System

Abstract

A system is provided.SOLUTION: A system comprising: means for analyzing a blood test result to identify missing nutrients when the blood test result is entered; means for suggesting a recipe to supplement the missing nutrients based on the missing nutrients; and means for displaying the suggested recipe to a user.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Until now, there has been no method to understand an individual's nutritional status based on blood test results and then suggest meals for appropriate nutritional intake based on that. Many people find it difficult to properly understand their own nutritional status and follow that dietary guide, which means that they are unable to adequately replenish missing nutrients, creating problems for maintaining and improving their health. Therefore, there is a need for a system that can identify missing nutrients based on blood test results and suggest appropriate meals based on that information. [Means for solving the problem]

[0005] In order to solve the above problems, the present invention provides the following means.

[0006] A system is provided that includes, upon input of blood test results, a means for analyzing the blood test results to identify nutrient deficiencies, a means for proposing recipes to supplement the nutrient deficiencies based on the nutrient deficiencies, and a means for displaying the proposed recipes to the user. The system is further characterized in that the blood test results are input manually or by file upload, and also includes a means for comparing the blood test results with normal nutrient ranges to identify nutrient deficiencies. This allows users to understand their own nutritional status and easily select meals based on that status, providing an effective method for maintaining and improving health.

[0007] "Blood test results" are numerical information indicating the concentrations of various nutrients and components obtained by analyzing blood.

[0008] "Analysis" is the process of performing calculations and comparisons based on input data to arrive at specific conclusions or information.

[0009] A "deficient nutrient" is a nutrient that is determined to be present in a relatively small amount compared to the normal range based on blood test results.

[0010] "Suggestion" means providing information on ingredients and dishes suitable for supplementing missing nutrients.

[0011] "Cooking" refers to a meal made by processing ingredients and following a set procedure.

[0012] "Input" refers to a user manually providing data or uploading a file.

[0013] "Manual input" refers to a method in which a user directly inputs data using the terminal interface.

[0014] "File upload" is a method of sending data in the form of an electronic file to a terminal and providing it to the system.

[0015] The "normal range" is the range of concentrations of nutrients and components that are generally considered to be in a healthy state.

[0016] A "database" is a system that organizes and stores a large amount of data, allowing that data to be searched and used as needed.

[0017] A "user" is an individual who uses the system to input blood test results and receive dietary suggestions based on their nutritional status. [Brief explanation of the drawings]

[0018] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0019] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

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

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

[0022] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0023] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0024] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0025] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0026] [First embodiment]

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

[0028] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0029] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0030] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0031] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

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

[0033] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

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

[0036] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0037] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0038] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0039] This invention is a system that identifies nutrient deficiencies based on a user's blood test results and suggests recipes to supplement those nutrients. This system supports the user's input of blood test data, analysis, suggestion generation, and result display.

[0040] 1. Data Entry

[0041] Users use the device to input or upload their blood test results. For manual input, users enter values ​​for iron, vitamin D, calcium, etc. into a form displayed on the device screen. For file upload, users upload an electronic file in CSV or Excel format to the device.

[0042] 2. Data Analysis

[0043] The terminal receives the entered blood test data, performs basic validation (checking the format and checking for abnormal values), and then sends the data to the server, which then begins processing to analyze the received blood test results.

[0044] The server first retrieves normal ranges for nutrient levels stored in a database—for example, "normal range for iron is 50-150 μg / dL" or "normal range for vitamin D is 20-50 ng / mL"—and then compares the user's blood test results with these normal ranges to identify any nutrient deficiencies.

[0045] 3. Proposal generation

[0046] Based on the identified nutrient deficiencies, the server searches its database for ingredients and dishes to fill them. For example, if an individual is iron deficient, it will select dishes containing spinach, lean meat, lentils, etc. The server creates a list of suitable dishes and compiles information about the ingredients, instructions, and nutritional value of each dish.

[0047] 4. Results display

[0048] The server sends the generated recipe suggestions to the device, which then displays the received suggestions on the screen for easy viewing by the user.

