957] Next, the device uses GPS to obtain the user's current location and calls a weather API (e.g., WeatherAPI) to retrieve weather information for the current location. The obtained climate information is sent from the device to the server, which adjusts the recipe values ​​based on this information. Specifically, rules such as adding 1 ml of seasoning if the temperature is below 15°C, or adding 30 seconds to the cooking time if the humidity is above 60%, are applied.

[0958] The adjusted recipe is sent from the server to the terminal and displayed on the terminal's display device. This display is also shown on the display device in the physical store. By following the displayed, specific, and adjusted recipe instructions, the cooking staff can consistently provide high-quality dishes that are adapted to the environment.

[0959] The hardware required includes a smartphone, tablet, display device, GPS module, and an internet connection to obtain weather information. The software used includes a recipe database, a generative AI model, a weather API, and a dedicated cooking assistance application.

[0960] As a concrete example, if a user wants to make "chicken curry," and uses the application to confirm their current location is Tokyo, and obtains weather information indicating a temperature of 10°C and humidity of 70%, the original recipe instruction of "one tablespoon of curry powder" will be converted to "16 ml," and "sauté over medium heat for 5 minutes" will be adjusted to "sauté over medium heat (heat level 3) for 5 minutes and 30 seconds."

[0961] Examples of prompt statements to input into the generative AI model are as follows:

[0962] Example of a prompt:

[0963] Input: "One tablespoon"

[0964] Output: "15ml"

[0965] Input: "5 minutes over medium heat"

[0966] Output: "5 minutes with firepower level 3"

[0967] This will enable us to consistently provide high-quality food in our physical stores as well.

[0968] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0969] Step 1:

[0970] The user launches the application and displays a list of cooking recipes. When the user selects a cooking recipe they want to make, the recipe information is sent from the device to the server.

[0971] Input: User-selected recipe ID

[0972] Output: Recipe ID to send to the server

[0973] Specific operation: When the user selects "Chicken Curry," the device automatically sends its recipe ID to the server.

[0974] Step 2:

[0975] Based on the recipe ID received from the terminal, the server retrieves the corresponding recipe information from the database and analyzes the recipe content.

[0976] Input: Recipe ID sent to the server

[0977] Output: Recipe information (including abstract expressions)

[0978] Specific operation: The server executes a database query to retrieve recipe information corresponding to the recipe ID. It then analyzes the retrieved recipe content and lists abstract expressions such as "one tablespoon" or "cook over medium heat for 5 minutes."

[0979] Step 3:

[0980] The server takes abstract representations as input to the generated AI model and converts them into concrete numerical values.

[0981] Input: Abstract expressions (e.g., "one tablespoon", "cook over medium heat for 5 minutes")

[0982] Output: Specific numerical values ​​(e.g., "15ml", "5 minutes at heat level 3")

[0983] Specific operation: The server takes prompt text such as "one tablespoon" as input to the generating AI model and obtains "15ml" as output. This operation is performed for each abstract expression, converting them into concrete numerical values.

[0984] Step 4:

[0985] The device uses GPS to obtain its current location and then calls a weather API to retrieve weather information for that location.

[0986] Input: User's current location (GPS data)

[0987] Output: Weather information for your current location (e.g., temperature 10°C, humidity 70%)

[0988] Specific operation: The device activates its GPS module and obtains the latitude and longitude of its current location. Then, it sends a request to the weather API to obtain weather information such as temperature, humidity, and wind speed.

[0989] Step 5:

[0990] The device sends the acquired weather information to the server, which then adjusts the recipe values ​​based on this information.

[0991] Input: Weather information for your current location

[0992] Output: Adjusted recipe information

[0993] Specific operation: Based on the acquired climate information, the server applies specific rules to adjust the recipe values ​​(e.g., if the temperature is 15°C or below, add 1 ml of seasoning; if the humidity is 60% or above, add 30 seconds to the cooking time).

[0994] Step 6:

[0995] The adjusted recipe information is sent to the terminal and displayed on the terminal's display device. It is also displayed on the display devices in the physical store.

[0996] Input: Adjusted recipe information

[0997] Output: Screen showing the adjusted recipe instructions.

[0998] Specific operation: The terminal displays the adjusted recipe information received from the server on its screen and also transmits that information to the display device in the physical store.

[0999] Step 7:

[1000] The cooking staff will prepare the food according to the specific and adjusted recipe instructions provided.

[1001] Input: Adjusted recipe instructions

[1002] Output: High-quality cuisine

[1003] Specific actions: Cooking staff will follow the specific recipe instructions displayed on the terminal and display device to carry out the cooking procedure.

[1004] 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.

[1005] This invention is a cooking support system in which the user, terminal, server, and emotion engine work together to convert abstract recipe descriptions into concrete numerical values ​​and adjust the recipe based on the current location's climate information and the user's emotional state. This system is implemented in the following steps.

[1006] Overall system flow

[1007] 1. The user selects a cooking recipe.

[1008] The user launches the application and displays the cooking recipe list screen.

[1009] The user selects the "Chicken Curry" recipe.

[1010] The device sends the selected recipe information to the server.

[1011] 2. The server receives and analyzes the recipe.

[1012] The server retrieves the corresponding recipe information from the database based on the recipe ID received from the terminal.

[1013] The server analyzes the recipe content and detects abstract expressions such as "one tablespoon" or "cook over medium heat for 5 minutes."

[1014] List the detected expressions.

[1015] 3. The server uses AI to convert the data into specific numerical values.

[1016] The server passes the detected abstract representations to the generating AI as input.

[1017] The generating AI converts "one tablespoon" into specific numerical values ​​such as 15 ml, and "5 minutes over medium heat" into "5 minutes at heat level 3."

[1018] The server then reflects the specific numerical values ​​that have been converted into the recipe.

[1019] 4. The device acquires GPS information.

[1020] The device activates GPS to obtain the user's current location.

[1021] The device acquires its current location information and sends its latitude and longitude data to the server.

[1022] 5. The device obtains weather information for its current location.

[1023] The device calls a weather API to obtain weather information for its current location. Specifically, it retrieves meteorological data such as current temperature, humidity, and atmospheric pressure.

[1024] The device sends the weather information it has acquired to the server.

[1025] 6. The server adjusts the recipe based on weather information.

[1026] The server analyzes the weather information it has acquired.

[1027] The server adjusts the recipe values ​​by applying recipe adjustment rules (e.g., add 1 ml of seasoning if the temperature is below 15°C, add 30 seconds to the cooking time if the humidity is above 60%).

[1028] The server updates each step of the recipe based on specific numerical values ​​and generates the adjusted recipe data.

[1029] 7. The device uses an emotion engine.

[1030] The device activates an emotion engine to estimate the user's emotional state by analyzing their facial expressions, voice, and other data.

[1031] The emotion engine analyzes the user's emotional state and sends that information to the server.

[1032] 8. The server further adjusts the recipe based on emotional information.

[1033] The server receives emotional information and dynamically changes the cooking procedure and comments based on the user's emotional state.

[1034] For example, if a user is feeling stressed, simpler instructions or encouraging comments will be displayed.

[1035] 9. The device displays the adjusted recipe to the user.

[1036] The terminal receives the adjusted recipe data from the server.

[1037] The device displays the adjusted recipe on the cooking instructions screen. Specifically, it will display instructions such as, "Add chicken and 16ml of seasoning, and heat over medium heat (heat level 3) for 5 minutes and 30 seconds."

[1038] 10. The user cooks according to the adjusted recipe.

[1039] The user cooks according to the specific and adjusted recipe displayed.

[1040] By following precise steps and numerical values, users can achieve a high success rate in cooking.

[1041] Specific example

[1042] For example, when making "chicken curry," a typical recipe might instruct you to "use one tablespoon of curry powder" or "sauté over medium heat for 5 minutes." The AI ​​generator converts this into specific numbers, such as 15ml of curry powder or 5 minutes at heat level 3. Furthermore, if the current temperature is below 15℃, it adds 1ml of seasoning, and if the humidity is above 60%, it adds 30 seconds to the cooking time. In addition, if the emotion engine detects the user's stress level, it simplifies the cooking procedure and displays advice and encouraging comments such as "lower the temperature," allowing the user to cook in a relaxed state.

[1043] This system enables even cooking novices to achieve professional-level results while also offering the flexibility to adapt to different environmental conditions and the user's emotional state.

[1044] The following describes the processing flow.

[1045] Step 1:

[1046] The user launches the application and displays a list of cooking recipes. The user selects the "Chicken Curry" recipe from the list, and the device sends the selected recipe information to the server.

[1047] Step 2:

[1048] The server retrieves the corresponding recipe information from the database based on the recipe ID received from the terminal. The server analyzes the recipe content and detects abstract expressions such as "one tablespoon" or "cook over medium heat for 5 minutes." The detected expressions are then listed.

[1049] Step 3:

[1050] The server passes the detected abstract expressions to the generating AI as input. The generating AI converts "one tablespoon" into specific numerical values, such as 15 ml, and "5 minutes over medium heat" into "5 minutes at heat level 3." The server then incorporates these converted specific values ​​into the recipe.

[1051] Step 4:

[1052] The device activates GPS to obtain the user's current location. The device obtains the location information and sends the latitude and longitude data to the server.

[1053] Step 5:

[1054] The device calls a weather API to obtain weather information for its current location. Specifically, it retrieves meteorological data such as current temperature, humidity, and atmospheric pressure. The device then sends the obtained weather information to the server.

[1055] Step 6:

[1056] The server analyzes the weather information it has acquired. The server applies recipe adjustment rules (e.g., add 1 ml of seasoning if the temperature is below 15°C, add 30 seconds to the cooking time if the humidity is above 60%) to adjust the values ​​in the recipe. The server updates each step of the recipe based on the specific values ​​and generates the adjusted recipe data.

[1057] Step 7:

[1058] The device captures the user's facial expressions and voice, and uses an emotion engine to analyze the user's emotional state. The device then sends the results of the emotion engine's analysis to the server.

[1059] Step 8:

[1060] The server receives emotional information and further adjusts the recipe based on the user's emotional state. For example, if the user is feeling stressed, the server may simplify the cooking steps and add encouraging comments. Specifically, it might offer advice such as, "Lower the temperature and give yourself more time to relax."

[1061] Step 9:

[1062] The device receives the adjusted recipe data from the server. The device displays the adjusted recipe on the cooking procedure screen. Specifically, it displays "Add chicken and 16ml of seasoning, and heat over medium heat (heat level 3) for 5 minutes and 30 seconds."

[1063] Step 10:

[1064] The user cooks according to the displayed, specific, and adjusted recipe. By following the precise steps and numerical values, the user can achieve a high success rate in cooking. Furthermore, adjustments based on the user's emotional state can improve their satisfaction and sense of security during cooking.

[1065] Each of these steps helps the system to consistently create delicious dishes regardless of the user's environment or emotional state.

[1066] (Example 2)

[1067] 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."

[1068] Conventional cooking support systems have a problem in that abstract recipes are not translated into concrete numerical values, forcing users to rely on experience when cooking. Furthermore, because recipes are not adjusted to take into account the current climate information or the user's emotional state, there are issues such as inconsistent cooking quality and a failure to reduce user stress during cooking.

[1069] The identification processing performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for selecting a recipe that the user wants to cook, means for converting abstract expressions into concrete numerical values ​​using a generation AI, means for acquiring climate information of the current location using GPS and weather information, means for adjusting the numerical values ​​of the recipe based on the acquired climate information, means for analyzing the user's emotional state and further adjusting the recipe based on that information, and means for displaying the adjusted recipe to the user. As a result, even beginners in cooking can cook based on concrete numerical values, and by providing an optimal recipe that corresponds to climate information and emotional state, it is possible to improve the quality of the cooking and reduce user stress.

[1070] A "user" is the entity that uses the system to select a cooking recipe and then performs the cooking.

[1071] A "recipe" is information that describes the steps and ingredients for cooking, and includes specific numerical values ​​and instructions such as cooking time.

[1072] "Generative AI" refers to artificial intelligence technology that includes algorithms and models for converting abstract expressions into concrete numerical values.

[1073] "Abstract descriptions" are those that lack clear numerical values ​​or specific instructions, such as "one tablespoon" or "cook over medium heat for 5 minutes."

[1074] "Specific numerical values" refer to clear and measurable numbers such as 15ml or 5 minutes at heat level 3.

[1075] GPS is a global positioning system used by devices to obtain location information.

[1076] "Weather information" refers to meteorological data for a specific location, such as current temperature, humidity, and atmospheric pressure.

[1077] "Climate information" refers to data that indicates the environmental conditions of the current location based on GPS and weather information.

[1078] "Emotional state" refers to data that represents the user's stress level and emotional state, and is obtained through facial expression and voice analysis.

[1079] "Cooking instructions" refer to a set of steps and actions required to complete a dish.

[1080] The "adjusted recipe" refers to the final recipe, which has been refined and made concrete based on the acquired climate and sentiment data.

[1081] This invention is a system that improves the user's cooking experience through the cooperation of the user, terminal, server, and emotion analysis engine. This system converts abstract expressions of cooking recipes into concrete numerical values ​​and adjusts the recipes based on the user's current location's climate information and their emotional state.

[1082] Hardware and software to be used

[1083] This system utilizes devices such as smartphones and tablets, internet-connected servers, GPS modules, weather APIs, generative AI models, and sentiment analysis engines. Specifically, the following common hardware and software are used:

[1084] Devices: Smartphones, tablets, PCs

[1085] Server: A server on the internet

[1086] Generative AI models: AI algorithms (e.g., GPT-3)

[1087] Emotion analysis engine: Facial expression recognition software, voice analysis tool

[1088] GPS module: GPS function built into the device

[1089] Weather API: Weather data service (e.g., OpenWeatherMap API)

[1090] Data processing and data calculation

[1091] The user launches the application and selects a cooking recipe. The device sends the selected recipe information to the server. The server retrieves the recipe information from its database based on the received recipe ID and uses a generative AI model to convert the abstract representation into concrete numerical values.

[1092] The device activates its GPS module to obtain its current location and sends the latitude and longitude data to the server. The server then uses this information to call a weather API to obtain weather information for the current location (temperature, humidity, atmospheric pressure, etc.). Based on the obtained weather information, it adjusts the recipe values ​​by applying pre-configured rules.

[1093] Next, the device activates its emotion analysis engine, analyzes the user's facial expressions and voice to estimate their emotional state, and sends this information to the server. The server then adjusts the recipe based on the received emotional information. If the user is experiencing stress, the server may simplify the procedure or add encouraging comments.

[1094] Finally, the device displays the adjusted recipe to the user, who then cooks according to it.

[1095] Examples of specific cases and prompt statements

[1096] For example, when making "chicken curry," a typical recipe includes abstract instructions such as "use one tablespoon of curry powder" or "sauté over medium heat for 5 minutes." The generative AI model converts these into specific numerical values, such as "15 ml of curry powder" or "5 minutes at heat level 3." Additionally, if the current temperature is below 15°C, 1 ml of seasoning is added, and if the humidity is above 60%, 30 seconds of cooking time is added.

[1097] Furthermore, if the emotion analysis engine detects a user's stress level, it will display simple actions such as "lower the temperature" or encouraging comments, allowing the user to cook in a relaxed state.

[1098] Examples of specific prompt messages:

[1099] "Please translate the following abstract phrases in the recipe into specific numbers: 'Add one tablespoon of curry powder,' 'Sauté over medium heat for 5 minutes.'"

[1100] "The recipe will be adjusted based on your current location's climate information. The current temperature is 12°C and the humidity is 65%. Please apply the corresponding adjustment rules."

[1101] "Users are feeling stressed. Please simplify the cooking instructions and add encouraging comments."

[1102] This system allows even cooking beginners to proceed with cooking based on precise numerical data, obtain optimal recipes that take into account climate conditions and emotional states, and ultimately achieve higher quality dishes.

[1103] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1104] Step 1:

[1105] The user selects a cooking recipe.

[1106] Specific actions:

[1107] The user launches the application and selects "Chicken Curry" from the list of recipes.

[1108] The device retrieves the selected recipe ID and sends a request containing that recipe ID to the server.

[1109] Input: Recipe ID

[1110] Output: Request sent to the server

[1111] Step 2:

[1112] The server receives and analyzes the recipe.

[1113] Specific actions:

[1114] The server receives the recipe ID sent from the terminal.

[1115] The server retrieves the corresponding recipe information from the database based on the recipe ID.

[1116] The server analyzes the recipe content and detects abstract expressions such as "one tablespoon" or "cook over medium heat for 5 minutes."

[1117] The server lists the detected abstract representations.

[1118] Input: Recipe ID

[1119] Output: Listed abstraction

[1120] Step 3:

[1121] The server uses AI generation to convert the data into specific numerical values.

[1122] Specific actions:

[1123] The server inputs a list of abstract representations into an AI model that generates them.

[1124] The generating AI model converts "one tablespoon" into specific numerical values ​​such as 15 ml and "5 minutes over medium heat" into "5 minutes at heat level 3".

[1125] The server reflects the specific numerical values ​​it generates into the recipe data.

[1126] Input: Listed abstract expressions

[1127] Output: Recipe converted to specific numerical values

[1128] Step 4:

[1129] The device acquires GPS information.

[1130] Specific actions:

[1131] The device activates its GPS module and obtains the user's current location information.

[1132] The terminal sends the acquired latitude and longitude data to the server.

[1133] Input: None (Location information obtained via GPS module)

[1134] Output: Latitude and longitude data

[1135] Step 5:

[1136] The device obtains weather information for its current location.

[1137] Specific actions:

[1138] The device uses a weather API to obtain weather information (temperature, humidity, atmospheric pressure, etc.) for its current location.

[1139] The device sends the weather information it has acquired to the server.

[1140] Input: Latitude and longitude data

[1141] Output: Weather information

[1142] Step 6:

[1143] The server adjusts the recipe based on weather information.

[1144] Specific actions:

[1145] The server analyzes the weather information that has been sent.

[1146] The server adjusts the values ​​by applying recipe adjustment rules (e.g., add 1 ml of seasoning if the temperature is below 15°C, add 30 seconds to the cooking time if the humidity is above 60%).

[1147] The server generates the adjusted recipe data.

[1148] Input: Weather information

[1149] Output: Adjusted recipe data

[1150] Step 7:

[1151] The device uses an emotion engine.