[0049] Specific examples

[0050] 1. The user enters the following blood test results into the terminal:

[0051] Iron: 30 μg / dL

[0052] Vitamin D: 15 ng / mL

[0053] Calcium: 10 mg / dL

[0054] 2. The device receives this data and sends it to the server.

[0055] 3. The server performs the analysis and identifies that there is an iron deficiency.

[0056] 4. Servers can choose dishes like spinach and lean meat stir-fry or lentil soup to help them get iron.

[0057] 5. The server sends this information to the terminal and displays it to the user:

[0058] "You're low in iron. Try making a spinach and lean meat stir fry or lentil soup."

[0059] This allows users to accurately understand their nutritional deficiencies and easily select and execute specific meals to supplement them.

[0060] The processing flow will be explained below.

[0061] Step 1: Data entry

[0062] Users enter blood test results on the device or upload them as a file.

[0063] In the case of manual input, the user inputs the numerical value of each nutrient into an input form on the terminal.

[0064] When uploading a file, the user selects and uploads a CSV or Excel file containing the blood test results.

[0065] Step 2: Send data

[0066] The terminal receives the entered blood test data and validates its format and content, for example, checking that the values ​​are entered in the correct format and that they do not contain any abnormal values.

[0067] Data that passes validation is converted into an appropriate format and sent to the server.

[0068] Step 3: Data analysis

[0069] The server connects to a database to obtain normal range information for nutrients in order to analyze the received blood test data.

[0070] The server compares the user's blood test results with the normal range and identifies the deficiency of each nutrient. For example, if the user's iron level is 30 μg / dL and the normal range is 50-150 μg / dL, it will determine that the user is iron deficient.

[0071] Step 4: Identify nutrient deficiencies

[0072] The server creates a list of nutrients (e.g., iron, vitamin D) that are lacking from the analysis results.

[0073] For each identified nutrient deficiency, the server searches its database for data on appropriate ingredients and dishes to compensate for it.

[0074] Step 5: Food suggestions

[0075] The server generates cooking recipes to supplement missing nutrients, including the name of the dish, a list of ingredients, cooking instructions, and nutritional information.

[0076] For example, dishes that can help replenish iron include "stir-fried spinach and lean meat" and "lentil soup."

[0077] Step 6: Send results

[0078] The server sends the generated recipe suggestions to the terminal.

[0079] The suggestions are formatted in a clear and user-friendly way.

[0080] Step 7: View the results

[0081] The device will then display the received recipe suggestions to the user. For example, it might say, "You're low in iron. Try making a stir-fry of spinach and lean meat or lentil soup."

[0082] Based on the displayed information, users can create suggested dishes and supplement any missing nutrients.

[0083] Example 1

[0084] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0085] Nutritional deficiencies can cause a variety of health problems, but it can be difficult to independently identify specific nutritional supplementation strategies based on individual blood test results. Furthermore, conventional methods require a lot of time and effort for manual data entry and analysis. Therefore, there is a need for a system that allows users to easily identify nutrient deficiencies based on their own blood test results and receive specific recipe suggestions based on those deficiencies.

[0086] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0087] In this invention, the server includes: means for a user to input or upload blood test results using a terminal; means for the terminal to validate the input blood test results and check for abnormal values; means for the terminal to send the validated blood test results to the server; means for the server to obtain normal range nutrient levels from a database and compare them with the blood test results to identify deficient nutrients; means for the server to search for ingredients and dishes to supplement the deficient nutrients and generate a list of dishes; and means for sending the recipe suggestions generated by the server to the terminal and displaying them to the user. This allows users to quickly and accurately identify deficient nutrients based on their blood test results and easily receive specific recipe suggestions to supplement them.

[0088] "User" refers to an individual who uses the system to input blood test results and receive information about nutrient deficiencies and recipe suggestions to compensate for them.

[0089] "Terminal" refers to a device used by a user to input or upload blood test results, and is responsible for validating input data and transmitting the data to a server.

[0090] The "server" refers to a computer that analyzes the received blood test results, identifies any missing nutrients, generates recipe suggestions to compensate for those nutrients, and sends them to the terminal.

[0091] "Blood test results" refers to data that indicates the numerical information of nutrients contained in the user's blood.

[0092] "Validation" refers to the process of checking whether the data format of the entered blood test results is correct and whether it contains any invalid or abnormal values.

[0093] "Database" refers to the collection of data that the server accesses to obtain normal range nutrient levels and cooking suggestion information.

[0094] "Normal range" refers to a value that represents a particular nutrient within a standard range of healthy levels.

[0095] "Deficient nutrients" refers to nutrients that are not within the normal range based on the user's blood test results.

[0096] "Cooking suggestions" refers to a list of specific dishes generated by the server to supplement missing nutrients, along with detailed information (ingredients, cooking instructions, nutritional information).

[0097] "Input" refers to the act of a user providing blood test results to the system via a terminal.

[0098] "Upload" refers to the act of a user sending blood test results in electronic file format to a terminal and having them imported into the system.

[0099] "Manual input" refers to the act of a user directly entering blood test result values ​​into a form displayed on the terminal screen.

[0100] "Comparison" refers to the process in which the server compares the user's blood test results with the normal range values ​​stored in the database to identify any nutrients that are lacking.

[0101] This invention relates to a system that identifies nutrient deficiencies based on a user's blood test results and suggests recipes to supplement those nutrients. This system supports the user's blood test data entry, analysis, suggestion generation, and result display.

[0102] Users use the device to input or upload their blood test results. For manual input, users enter values ​​such as iron, vitamin D, and calcium into a form displayed on the device screen. For file upload, users upload electronic files in CSV or Excel format to the device.

[0103] The terminal receives the entered blood test data and, after basic validation (checking the format and abnormal values), sends the data to the server. The server then begins the process of analyzing the received blood test results. The server first obtains the normal range of nutrient levels stored in the database. For example, it obtains values ​​such as "the normal range for iron is 50-150 μg / dL" and "the normal range for vitamin D is 20-50 ng / mL." It then compares the user's blood test results with these normal ranges to identify any nutrient deficiencies.

[0104] Based on the identified nutrient deficiencies, the server searches its database for ingredients and dishes to fill them. For example, if an individual is iron deficient, it will select dishes containing spinach, lean meat, lentils, etc. The server creates a list of suitable dishes and compiles information about the ingredients, instructions, and nutritional value of each dish.

[0105] The server sends the generated recipe suggestions to the device, which then displays the received suggestions on the screen for easy viewing by the user.

[0106] Specific examples

[0107] Specific examples are shown below.

[0108] 1. The user enters the following blood test results into the terminal:

[0109] Iron: 30 μg / dL

[0110] Vitamin D: 15 ng / mL

[0111] Calcium: 10 mg / dL

[0112] 2. The device receives this data and sends it to the server.

[0113] 3. The server performs the analysis and identifies that there is an iron deficiency.

[0114] 4. Servers can choose dishes like spinach and lean meat stir-fry or lentil soup to help them get iron.

[0115] 5. The server sends this information to the terminal and displays it to the user:

[0116] "You're low in iron. Try making a spinach and lean meat stir fry or lentil soup."

[0117] This allows users to accurately understand their nutritional deficiencies and easily select and execute specific meals to supplement them.

[0118] Prompt Sentence Examples

[0119] The following prompts can be used for the generative AI model:

[0120] "Please suggest meals for iron deficiency. User's blood test results are: Iron 30 μg / dL, Vitamin D 15 ng / mL, Calcium 10 mg / dL."

[0121] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0122] Step 1: Data entry

[0123] Users use the device to input or upload blood test results. For manual input, users enter values ​​such as iron, vitamin D, and calcium into a form displayed on the device screen. For file upload, users upload electronic files in CSV or Excel format to the device.

[0124] Specifically, when the user clicks the "Input" button, the data is imported into the system. The input data includes the numerical values ​​of the blood test results and is sent directly to the next step.