[1152] Specific actions:

[1153] The device uses its camera and microphone to collect the user's facial expressions and voice in real time.

[1154] The emotion engine analyzes the user's emotional state and sends the data to the server.

[1155] Input: Facial expression data, audio data

[1156] Output: User sentiment information

[1157] Step 8:

[1158] The server further adjusts the recipe based on emotional information.

[1159] Specific actions:

[1160] The server analyzes the user's emotional information received from the emotion engine.

[1161] The server takes into account the user's stress level and makes changes such as simplifying the cooking procedure.

[1162] The server generates the recipe data after final adjustments.

[1163] Input: User sentiment information

[1164] Output: Recipe data after final adjustments

[1165] Step 9:

[1166] The device displays the adjusted recipe to the user.

[1167] Specific actions:

[1168] The terminal receives the finalized recipe data from the server.

[1169] Based on the data received by the device, the cooking procedure screen displays specific steps.

[1170] Input: Recipe data after final adjustments

[1171] Output: The final adjusted recipe displayed on the terminal.

[1172] Step 10:

[1173] The user cooks according to the adjusted recipe.

[1174] Specific actions:

[1175] The user reviews the specific and customized recipe displayed on their device.

[1176] The user follows the instructions to cook and ultimately complete a dish with a high success rate.

[1177] Input: Recipe after final adjustments

[1178] Output: Finished dish

[1179] (Application Example 2)

[1180] 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."

[1181] Conventional cooking support systems have the problem of being unable to reflect climate information or the user's emotional state, leading to decreased user satisfaction under certain conditions. Furthermore, when converting recipes and cooking procedures into concrete numerical values, they remain abstract, making it difficult for users to accurately reproduce the dishes. In addition, there is a lack of features for personalized cooking suggestions and adjustments, resulting in a failure to adequately meet user needs.

[1182] 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.

[1183] In this invention, the server includes means for selecting a recipe that the user wants to cook, means for converting abstract expressions into concrete numerical values ​​using a generative AI, means for acquiring climate information of the current location using GPS and weather information, means for adjusting the numerical values ​​of the recipe based on the acquired climate information, means for displaying the adjusted recipe to the user, means for analyzing the user's emotional state using an emotion analysis engine and acquiring the analysis results, means for dynamically adjusting the recipe and suggested comments based on the acquired emotional state, and means for suggesting dishes to the user based on the climate information and emotional state. As a result, the user can obtain specific cooking procedures that are appropriate to the climate information and emotional state, thereby increasing user satisfaction.

[1184] "A means for users to select a recipe they want to make" refers to a function that allows users to select a recipe for their desired dish through an interface.

[1185] "A method for converting abstract expressions into concrete numerical values ​​using generative AI" refers to a technology for converting abstract cooking expressions such as "one tablespoon" or "5 minutes over medium heat" into specific numerical values ​​such as milliliters or cooking time.

[1186] "Means for obtaining climate information for the current location using GPS and weather information" refers to a function that uses a GPS device and a weather API to obtain the user's current location's latitude, longitude, and climate information (temperature, humidity, etc.).

[1187] "Methods for adjusting recipe values ​​based on acquired climate information" refers to technologies that adjust recipe ingredients, cooking times, etc., based on acquired climate information for the current location.

[1188] "Means for displaying the adjusted recipe to the user" refers to a function for displaying the specific adjusted recipe content on the user's device (such as a smartphone or tablet).

[1189] "A means of analyzing a user's emotional state using an emotion analysis engine and obtaining the analysis results" refers to a technology that uses a smartphone's camera and microphone to analyze the user's facial expressions and voice, estimate their emotional state, and obtain the results.

[1190] "Means for dynamically adjusting recipes and suggested comments based on acquired emotional states" refers to a technology that dynamically changes and adjusts recipe procedures and provided comments based on the user's emotional state.

[1191] "Means for suggesting dishes to a user based on climate information and emotional state" refers to a function that automatically suggests dishes suitable for the user based on acquired climate information and emotional state.

[1192] This invention relates to a system that suggests and adjusts cooking recipes suitable for a user under specific conditions. This system operates using a smartphone, GPS module, weather API, sentiment analysis engine, generative AI model, and the like.

[1193] System Configuration

[1194] 1. User terminal

[1195] Smartphone: Used as a user interface to accept user input.

[1196] GPS module: Obtains the user's current location.

[1197] Camera and microphone: Use an emotion analysis engine to analyze the user's emotional state.

[1198] 2. Server

[1199] Database: Stores cooking recipes and related data.

[1200] Generative AI model: Converts abstract cooking instructions into concrete numerical values.

[1201] Weather API: Retrieves weather information for your current location from an external weather data service.

[1202] Emotion analysis engine: A software module for analyzing a user's emotional state.

[1203] Processing flow

[1204] 1. Recipe Selection

[1205] The server displays a list of cooking recipes through the user's terminal, and the user selects the recipe they want.

[1206] 2. Data Acquisition

[1207] The smartphone's GPS module is used to obtain current location information, and a weather API is used to obtain weather information (temperature, humidity, etc.) for the current location.

[1208] 3. Emotion analysis

[1209] Using the smartphone's camera and microphone, the emotion analysis engine analyzes the user's facial expressions and voice to estimate their emotional state.

[1210] 4. Recipe adjustment

[1211] The server uses a generated AI model to convert abstract recipe instructions into concrete numbers and adjusts the recipe content based on acquired weather information and emotional states.

[1212] 5. Presentation to the user

[1213] The adjusted recipe is displayed on the user's device, and specific cooking instructions and encouraging comments are provided to the user.

[1214] Examples of specific cases and prompt statements

[1215] For example, if a user wants to make "chicken curry," the initial recipe might include abstract instructions such as "use one tablespoon of curry powder" or "sauté over medium heat for 5 minutes." The server then uses a generative AI model to convert these into specific numerical values ​​(e.g., 15 ml of curry powder, 5 minutes at heat level 3).

[1216] Furthermore, if the current temperature is low, 1 ml of seasoning will be added, and if the humidity is high, 30 seconds will be added to the cooking time. In addition, if the emotion analysis engine determines that the user is stressed, the cooking procedure will be simplified and advice or encouraging comments such as "lower the temperature" will be displayed.

[1217] Example of a prompt:

[1218] "To suggest a cooking genre, please tell me what kind of dishes would be suitable when the current temperature is 25 degrees Celsius."

[1219] "Please suggest dishes that can help users relax when they are feeling stressed."

[1220] In this way, the system can suggest and adjust the optimal dish according to the user's environment and emotional state.

[1221] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1222] Step 1:

[1223] The user selects a recipe. The user terminal displays a list of cooking recipes provided by the server on its interface. The user selects the desired recipe, and the terminal sends that information to the server. The input is the recipe ID selected by the user, and the output is confirmation that the information has been sent to the server.

[1224] Step 2:

[1225] The server receives and analyzes recipe information. Based on the recipe ID received from the terminal, the server retrieves the corresponding recipe information from the database and identifies the abstract expression. It then calls a generative AI model to convert the abstract expression into concrete numerical values. The input is the recipe ID, and the output is the recipe information converted into concrete numerical values.

[1226] Step 3:

[1227] The terminal obtains the user's current location. It activates the GPS module on the user's terminal, acquires location information (latitude and longitude), and sends this information to the server. The input is GPS data, and the output is latitude and longitude information.

[1228] Step 4:

[1229] The device obtains weather information for its current location. The device calls a weather API to retrieve weather information such as temperature, humidity, and atmospheric pressure for its current location, and sends it to the server. The input is latitude and longitude information, and the output is weather information.

[1230] Step 5:

[1231] The server adjusts the recipe based on weather information. The server analyzes the acquired weather information and applies recipe adjustment rules to adjust specific values. The input is weather information, and the output is the adjusted recipe information.

[1232] Step 6:

[1233] The device activates the emotion analysis engine. The device uses its camera and microphone to capture the user's facial expressions and voice, and uses the emotion analysis engine to analyze the user's emotional state. The analysis results are sent to the server. The input is the user's facial expressions and voice data, and the output is the emotional state.

[1234] Step 7:

[1235] The server further adjusts the recipe based on emotional information. The server receives emotional information and dynamically changes the recipe content and cooking procedure according to the user's emotional state. The input is emotional information, and the output is the final adjusted recipe information.

[1236] Step 8:

[1237] The terminal displays the adjusted recipe to the user. The terminal displays the adjusted recipe information received from the server on its interface, providing the user with specific cooking instructions and suggested comments. The input is the final adjusted recipe information, and the output is what is displayed to the user.

[1238] 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.

[1239] 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.

[1240] 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.

[1241] [Fourth Embodiment]

[1242] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[1243] 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.

[1244] 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).

[1245] 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.

[1246] 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.

[1247] 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).

[1248] 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.

[1249] 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.

[1250] 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.

[1251] 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.

[1252] 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.

[1253] 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.

[1254] 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".

[1255] This invention is a cooking support system in which a user, a terminal, and a server work together to convert abstract recipe descriptions into concrete numerical values ​​and further adjust the recipe based on climate information of the user's current location. This system is implemented in the following steps.

[1256] Overall system flow

[1257] 1. The user selects a cooking recipe.

[1258] The user launches the application and displays the cooking recipe list screen.

[1259] The user selects the "Chicken Curry" recipe.

[1260] The device sends the selected recipe information to the server.

[1261] 2. The server receives and analyzes the recipe.

[1262] The server retrieves recipe information from the database based on the recipe ID received from the terminal.

[1263] The server analyzes the recipe content and detects abstract expressions such as "one tablespoon" and "cook over medium heat for 5 minutes."

[1264] The detected expressions are listed and prepared for conversion.

[1265] 3. The server uses AI to convert the data into specific numerical values.

[1266] The server passes the detected abstract representations to the generating AI as input.

[1267] The generating AI converts "one tablespoon" into specific numerical values ​​such as 15 ml, and "5 minutes over medium heat" into "5 minutes at heat level 3."

[1268] The server then reflects the specific numerical values ​​that have been converted into the recipe.

[1269] 4. The device acquires GPS information.

[1270] The device activates GPS to obtain the user's current location.

[1271] The device obtains its current location information and sends it to the server.

[1272] 5. The device obtains weather information for its current location.

[1273] The device calls a weather API to obtain weather information for its current location.

[1274] The device sends the weather information it has acquired to the server.

[1275] 6. The server adjusts the recipe based on weather information.

[1276] The server analyzes weather information and applies recipe adjustment rules (e.g., add 1 ml of seasoning if the temperature is below 15°C, or add 30 seconds to the cooking time if the humidity is above 60%).

[1277] The server adds adjustment values ​​based on weather information to recipes that have specific numerical values.

[1278] Update each step of the recipe and generate the adjusted recipe data.

[1279] 7. The device displays the adjusted recipe to the user.

[1280] The terminal receives the adjusted recipe data from the server.

[1281] The device displays the adjusted recipe on the cooking instructions screen. For example, it might say, "Add chicken and 16ml of seasoning, and heat over medium heat (heat level 3) for 5 minutes and 30 seconds."

[1282] 8. The user cooks according to the adjusted recipe.

[1283] The user cooks according to the specific and adjusted recipe displayed.

[1284] By following precise steps and numerical values, users can achieve a high success rate in cooking.

[1285] Specific example

[1286] For example, when making "chicken curry," a typical recipe might instruct you to "use one tablespoon of curry powder" or "sauté over medium heat for 5 minutes." The AI ​​then converts this into specific values ​​such as 15ml of curry powder and 5 minutes at heat level 3. Furthermore, it makes adjustments such as adding 1ml of seasoning if the current temperature is below 15℃, or adding 30 seconds to the cooking time if the humidity is above 60%, ensuring that delicious food can be consistently produced in any environment.

[1287] This system enables even cooking novices to achieve professional-level results while also offering the flexibility to adapt to different environmental conditions.

[1288] The following describes the processing flow.

[1289] Step 1:

[1290] The user launches the application and displays a list of cooking recipes. The user selects the "Chicken Curry" recipe from the list, and the device sends the selected recipe information to the server.

[1291] Step 2:

[1292] The server retrieves the corresponding recipe information from the database based on the recipe ID received from the terminal. The server analyzes the recipe content and detects abstract expressions such as "one tablespoon" or "cook over medium heat for 5 minutes." The detected expressions are then listed.

[1293] Step 3:

[1294] The server passes the detected abstract expressions to the generating AI as input. The generating AI converts "one tablespoon" into specific numerical values, such as 15 ml, and "5 minutes over medium heat" into "5 minutes at heat level 3." The server then incorporates these converted specific values ​​into the recipe.

[1295] Step 4:

[1296] The device activates GPS to obtain the user's current location. The device obtains the location information and sends the latitude and longitude data to the server.

[1297] Step 5:

[1298] The device calls a weather API to obtain weather information for its current location. Specifically, it retrieves meteorological data such as current temperature, humidity, and atmospheric pressure. The device then sends the obtained weather information to the server.

[1299] Step 6:

[1300] The server analyzes the weather information it has acquired. The server applies recipe adjustment rules (e.g., add 1 ml of seasoning if the temperature is below 15°C, add 30 seconds to the cooking time if the humidity is above 60%) to adjust the values ​​in the recipe. The server updates each step of the recipe based on the specific values ​​and generates the adjusted recipe data.

[1301] Step 7:

[1302] The terminal receives the adjusted recipe data from the server. The terminal displays the adjusted recipe to the user. Specifically, it displays "Add chicken and 16ml of seasoning, and heat over medium heat (heat level 3) for 5 minutes and 30 seconds."

[1303] Step 8:

[1304] The user cooks according to the specific and adjusted recipe displayed. By following the precise steps and numerical values, the user can achieve a high success rate in cooking.

[1305] Each of these steps helps the system to support users in consistently creating delicious meals, regardless of the environment.

[1306] (Example 1)

[1307] 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".

[1308] In recent years, there has been a growing need for easy-to-use cooking methods that allow even users with limited culinary knowledge and experience to prepare delicious meals. However, the results are often inconsistent due to abstract recipe instructions and environmental differences (such as climate variations). Furthermore, there is no clear method for incorporating acquired climate information into recipes. Therefore, there is a need to develop a system that enables anyone to consistently produce high-quality meals.

[1309] 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.

[1310] In this invention, the server includes means for the user to select a recipe they wish to make, means for converting abstract expressions into concrete numerical values ​​using a generative AI model, and means for acquiring environmental information of the current location using GPS and climate data. This allows the user to be provided with an adjusted recipe indicated by concrete numerical values, enabling the creation of consistently delicious dishes even under different climate conditions.

[1311] A "user" is someone who uses the system to create a dish.

[1312] A "recipe to be made" refers to the steps of a dish that the user selects and which the system then processes.

[1313] A "generative AI model" is an artificial intelligence model designed to give concrete form to abstract expressions that are input into it.

[1314] "Abstract expressions" refer to expressions in a recipe that do not specify concrete numerical values. Examples: "one tablespoon," "cook over medium heat for 5 minutes," etc.

[1315] "Specific numerical values" refer to numbers that accurately represent the cooking steps and quantities in a recipe. Examples: "15ml", "5 minutes at heat level 3", etc.

[1316] GPS is a global positioning system used to determine a user's current location.

[1317] "Climate data" refers to data that includes environmental information such as temperature, humidity, and precipitation.

[1318] "Environmental information" refers to climate data for the current location.

[1319] "Adjusting" means changing the numerical values ​​of a recipe based on the acquired environmental information.

[1320] "Data storage means" refers to a method or apparatus for saving and reusing the numerical values ​​of a modified recipe.

[1321] "Multiple rules" refers to several criteria or guidelines for adjusting recipe values ​​based on acquired environmental information.

[1322] A "prompt message" is a text input to a generative AI model, intended to prompt the model to transform abstract expressions.

[1323] This invention is a system in which a user, a terminal, and a server work together to convert cooking recipes into specific numerical values ​​and further adjust the recipes based on climate information of the current location. The following hardware and software are used to implement this system.

[1324] The user launches the application on a device such as a smartphone or tablet. This application allows the user to access a screen where they can select a cooking recipe. For example, if the user selects the "Chicken Curry" recipe, the device sends that information to the server. The server retrieves the recipe information from its database based on the recipe ID and analyzes the recipe content. From the analyzed recipe content, it detects abstract expressions such as "one tablespoon" and "cook over medium heat for 5 minutes."

[1325] The server uses a generative AI model (e.g., GPT-4) to convert this abstract expression into concrete numerical values. Input to this AI model is in the form of prompt statements. For example, if the prompt statement "Convert the expression 'one tablespoon' into a concrete numerical value" is passed to the generative AI model, "one tablespoon" will be converted to 15 ml, and "5 minutes over medium heat" will be converted to "5 minutes at heat level 3". The server then reflects these conversion results in the original recipe.

[1326] Furthermore, the device uses GPS functionality to obtain the user's current location. Based on this location information, the device calls a weather API (for example, the OpenWeatherMap API) to obtain climate data such as temperature and humidity at the current location. This climate data is sent to a server, which performs analysis. Based on the results of this analysis, the recipe is adjusted. For example, if the temperature is below 15°C, the amount of seasoning is increased by 1 ml, and if the humidity is above 60%, the cooking time is increased by 30 seconds, according to these rules.

[1327] The adjusted recipe is sent back from the server to the terminal and displayed on the terminal. The displayed recipe includes specific numerical values, such as "Add chicken and 16 ml of seasoning, and heat over medium heat (heat level 3) for 5 minutes and 30 seconds." The user then cooks according to these instructions.

[1328] This system enables even cooking novices to achieve professional-level results while also offering the flexibility to adapt to different environmental conditions.

[1329] The flow of the specific processing in Example 1 will be explained using Figure 11.

[1330] Step 1:

[1331] The user selects a cooking recipe. The user launches the application on their device and selects "Chicken Curry" from the displayed list of cooking recipes. The device receives the selected recipe ID as input and sends it to the server. Specifically, the user performs a tap operation, and the device generates an HTTP request and sends the recipe ID to the server.

[1332] Step 2:

[1333] The server receives and analyzes the recipe. The server uses the recipe ID received from the terminal as input to retrieve the corresponding recipe information from the database. The retrieved recipe information includes abstract expressions such as "one tablespoon" and "5 minutes over medium heat." The server analyzes these abstract expressions and creates a list. Data processing involves string manipulation to extract abstract keywords.