[0125] Step 2: Data Validation

[0126] The terminal receives the blood test data entered and performs basic validation. Validation includes checking the format and checking for abnormal values. For example, it checks whether the iron value is a number and whether the vitamin D value is entered in the correct units. Abnormally high or low values ​​are also detected.

[0127] Specifically, the terminal checks the data format and range, and if it detects any abnormal data, it displays an error message to the user. Only normal data will proceed to the next step.

[0128] Step 3: Send data

[0129] If the validation is successful, the terminal sends the data to the server. The data sent is the numerical information of the blood test results that have passed validation.

[0130] Specifically, the device displays a success message to the user and sends the data to the server via an HTTP request. The server receives this request and prepares to process the data.

[0131] Step 4: Database access and analysis

[0132] The server begins analyzing the received blood test results. First, it retrieves normal range nutrient levels from the database. Then it compares the user's blood test results with these normal ranges to identify any nutrient deficiencies. The input is the blood test results and the normal range data retrieved from the database. The output is a list of nutrients that are deficient.

[0133] Specifically, the server uses an SQL query to retrieve data from the database and performs a comparison operation to identify nutrient deficiencies.

[0134] Step 5: Proposal Generation

[0135] Based on the identified nutrient deficiencies, the server searches the database for ingredients and dishes to fill them. For example, if there is an iron deficiency, it will select dishes containing spinach, lean meat, lentils, etc. The server creates a list of appropriate dishes and compiles information about the ingredients, steps, and nutritional value of each dish. The input is a list of nutrient deficiencies, and the output is a list of dish suggestions.

[0136] Specifically, the server uses SQL queries and AI models to generate recipe suggestions, then formats them into a data list.

[0137] Step 6: View the results

[0138] The server sends the generated recipe suggestions to the device, which displays the received suggestions on the screen for easy viewing by the user. The input is a list of recipe suggestions, and the output is specific suggestion information displayed to the user.

[0139] Specifically, the device analyzes the data received from the server and displays it on the screen in an easy-to-read format for the user. The user can then check the screen and select and execute the suggested dishes.

[0140] (Application example 1)

[0141] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0142] In modern society, many people suffer from unbalanced diets and inadequate nutritional intake. It is difficult to accurately identify missing nutrients and select appropriate meals to supplement them. Furthermore, the time and effort required to actually prepare and cook such meals is a significant burden. This creates problems that hinder the maintenance and improvement of health.

[0143] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0144] In this invention, the server includes means for analyzing blood test results when the blood test results are input and identifying nutrient deficiencies, means for suggesting dishes to supplement the nutrient deficiencies based on the nutrient deficiencies, means for displaying the suggested dishes to the user, means for the user to directly order the suggested dishes, and means for delivering the ordered dishes to the user. This allows the user to easily understand their nutritional status, easily select and order appropriate meals to supplement their nutritional status, and further receive cooked meals at home.

[0145] "Blood test results" are data showing the values ​​of various nutrients and components measured using blood.

[0146] "Nutrients" are substances necessary for the growth and maintenance of the human body, and particularly refer to trace elements such as vitamins, minerals, and proteins.

[0147] "Analysis" is the process of breaking down and analyzing input data using calculations and statistics to extract meaningful information.

[0148] "Deficient nutrients" refers to nutrients that are determined to be deficient relative to the normal range reference value.

[0149] "Means for suggesting dishes" refers to methods for displaying and providing meal menus and combinations of ingredients suitable for supplementing specified nutrients.

[0150] A "means for displaying dishes" is a system or device that allows a user to visually view details of suggested dishes.

[0151] "Direct ordering" refers to the ability for a user to receive a dish suggestion and then immediately order the dish through an application or device.

[0152] "Delivery means" refers to the logistics system or delivery service used to deliver the ordered food to the specified location.

[0153] A "server" is a computer system that stores, processes, and communicates data over a network.

[0154] The present invention is a system that identifies nutrient deficiencies based on the user's blood test results, suggests meals to supplement those nutrients, and delivers the meals. An embodiment of the system will be described in detail below.

[0155] 1. Data Entry

[0156] Users input or upload their blood test results using a smartphone or other device. For manual input, users enter the required nutrient values ​​(e.g., iron, vitamin D, calcium, etc.) into a form displayed on the device screen. For file upload, users can upload electronic files in CSV or Excel format to the device.

[0157] 2. Data Analysis

[0158] The server receives the blood test data sent from the device and begins analysis. First, the server performs basic validation (checking the format and abnormal values). Then, it retrieves normal range nutrient levels from a database and compares them with the input blood test data to identify nutrient deficiencies. Here, data analysis tools such as Python and TENSORFLOW® are used.

[0159] 3. Proposal generation

[0160] The server searches the database for appropriate ingredients and dishes based on the identified nutrient deficiencies. For example, if you are iron deficient, it will select dishes containing spinach, lean meat, and lentils. The list of selected dishes includes ingredients, instructions, and nutritional information. Based on this information, it generates recipe suggestions for the user.

[0161] 4. Displaying results and ordering

[0162] The device will display the generated dish suggestions to the user, who can then review the suggested dishes and place an order directly from the device. By tapping the order button, the corresponding order information will be sent via the food delivery partner's API.

[0163] 5. Food delivery

[0164] The food delivery partner will prepare the ordered food and deliver it to the user's designated location. Users can check the delivery status in real time through the application.

[0165] Specific examples

[0166] For example, suppose a user enters blood test results showing iron levels of 30 μg / dL, vitamin D levels of 15 ng / mL, and calcium levels of 10 mg / dL. The system analyzes this and determines that the user is iron deficient. The system then suggests dishes to supplement the iron levels, such as spinach and lean meat stir-fry and lentil soup. The user can view these dishes on their smartphone screen and order the dishes displayed with the message, "You are low in iron. Try spinach and lean meat stir-fry or lentil soup."

[0167] Prompt Sentence Examples

[0168] "I have entered my blood test results, which show iron at 30 μg / dL, vitamin D at 15 ng / mL, and calcium at 10 mg / dL. Please suggest appropriate dishes based on the nutrients I am lacking."

[0169] This system allows users to easily understand their nutritional status, select the appropriate meals to supplement it, and order and receive them without any hassle.

[0170] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0171] Step 1: Data entry

[0172] Users can input or upload their blood test results using a smartphone or other device. For manual input, users enter values ​​such as iron, vitamin D, and calcium into the application form. For file upload, users can upload CSV or Excel format files to their device.

[0173] Input: Blood test results entered or uploaded by the user

[0174] Output: Blood test result data (manually entered values ​​or uploaded file contents)

[0175] Specific processing behavior:

[0176] The user opens the app on their smartphone and accesses the blood test result input screen.

[0177] Enter the required values ​​or select a file, then press the send button.

[0178] Step 2: Data validation and submission

[0179] The terminal receives the entered blood test data, performs basic validation, and then sends the validated data to the server.

[0180] Input: Blood test result data

[0181] Output: Validated blood test data sent to the server

[0182] Specific processing behavior:

[0183] The terminal checks the format of the input data and checks for invalid or missing values.

[0184] Data that has been successfully validated is sent to the server via an HTTP request.

[0185] Step 3: Data analysis and identification of nutrient deficiencies

[0186] The server analyzes the received blood test data and identifies any deficiencies in nutrients by comparing it with a database of nutrients in the normal range.

[0187] Input: Blood test data sent to the server

[0188] Output: List of nutrients that are lacking

[0189] Specific processing behavior:

[0190] The server retrieves the normal range for each nutrient from the database.

[0191] Compare received blood test results with normal ranges to identify nutrient deficiencies.

[0192] List any identified nutrient deficiencies.

[0193] Step 4: Generate dish suggestions

[0194] Based on the identified nutrient deficiencies, the server searches the database for appropriate ingredients and dishes to compensate for them and generates a list of dishes.

[0195] Input: List of nutrients you are deficient in

[0196] Output: A list of suggested dishes, including ingredients, instructions, and nutritional information

[0197] Specific processing behavior:

[0198] Search the database for ingredients and dishes that address missing nutrients.