[1334] Step 3:

[1335] The server uses a generative AI model to convert abstract expressions into concrete numerical values. The server generates a prompt and passes the abstract expression as input to the generative AI model. An example of such a prompt is, "Please convert the expression 'one tablespoon' into a concrete numerical value." Based on the prompt, the generative AI model converts "one tablespoon" to 15 ml and "5 minutes over medium heat" to "5 minutes at heat level 3," and outputs these to the server. The server then incorporates this output into the original recipe.

[1336] Step 4:

[1337] The device acquires GPS information. While the user carries the device, the device activates its internal GPS module and acquires current location information as input. The acquired latitude and longitude information is sent to the server in HTTP request format. Specifically, the device activates its GPS sensor, periodically captures location information, and sends it to the server.

[1338] Step 5:

[1339] The device obtains weather information for its current location. Based on this location information, the device calls a weather API to obtain environmental information such as temperature and humidity. For example, it uses the OpenWeatherMap API. This obtained climate data is sent to the server as input. The device generates an HTTP request, sends the request to the weather API, parses the resulting JSON data, extracts the necessary information, and sends it to the server.

[1340] Step 6:

[1341] The server analyzes weather information and adjusts recipes accordingly. The server analyzes environmental information such as temperature and humidity. Based on this analysis, the server applies recipe adjustment rules. For example, if the temperature is below 15°C, it increases the amount of seasoning by 1 ml, and if the humidity is above 60%, it increases the cooking time by 30 seconds. The server uses these adjusted values ​​to update the recipe and generate new recipe data. Data processing involves adding or updating numerical values ​​in the recipe data.

[1342] Step 7:

[1343] The terminal displays the adjusted recipe to the user. The terminal receives the adjusted recipe data from the server as input and displays specific numerical values ​​and instructions on the cooking procedure screen. For example, it might display, "Add chicken and 16ml of seasoning, and heat over medium heat (heat level 3) for 5 minutes and 30 seconds." The terminal parses the received JSON data and displays it on the screen.

[1344] Step 8:

[1345] The user cooks according to the adjusted recipe. The user follows the specific and adjusted recipe displayed on the device. The user performs specific actions such as using measuring cups and adjusting the heat, and completes the dish according to the instructions. By following the appropriate procedures and values, the user can produce dishes of consistent quality.

[1346] (Application Example 1)

[1347] 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".

[1348] Traditional cooking recipes often standardize factors such as climate conditions, location, specific ingredient amounts, and cooking times, which can lead to variations in the quality and final result of dishes. Furthermore, the consistency of dishes in physical restaurants is often inconsistent, due to differences among cooking staff, making it difficult to provide customers with consistent quality. To address these issues, a system is needed that adjusts recipes based on real-time climate information and provides cooking staff with specific and consistent instructions.

[1349] 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.

[1350] In this invention, the server includes means for the user to select a recipe they wish to make, means for converting abstract expressions into concrete numerical values ​​using a generative AI, means for acquiring climate information of the current location using GPS and weather information, means for adjusting the numerical values ​​of the recipe based on the acquired climate information, means for displaying the adjusted recipe to the user, and means for displaying the adjusted recipe on a display device in a physical store. This makes it possible to consistently provide high-quality food in any environment.

[1351] A "user" is an individual or a kitchen staff member at a physical restaurant who selects a recipe and uses the system to prepare a meal.

[1352] A "recipe" is a document that contains specific instructions and information about ingredients for preparing a dish.

[1353] "Generative AI" is an artificial intelligence model that converts abstract expressions into concrete numbers or instructions.

[1354] "Abstract expressions" are those that lack specific numerical values, such as "one tablespoon" or "5 minutes over medium heat."

[1355] "Specific numerical values" refer to clear, quantified instructions such as "15ml" or "5 minutes at heat level 3" that are converted by the generating AI.

[1356] GPS is a global positioning system used to determine one's location on Earth.

[1357] "Weather information" refers to meteorological data such as temperature, humidity, and wind speed at your current location.

[1358] "Climate information" refers to environmental data based on acquired weather information.

[1359] "Adjusting the values" means changing the amount of ingredients or cooking time in a recipe based on climate information.

[1360] A "display device" is a screen device used to display the adjusted recipe and cooking instructions.

[1361] This invention is a system for converting abstract recipe instructions into concrete numerical values ​​when a user is cooking, and further adjusting the recipe based on climate information of the user's current location. Specifically, it is implemented in the following steps.

[1362] First, the user launches the application using a device (such as a smartphone or tablet) and displays a list of cooking recipes. When the user selects a recipe they want to make, the recipe information is sent from the device to the server. The server retrieves the corresponding recipe information from the database based on the recipe ID and analyzes the recipe content. At this time, the server uses a generation AI to detect abstract expressions such as "one tablespoon" or "5 minutes over medium heat" and converts them into specific numerical values ​​such as "15 ml" or "5 minutes at heat level 3".

[1363] Next, the device uses GPS to obtain the user's current location and calls a weather API (e.g., WeatherAPI) to retrieve weather information for the current location. The obtained climate information is sent from the device to the server, which adjusts the recipe values ​​based on this information. Specifically, rules such as adding 1 ml of seasoning if the temperature is below 15°C, or adding 30 seconds to the cooking time if the humidity is above 60%, are applied.

[1364] The adjusted recipe is sent from the server to the terminal and displayed on the terminal's display device. This display is also shown on the display device in the physical store. By following the displayed, specific, and adjusted recipe instructions, the cooking staff can consistently provide high-quality dishes that are adapted to the environment.

[1365] The hardware required includes a smartphone, tablet, display device, GPS module, and an internet connection to obtain weather information. The software used includes a recipe database, a generative AI model, a weather API, and a dedicated cooking assistance application.

[1366] As a concrete example, if a user wants to make "chicken curry," and uses the application to confirm their current location is Tokyo, and obtains weather information indicating a temperature of 10°C and humidity of 70%, the original recipe instruction of "one tablespoon of curry powder" will be converted to "16 ml," and "sauté over medium heat for 5 minutes" will be adjusted to "sauté over medium heat (heat level 3) for 5 minutes and 30 seconds."

[1367] Examples of prompt statements to input into the generative AI model are as follows:

[1368] Example of a prompt:

[1369] Input: "One tablespoon"

[1370] Output: "15ml"

[1371] Input: "5 minutes over medium heat"

[1372] Output: "5 minutes with firepower level 3"

[1373] This will enable us to consistently provide high-quality food in our physical stores as well.

[1374] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[1375] Step 1:

[1376] The user launches the application and displays a list of cooking recipes. When the user selects a cooking recipe they want to make, the recipe information is sent from the device to the server.

[1377] Input: User-selected recipe ID

[1378] Output: Recipe ID to send to the server

[1379] Specific operation: When the user selects "Chicken Curry," the device automatically sends its recipe ID to the server.

[1380] Step 2:

[1381] Based on the recipe ID received from the terminal, the server retrieves the corresponding recipe information from the database and analyzes the recipe content.

[1382] Input: Recipe ID sent to the server

[1383] Output: Recipe information (including abstract expressions)

[1384] Specific operation: The server executes a database query to retrieve recipe information corresponding to the recipe ID. It then analyzes the retrieved recipe content and lists abstract expressions such as "one tablespoon" or "cook over medium heat for 5 minutes."

[1385] Step 3:

[1386] The server takes abstract representations as input to the generated AI model and converts them into concrete numerical values.

[1387] Input: Abstract expressions (e.g., "one tablespoon", "cook over medium heat for 5 minutes")

[1388] Output: Specific numerical values ​​(e.g., "15ml", "5 minutes at heat level 3")

[1389] Specific operation: The server takes prompt text such as "one tablespoon" as input to the generating AI model and obtains "15ml" as output. This operation is performed for each abstract expression, converting them into concrete numerical values.

[1390] Step 4:

[1391] The device uses GPS to obtain its current location and then calls a weather API to retrieve weather information for that location.

[1392] Input: User's current location (GPS data)

[1393] Output: Weather information for your current location (e.g., temperature 10°C, humidity 70%)

[1394] Specific operation: The device activates its GPS module and obtains the latitude and longitude of its current location. Then, it sends a request to the weather API to obtain weather information such as temperature, humidity, and wind speed.

[1395] Step 5:

[1396] The device sends the acquired weather information to the server, which then adjusts the recipe values ​​based on this information.

[1397] Input: Weather information for your current location

[1398] Output: Adjusted recipe information

[1399] Specific operation: Based on the acquired climate information, the server applies specific rules to adjust the recipe values ​​(e.g., if the temperature is 15°C or below, add 1 ml of seasoning; if the humidity is 60% or above, add 30 seconds to the cooking time).

[1400] Step 6:

[1401] The adjusted recipe information is sent to the terminal and displayed on the terminal's display device. It is also displayed on the display devices in the physical store.

[1402] Input: Adjusted recipe information

[1403] Output: Screen showing the adjusted recipe instructions.

[1404] Specific operation: The terminal displays the adjusted recipe information received from the server on its screen and also transmits that information to the display device in the physical store.

[1405] Step 7:

[1406] The cooking staff will prepare the food according to the specific and adjusted recipe instructions provided.

[1407] Input: Adjusted recipe instructions

[1408] Output: High-quality cuisine

[1409] Specific actions: Cooking staff will follow the specific recipe instructions displayed on the terminal and display device to carry out the cooking procedure.