[0199] Get detailed recipe information (ingredients, instructions, nutritional information).

[0200] Generate a list of suggested dishes.

[0201] Step 5: View results and place orders

[0202] The device displays the generated food suggestions to the user, who has the option to review the suggested dishes and order directly. The user's order information is sent via the food delivery partner's API.

[0203] Input: A list of dishes to suggest

[0204] Output: Detailed information about the food selected and ordered by the user

[0205] Specific processing behavior:

[0206] The terminal displays a list of suggested dishes to the user.

[0207] The user selects a dish and presses the order button.

[0208] Order information is sent to the food delivery partner's API.

[0209] Step 6: Food preparation and delivery

[0210] The food delivery partner prepares the ordered food and delivers it to the user's designated location. The user can check the delivery status in real time through the application.

[0211] Input: Details of the food ordered by the user

[0212] Output: The meal delivered to the user

[0213] Specific processing behavior:

[0214] The food delivery partner receives the order information and prepares the corresponding food.

[0215] The cooked food is delivered to the user's specified address.

[0216] The user can check the delivery status on the application and receive a notification that delivery has been completed.

[0217] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0218] This invention combines a system that identifies nutrient deficiencies based on the user's blood test results and suggests recipes to supplement those deficiencies with an emotion engine that recognizes the user's emotions. This system makes suggestions based on the user's emotional state, thereby realizing more personalized nutritional supplementation suggestions.

[0219] Data Entry and Emotion Recognition

[0220] Users use a device to input their blood test results or upload them as a file. There are two input methods: manual input and file upload. With manual input, users input the numerical values ​​of each nutrient into the input form on the device. With file upload, users select and upload a CSV or Excel file containing the blood test results.

[0221] Furthermore, the device is equipped with a camera and microphone, and the emotion engine uses these devices to recognize emotions from the user's facial expressions and voice. Facial recognition technology is used to determine emotions such as smile, anger, and sadness, and voice analysis is used to analyze the user's intentions and emotions.

[0222] Data transmission and analysis

[0223] The device sends the input blood test data and recognized emotion data to the server. The server analyzes the received blood test data and connects to a database to obtain normal nutrient range information. It then compares the user's blood test results with the normal range and identifies nutrient deficiencies.

[0224] Identifying nutrient deficiencies and suggesting recipes

[0225] The server uses the analysis results to create a list of nutrients (e.g., iron, vitamin D) that are lacking. Furthermore, the emotion engine customizes the suggested dishes based on the user's emotional state. For example, if the server detects that the user is under stress, it will suggest ingredients and dishes that have a relaxing effect.

[0226] The server creates a list of suitable dishes and compiles the ingredients, instructions, and nutritional information for each dish, including the dish name, ingredient list, cooking instructions, and nutritional information.

[0227] Results display

[0228] The server sends the generated cooking suggestions to the device, which then displays the received suggestions to the user. For example, it might say, "You're low in iron, and you seem to be under stress. I recommend making a stir-fry of spinach and lean meat, and some relaxing mint tea."

[0229] Specific examples

[0230] 1. The user enters the following blood test results into the terminal:

[0231] Iron: 30 μg / dL

[0232] Vitamin D: 15 ng / mL

[0233] Calcium: 10 mg / dL

[0234] 2. The device receives this data and sends it to the server.

[0235] 3. The server performs the analysis and identifies that there is an iron deficiency.

[0236] 4. The emotion engine recognizes the user's stress level from their facial expressions and voice.

[0237] 5. The server recommends a spinach and lean meat stir fry as a way to supplement iron intake, and also suggests adding mint tea to reduce stress.

[0238] 6. The server sends this information to the terminal and displays it to the user:

[0239] "You're iron deficient. You also seem to be under stress. I'd recommend making a spinach and lean meat stir fry and a relaxing mint tea."

[0240] This allows users to accurately identify their nutritional deficiencies, easily select and execute specific meals to supplement them, and even receive advice based on their emotional state.