[1410] 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.

[1411] This invention is a cooking support system in which the user, terminal, server, and emotion engine work together to convert abstract recipe descriptions into concrete numerical values ​​and adjust the recipe based on the current location's climate information and the user's emotional state. This system is implemented in the following steps.

[1412] Overall system flow

[1413] 1. The user selects a cooking recipe.

[1414] The user launches the application and displays the cooking recipe list screen.

[1415] The user selects the "Chicken Curry" recipe.

[1416] The device sends the selected recipe information to the server.

[1417] 2. The server receives and analyzes the recipe.

[1418] The server retrieves the corresponding recipe information from the database based on the recipe ID received from the terminal.

[1419] The server analyzes the recipe content and detects abstract expressions such as "one tablespoon" or "cook over medium heat for 5 minutes."

[1420] List the detected expressions.

[1421] 3. The server uses AI to convert the data into specific numerical values.

[1422] The server passes the detected abstract representations to the generating AI as input.

[1423] The generating AI converts "one tablespoon" into specific numerical values ​​such as 15 ml, and "5 minutes over medium heat" into "5 minutes at heat level 3."

[1424] The server then reflects the specific numerical values ​​that have been converted into the recipe.

[1425] 4. The device acquires GPS information.

[1426] The device activates GPS to obtain the user's current location.

[1427] The device acquires its current location information and sends its latitude and longitude data to the server.

[1428] 5. The device obtains weather information for its current location.

[1429] The device calls a weather API to obtain weather information for its current location. Specifically, it retrieves meteorological data such as current temperature, humidity, and atmospheric pressure.

[1430] The device sends the weather information it has acquired to the server.

[1431] 6. The server adjusts the recipe based on weather information.

[1432] The server analyzes the weather information it has acquired.

[1433] The server adjusts the recipe values ​​by applying recipe adjustment rules (e.g., add 1 ml of seasoning if the temperature is below 15°C, add 30 seconds to the cooking time if the humidity is above 60%).

[1434] The server updates each step of the recipe based on specific numerical values ​​and generates the adjusted recipe data.

[1435] 7. The device uses an emotion engine.

[1436] The device activates an emotion engine to estimate the user's emotional state by analyzing their facial expressions, voice, and other data.

[1437] The emotion engine analyzes the user's emotional state and sends that information to the server.

[1438] 8. The server further adjusts the recipe based on emotional information.

[1439] The server receives emotional information and dynamically changes the cooking procedure and comments based on the user's emotional state.

[1440] For example, if a user is feeling stressed, simpler instructions or encouraging comments will be displayed.

[1441] 9. The device displays the adjusted recipe to the user.

[1442] The terminal receives the adjusted recipe data from the server.

[1443] The device displays the adjusted recipe on the cooking instructions screen. Specifically, it will display instructions such as, "Add chicken and 16ml of seasoning, and heat over medium heat (heat level 3) for 5 minutes and 30 seconds."

[1444] 10. The user cooks according to the adjusted recipe.

[1445] The user cooks according to the specific and adjusted recipe displayed.

[1446] By following precise steps and numerical values, users can achieve a high success rate in cooking.

[1447] Specific example

[1448] For example, when making "chicken curry," a typical recipe might instruct you to "use one tablespoon of curry powder" or "sauté over medium heat for 5 minutes." The AI ​​generator converts this into specific numbers, such as 15ml of curry powder or 5 minutes at heat level 3. Furthermore, if the current temperature is below 15℃, it adds 1ml of seasoning, and if the humidity is above 60%, it adds 30 seconds to the cooking time. In addition, if the emotion engine detects the user's stress level, it simplifies the cooking procedure and displays advice and encouraging comments such as "lower the temperature," allowing the user to cook in a relaxed state.

[1449] This system enables even cooking novices to achieve professional-level results while also offering the flexibility to adapt to different environmental conditions and the user's emotional state.

[1450] The following describes the processing flow.

[1451] Step 1:

[1452] The user launches the application and displays a list of cooking recipes. The user selects the "Chicken Curry" recipe from the list, and the device sends the selected recipe information to the server.

[1453] Step 2:

[1454] The server retrieves the corresponding recipe information from the database based on the recipe ID received from the terminal. The server analyzes the recipe content and detects abstract expressions such as "one tablespoon" or "cook over medium heat for 5 minutes." The detected expressions are then listed.

[1455] Step 3:

[1456] The server passes the detected abstract expressions to the generating AI as input. The generating AI converts "one tablespoon" into specific numerical values, such as 15 ml, and "5 minutes over medium heat" into "5 minutes at heat level 3." The server then incorporates these converted specific values ​​into the recipe.

[1457] Step 4:

[1458] The device activates GPS to obtain the user's current location. The device obtains the location information and sends the latitude and longitude data to the server.

[1459] Step 5:

[1460] The device calls a weather API to obtain weather information for its current location. Specifically, it retrieves meteorological data such as current temperature, humidity, and atmospheric pressure. The device then sends the obtained weather information to the server.

[1461] Step 6:

[1462] The server analyzes the weather information it has acquired. The server applies recipe adjustment rules (e.g., add 1 ml of seasoning if the temperature is below 15°C, add 30 seconds to the cooking time if the humidity is above 60%) to adjust the values ​​in the recipe. The server updates each step of the recipe based on the specific values ​​and generates the adjusted recipe data.

[1463] Step 7:

[1464] The device captures the user's facial expressions and voice, and uses an emotion engine to analyze the user's emotional state. The device then sends the results of the emotion engine's analysis to the server.

[1465] Step 8:

[1466] The server receives emotional information and further adjusts the recipe based on the user's emotional state. For example, if the user is feeling stressed, the server may simplify the cooking steps and add encouraging comments. Specifically, it might offer advice such as, "Lower the temperature and give yourself more time to relax."

[1467] Step 9:

[1468] The device receives the adjusted recipe data from the server. The device displays the adjusted recipe on the cooking procedure screen. Specifically, it displays "Add chicken and 16ml of seasoning, and heat over medium heat (heat level 3) for 5 minutes and 30 seconds."

[1469] Step 10:

[1470] The user cooks according to the displayed, specific, and adjusted recipe. By following the precise steps and numerical values, the user can achieve a high success rate in cooking. Furthermore, adjustments based on the user's emotional state can improve their satisfaction and sense of security during cooking.

[1471] Each of these steps helps the system to consistently create delicious dishes regardless of the user's environment or emotional state.

[1472] (Example 2)

[1473] 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".

[1474] Conventional cooking support systems have a problem in that abstract recipes are not translated into concrete numerical values, forcing users to rely on experience when cooking. Furthermore, because recipes are not adjusted to take into account the current climate information or the user's emotional state, there are issues such as inconsistent cooking quality and a failure to reduce user stress during cooking.

[1475] The identification processing performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for selecting a recipe that the user wants to cook, means for converting abstract expressions into concrete numerical values ​​using a generation AI, means for acquiring climate information of the current location using GPS and weather information, means for adjusting the numerical values ​​of the recipe based on the acquired climate information, means for analyzing the user's emotional state and further adjusting the recipe based on that information, and means for displaying the adjusted recipe to the user. As a result, even beginners in cooking can cook based on concrete numerical values, and by providing an optimal recipe that corresponds to climate information and emotional state, it is possible to improve the quality of the cooking and reduce user stress.

[1476] A "user" is the entity that uses the system to select a cooking recipe and then performs the cooking.

[1477] A "recipe" is information that describes the steps and ingredients for cooking, and includes specific numerical values ​​and instructions such as cooking time.

[1478] "Generative AI" refers to artificial intelligence technology that includes algorithms and models for converting abstract expressions into concrete numerical values.

[1479] "Abstract descriptions" are those that lack clear numerical values ​​or specific instructions, such as "one tablespoon" or "cook over medium heat for 5 minutes."

[1480] "Specific numerical values" refer to clear and measurable numbers such as 15ml or 5 minutes at heat level 3.

[1481] GPS is a global positioning system used by devices to obtain location information.

[1482] "Weather information" refers to meteorological data for a specific location, such as current temperature, humidity, and atmospheric pressure.

[1483] "Climate information" refers to data that indicates the environmental conditions of the current location based on GPS and weather information.

[1484] "Emotional state" refers to data that represents the user's stress level and emotional state, and is obtained through facial expression and voice analysis.

[1485] "Cooking instructions" refer to a set of steps and actions required to complete a dish.

[1486] The "adjusted recipe" refers to the final recipe, which has been refined and made concrete based on the acquired climate and sentiment data.

[1487] This invention is a system that improves the user's cooking experience through the cooperation of the user, terminal, server, and emotion analysis engine. This system converts abstract expressions of cooking recipes into concrete numerical values ​​and adjusts the recipes based on the user's current location's climate information and their emotional state.

[1488] Hardware and software to be used

[1489] This system utilizes devices such as smartphones and tablets, internet-connected servers, GPS modules, weather APIs, generative AI models, and sentiment analysis engines. Specifically, the following common hardware and software are used:

[1490] Devices: Smartphones, tablets, PCs

[1491] Server: A server on the internet

[1492] Generative AI models: AI algorithms (e.g., GPT-3)

[1493] Emotion analysis engine: Facial expression recognition software, voice analysis tool

[1494] GPS module: GPS function built into the device

[1495] Weather API: Weather data service (e.g., OpenWeatherMap API)

[1496] Data processing and data calculation

[1497] The user launches the application and selects a cooking recipe. The device sends the selected recipe information to the server. The server retrieves the recipe information from its database based on the received recipe ID and uses a generative AI model to convert the abstract representation into concrete numerical values.

[1498] The device activates its GPS module to obtain its current location and sends the latitude and longitude data to the server. The server then uses this information to call a weather API to obtain weather information for the current location (temperature, humidity, atmospheric pressure, etc.). Based on the obtained weather information, it adjusts the recipe values ​​by applying pre-configured rules.

[1499] Next, the device activates its emotion analysis engine, analyzes the user's facial expressions and voice to estimate their emotional state, and sends this information to the server. The server then adjusts the recipe based on the received emotional information. If the user is experiencing stress, the server may simplify the procedure or add encouraging comments.

[1500] Finally, the device displays the adjusted recipe to the user, who then cooks according to it.

[1501] Examples of specific cases and prompt statements

[1502] For example, when making "chicken curry," a typical recipe includes abstract instructions such as "use one tablespoon of curry powder" or "sauté over medium heat for 5 minutes." The generative AI model converts these into specific numerical values, such as "15 ml of curry powder" or "5 minutes at heat level 3." Additionally, if the current temperature is below 15°C, 1 ml of seasoning is added, and if the humidity is above 60%, 30 seconds of cooking time is added.

[1503] Furthermore, if the emotion analysis engine detects a user's stress level, it will display simple actions such as "lower the temperature" or encouraging comments, allowing the user to cook in a relaxed state.

[1504] Examples of specific prompt messages:

[1505] "Please translate the following abstract phrases in the recipe into specific numbers: 'Add one tablespoon of curry powder,' 'Sauté over medium heat for 5 minutes.'"

[1506] "The recipe will be adjusted based on your current location's climate information. The current temperature is 12°C and the humidity is 65%. Please apply the corresponding adjustment rules."

[1507] "Users are feeling stressed. Please simplify the cooking instructions and add encouraging comments."

[1508] This system allows even cooking beginners to proceed with cooking based on precise numerical data, obtain optimal recipes that take into account climate conditions and emotional states, and ultimately achieve higher quality dishes.

[1509] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1510] Step 1:

[1511] The user selects a cooking recipe.

[1512] Specific actions:

[1513] The user launches the application and selects "Chicken Curry" from the list of recipes.

[1514] The device retrieves the selected recipe ID and sends a request containing that recipe ID to the server.

[1515] Input: Recipe ID

[1516] Output: Request sent to the server

[1517] Step 2:

[1518] The server receives and analyzes the recipe.

[1519] Specific actions:

[1520] The server receives the recipe ID sent from the terminal.

[1521] The server retrieves the corresponding recipe information from the database based on the recipe ID.

[1522] The server analyzes the recipe content and detects abstract expressions such as "one tablespoon" or "cook over medium heat for 5 minutes."

[1523] The server lists the detected abstract representations.

[1524] Input: Recipe ID

[1525] Output: Listed abstraction

[1526] Step 3:

[1527] The server uses AI generation to convert the data into specific numerical values.

[1528] Specific actions:

[1529] The server inputs a list of abstract representations into an AI model that generates them.

[1530] The generating AI model converts "one tablespoon" into specific numerical values ​​such as 15 ml and "5 minutes over medium heat" into "5 minutes at heat level 3".

[1531] The server reflects the specific numerical values ​​it generates into the recipe data.

[1532] Input: Listed abstract expressions

[1533] Output: Recipe converted to specific numerical values

[1534] Step 4:

[1535] The device acquires GPS information.

[1536] Specific actions:

[1537] The device activates its GPS module and obtains the user's current location information.

[1538] The terminal sends the acquired latitude and longitude data to the server.

[1539] Input: None (Location information obtained via GPS module)

[1540] Output: Latitude and longitude data

[1541] Step 5:

[1542] The device obtains weather information for its current location.

[1543] Specific actions:

[1544] The device uses a weather API to obtain weather information (temperature, humidity, atmospheric pressure, etc.) for its current location.

[1545] The device sends the weather information it has acquired to the server.

[1546] Input: Latitude and longitude data

[1547] Output: Weather information

[1548] Step 6:

[1549] The server adjusts the recipe based on weather information.

[1550] Specific actions:

[1551] The server analyzes the weather information that has been sent.

[1552] The server adjusts the values ​​by applying recipe adjustment rules (e.g., add 1 ml of seasoning if the temperature is below 15°C, add 30 seconds to the cooking time if the humidity is above 60%).

[1553] The server generates the adjusted recipe data.

[1554] Input: Weather information

[1555] Output: Adjusted recipe data

[1556] Step 7:

[1557] The device uses an emotion engine.

[1558] Specific actions:

[1559] The device uses its camera and microphone to collect the user's facial expressions and voice in real time.

[1560] The emotion engine analyzes the user's emotional state and sends the data to the server.

[1561] Input: Facial expression data, audio data

[1562] Output: User sentiment information

[1563] Step 8:

[1564] The server further adjusts the recipe based on emotional information.

[1565] Specific actions:

[1566] The server analyzes the user's emotional information received from the emotion engine.

[1567] The server takes into account the user's stress level and makes changes such as simplifying the cooking procedure.

[1568] The server generates the recipe data after final adjustments.

[1569] Input: User sentiment information

[1570] Output: Recipe data after final adjustments

[1571] Step 9:

[1572] The device displays the adjusted recipe to the user.

[1573] Specific actions:

[1574] The terminal receives the finalized recipe data from the server.

[1575] Based on the data received by the device, the cooking procedure screen displays specific steps.

[1576] Input: Recipe data after final adjustments

[1577] Output: The final adjusted recipe displayed on the terminal.

[1578] Step 10:

[1579] The user cooks according to the adjusted recipe.

[1580] Specific actions:

[1581] The user reviews the specific and customized recipe displayed on their device.

[1582] The user follows the instructions to cook and ultimately complete a dish with a high success rate.

[1583] Input: Recipe after final adjustments

[1584] Output: Finished dish

[1585] (Application Example 2)

[1586] 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".

[1587] Conventional cooking support systems have the problem of being unable to reflect climate information or the user's emotional state, leading to decreased user satisfaction under certain conditions. Furthermore, when converting recipes and cooking procedures into concrete numerical values, they remain abstract, making it difficult for users to accurately reproduce the dishes. In addition, there is a lack of features for personalized cooking suggestions and adjustments, resulting in a failure to adequately meet user needs.

[1588] 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.

[1589] In this invention, the server includes means for selecting a recipe that the user wants to cook, means for converting abstract expressions into concrete numerical values ​​using a generative AI, means for acquiring climate information of the current location using GPS and weather information, means for adjusting the numerical values ​​of the recipe based on the acquired climate information, means for displaying the adjusted recipe to the user, means for analyzing the user's emotional state using an emotion analysis engine and acquiring the analysis results, means for dynamically adjusting the recipe and suggested comments based on the acquired emotional state, and means for suggesting dishes to the user based on the climate information and emotional state. As a result, the user can obtain specific cooking procedures that are appropriate to the climate information and emotional state, thereby increasing user satisfaction.

[1590] "A means for users to select a recipe they want to make" refers to a function that allows users to select a recipe for their desired dish through an interface.

[1591] "A method for converting abstract expressions into concrete numerical values ​​using generative AI" refers to a technology for converting abstract cooking expressions such as "one tablespoon" or "5 minutes over medium heat" into specific numerical values ​​such as milliliters or cooking time.

[1592] "Means for obtaining climate information for the current location using GPS and weather information" refers to a function that uses a GPS device and a weather API to obtain the user's current location's latitude, longitude, and climate information (temperature, humidity, etc.).

[1593] "Methods for adjusting recipe values ​​based on acquired climate information" refers to technologies that adjust recipe ingredients, cooking times, etc., based on acquired climate information for the current location.

[1594] "Means for displaying the adjusted recipe to the user" refers to a function for displaying the specific adjusted recipe content on the user's device (such as a smartphone or tablet).

[1595] "A means of analyzing a user's emotional state using an emotion analysis engine and obtaining the analysis results" refers to a technology that uses a smartphone's camera and microphone to analyze the user's facial expressions and voice, estimate their emotional state, and obtain the results.

[1596] "Means for dynamically adjusting recipes and suggested comments based on acquired emotional states" refers to a technology that dynamically changes and adjusts recipe procedures and provided comments based on the user's emotional state.

[1597] "Means for suggesting dishes to a user based on climate information and emotional state" refers to a function that automatically suggests dishes suitable for the user based on acquired climate information and emotional state.

[1598] This invention relates to a system that suggests and adjusts cooking recipes suitable for a user under specific conditions. This system operates using a smartphone, GPS module, weather API, sentiment analysis engine, generative AI model, and the like.

[1599] System Configuration

[1600] 1. User terminal

[1601] Smartphone: Used as a user interface to accept user input.

[1602] GPS module: Obtains the user's current location.

[1603] Camera and microphone: Use an emotion analysis engine to analyze the user's emotional state.

[1604] 2. Server

[1605] Database: Stores cooking recipes and related data.

[1606] Generative AI model: Converts abstract cooking instructions into concrete numerical values.

[1607] Weather API: Retrieves weather information for your current location from an external weather data service.

[1608] Emotion analysis engine: A software module for analyzing a user's emotional state.

[1609] Processing flow

[1610] 1. Recipe Selection

[1611] The server displays a list of cooking recipes through the user's terminal, and the user selects the recipe they want.

[1612] 2. Data Acquisition

[1613] The smartphone's GPS module is used to obtain current location information, and a weather API is used to obtain weather information (temperature, humidity, etc.) for the current location.

[1614] 3. Emotion analysis

[1615] Using the smartphone's camera and microphone, the emotion analysis engine analyzes the user's facial expressions and voice to estimate their emotional state.

[1616] 4. Recipe adjustment

[1617] The server uses a generated AI model to convert abstract recipe instructions into concrete numbers and adjusts the recipe content based on acquired weather information and emotional states.

[1618] 5. Presentation to the user

[1619] The adjusted recipe is displayed on the user's device, and specific cooking instructions and encouraging comments are provided to the user.

[1620] Examples of specific cases and prompt statements

[1621] For example, if a user wants to make "chicken curry," the initial recipe might include abstract instructions such as "use one tablespoon of curry powder" or "sauté over medium heat for 5 minutes." The server then uses a generative AI model to convert these into specific numerical values ​​(e.g., 15 ml of curry powder, 5 minutes at heat level 3).

[1622] Furthermore, if the current temperature is low, 1 ml of seasoning will be added, and if the humidity is high, 30 seconds will be added to the cooking time. In addition, if the emotion analysis engine determines that the user is stressed, the cooking procedure will be simplified and advice or encouraging comments such as "lower the temperature" will be displayed.

[1623] Example of a prompt:

[1624] "To suggest a cooking genre, please tell me what kind of dishes would be suitable when the current temperature is 25 degrees Celsius."

[1625] "Please suggest dishes that can help users relax when they are feeling stressed."

[1626] In this way, the system can suggest and adjust the optimal dish according to the user's environment and emotional state.

[1627] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1628] Step 1:

[1629] The user selects a recipe. The user terminal displays a list of cooking recipes provided by the server on its interface. The user selects the desired recipe, and the terminal sends that information to the server. The input is the recipe ID selected by the user, and the output is confirmation that the information has been sent to the server.

[1630] Step 2:

[1631] The server receives and analyzes recipe information. Based on the recipe ID received from the terminal, the server retrieves the corresponding recipe information from the database and identifies the abstract expression. It then calls a generative AI model to convert the abstract expression into concrete numerical values. The input is the recipe ID, and the output is the recipe information converted into concrete numerical values.

[1632] Step 3:

[1633] The terminal obtains the user's current location. It activates the GPS module on the user's terminal, acquires location information (latitude and longitude), and sends this information to the server. The input is GPS data, and the output is latitude and longitude information.

[1634] Step 4:

[1635] The device obtains weather information for its current location. The device calls a weather API to retrieve weather information such as temperature, humidity, and atmospheric pressure for its current location, and sends it to the server. The input is latitude and longitude information, and the output is weather information.

[1636] Step 5:

[1637] The server adjusts the recipe based on weather information. The server analyzes the acquired weather information and applies recipe adjustment rules to adjust specific values. The input is weather information, and the output is the adjusted recipe information.

[1638] Step 6:

[1639] The device activates the emotion analysis engine. The device uses its camera and microphone to capture the user's facial expressions and voice, and uses the emotion analysis engine to analyze the user's emotional state. The analysis results are sent to the server. The input is the user's facial expressions and voice data, and the output is the emotional state.

[1640] Step 7:

[1641] The server further adjusts the recipe based on emotional information. The server receives emotional information and dynamically changes the recipe content and cooking procedure according to the user's emotional state. The input is emotional information, and the output is the final adjusted recipe information.

[1642] Step 8:

[1643] The terminal displays the adjusted recipe to the user. The terminal displays the adjusted recipe information received from the server on its interface, providing the user with specific cooking instructions and suggested comments. The input is the final adjusted recipe information, and the output is what is displayed to the user.

[1644] 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.

[1645] 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.

[1646] 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.

[1647] 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.

[1648] 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.

[1649] 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.

[1650] 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.

[1651] 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.

[1652] 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."

[1653] 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.

[1654] 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.

[1655] 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.

[1656] 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.

[1657] 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.

[1658] 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.

[1659] 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.

[1660] 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.

[1661] 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.

[1662] 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.

[1663] 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.

[1664] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[1665] The following is further disclosed regarding the embodiments described above.

[1666] (Claim 1)

[1667] A means for users to select the recipe they want to cook,

[1668] A means of converting abstract expressions into concrete numerical values ​​using generative AI,

[1669] A means of obtaining climate information for the current location using GPS and weather information,

[1670] A means of adjusting recipe values ​​based on acquired climate information,

[1671] A means of displaying the adjusted recipe to the user,

[1672] A system that includes this.

[1673] (Claim 2)

[1674] A means for having a database for storing and reusing the adjusted recipe values,

[1675] The system according to claim 1, including the following:

[1676] (Claim 3)

[1677] A means of adjusting recipe values ​​by applying multiple rules based on acquired climate information,

[1678] The system according to claim 1, including the following:

[1679] "Example 1"

[1680] (Claim 1)

[1681] A means for users to select the recipe they want to cook,

[1682] A means of converting abstract representations into concrete numerical values ​​using a generative AI model,

[1683] A means of acquiring environmental information of the current location using GPS and climate data,

[1684] A means of adjusting recipe values ​​based on acquired environmental information,

[1685] A means of displaying the adjusted recipe to the user,

[1686] A system that includes this.

[1687] (Claim 2)

[1688] It has a data storage means for saving and reusing the adjusted recipe values.

[1689] The system according to claim 1.

[1690] (Claim 3)

[1691] A means of adjusting recipe values ​​by applying multiple rules based on acquired environmental information,

[1692] A means for generating prompt sentences to be input to a generative AI model,

[1693] The system according to claim 1, including the following:

[1694] "Application Example 1"

[1695] (Claim 1)

[1696] A means for users to select the recipe they want to cook,

[1697] A means of converting abstract expressions into concrete numerical values ​​using generative AI,

[1698] A means of obtaining climate information for the current location using GPS and weather information,

[1699] A means of adjusting recipe values ​​based on acquired climate information,

[1700] A means of displaying the adjusted recipe to the user,

[1701] A means of displaying the adjusted recipe on a display device in a physical store,

[1702] A system that includes this.

[1703] (Claim 2)

[1704] A means for having a database for storing and reusing the adjusted recipe values,

[1705] The system according to claim 1, including the following:

[1706] (Claim 3)

[1707] A means of adjusting recipe values ​​by applying multiple rules based on acquired climate information,

[1708] A means of providing the adjusted recipe to the cooking staff at the physical store,

[1709] The system according to claim 1, including the following:

[1710] "Example 2 of combining an emotion engine"

[1711] (Claim 1)

[1712] A means for users to select the recipe they want to cook,

[1713] A means of converting abstract expressions into concrete numerical values ​​using generative AI,

[1714] A means of obtaining climate information for the current location using GPS and weather information,

[1715] A means of adjusting recipe values ​​based on acquired climate information,

[1716] A means of analyzing the user's emotional state and further adjusting the recipe based on that information,

[1717] A means of displaying the adjusted recipe to the user,

[1718] A system that includes this.

[1719] (Claim 2)

[1720] A means for having a database for storing and reusing the adjusted recipe values,

[1721] The system according to claim 1, including the following:

[1722] (Claim 3)

[1723] A means of adjusting recipe values ​​by applying multiple rules based on acquired climate and sentiment information,

[1724] The system according to claim 1, including the following:

[1725] "Application example 2 when combining with an emotional engine"

[1726] (Claim 1)

[1727] A means for users to select the recipe they want to cook,

[1728] A means of converting abstract expressions into concrete numerical values ​​using generative AI,

[1729] A means of obtaining climate information for the current location using GPS and weather information,

[1730] A means of adjusting recipe values ​​based on acquired climate information,

[1731] A means of displaying the adjusted recipe to the user,

[1732] A means for analyzing a user's emotional state using an emotion analysis engine and obtaining the analysis results,

[1733] A means of dynamically adjusting recipes and suggested comments based on acquired emotional states,

[1734] A means of suggesting dishes to the user based on climate information and emotional state,

[1735] A system that includes this.

[1736] (Claim 2)

[1737] The system according to claim 1, having a database for storing and reusing adjusted recipe values.

[1738] (Claim 3)

[1739] A means of adjusting recipe values ​​by applying multiple rules based on acquired climate information,

[1740] A means of generating an emotional response based on the acquired emotional state and reflecting it in the adjusted recipe,

[1741] The system according to claim 1, including the following: [Explanation of Symbols]

[1742] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means for users to select the recipe they want to cook, A means of converting abstract expressions into concrete numerical values ​​using generative AI, A means of obtaining climate information for the current location using GPS and weather information, A means of adjusting recipe values ​​based on acquired climate information, A means of displaying the adjusted recipe to the user, A system that includes this.

2. A means for having a database for storing and reusing the adjusted recipe values, The system according to claim 1, including the following:

3. A means of adjusting recipe values ​​by applying multiple rules based on acquired climate information, The system according to claim 1, including the following:

Images