[0241] The processing flow will be explained below.

[0242] Step 1: Data entry

[0243] Users enter blood test results on the device or upload them as a file.

[0244] In the case of manual input, the user inputs the numerical value of each nutrient (e.g., iron, vitamin D, calcium, etc.) into an input form on the terminal.

[0245] When uploading a file, the user selects and uploads a CSV or Excel file containing the blood test results.

[0246] Step 2: Recognize emotions

[0247] Using the device's built-in camera and microphone, the emotion engine analyzes the user's facial expressions and voice.

[0248] In facial expression analysis, the emotion engine identifies emotions from the user's facial micro-expressions (e.g., smile, sadness, anger).

[0249] In voice analysis, the emotion engine determines emotions from the user's voice patterns and tone.

[0250] Step 3: Send data

[0251] The terminal transmits the input blood test data and the recognized emotion data to the server.

[0252] The transmitted information includes the values ​​of each nutrient in the blood test and the determined emotion (e.g., stressed, relaxed).

[0253] Step 4: Data analysis

[0254] The server analyzes the received blood test data and connects to a database to obtain normal range information for nutrients.

[0255] The server compares the user's blood test results with normal ranges and identifies deficiencies in each nutrient.

[0256] Example: "The user's iron level is 30 μg / dL, which is insufficient compared to the normal range of 50-150 μg / dL."

[0257] Step 5: Identify nutrient deficiencies and choose meals

[0258] The server searches a database for appropriate ingredients and recipes based on the identified nutrient deficiencies.

[0259] The system customizes the best meal for the user based on the identified emotional state (e.g., stress state).

[0260] For example: "Choose a spinach and lean meat stir fry for iron and add mint tea to reduce stress."

[0261] Step 6: Generate dish suggestions

[0262] The server generates a recommendation containing details of the selected dish (dish name, ingredient list, cooking instructions, nutritional information).

[0263] Examples: "Spinach and lean meat stir fry" and "Relaxing mint tea."

[0264] Step 7: Send results

[0265] The server sends the generated recipe suggestions to the terminal.

[0266] The proposed information is formatted in a way that is easy for the user to review.

[0267] Step 8: View the results

[0268] The terminal displays the received recipe suggestions to the user.

[0269] For example: "You're low in iron and seem to be under stress. I'd recommend making a spinach and lean meat stir fry and a relaxing cup of mint tea."

[0270] Step 9: Execution

[0271] The user prepares the necessary ingredients and creates the dish based on the suggested recipe.

[0272] This allows users to supplement missing nutrients and eat meals that take into consideration their emotional state.

[0273] Example 2

[0274] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0275] While conventional systems can identify nutritional deficiencies based on blood test results and suggest meals to replenish these nutrients, they are unable to provide personalized recommendations that take into account the user's emotional state. As a result, they lack the information needed for users to truly achieve optimal nutritional intake.

[0276] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0277] In this invention, the server includes means for analyzing blood test results when the blood test results are input and identifying deficient nutrients, means for suggesting dishes to supplement the deficient nutrients based on the deficient nutrients, and means for recognizing the user's emotional state and making suggestions according to the emotional state. This makes it possible to not only suggest dishes to appropriately supplement the user's deficient nutrients, but also to make personalized suggestions according to the user's emotional state.

[0278] "Blood test results" are numerical information on various nutrients obtained by analyzing the user's blood.

[0279] "Nutrients" are chemicals or compounds that the body needs to maintain normal function.

[0280] A "deficient nutrient" is a nutrient that is identified as being below a standard value based on blood test results.

[0281] The "means of analysis" refers to devices and algorithms that analyze blood test results and evaluate the values ​​of each nutrient.

[0282] "Cooking" refers to food made to supplement the nutrients that the user is lacking, and specific ingredients and cooking methods are suggested.

[0283] The "user's emotional state" refers to the user's psychological or emotional state as recognized through facial expressions, voice analysis, etc.

[0284] An "emotion engine" is a software or hardware system for recognizing and analyzing a user's emotional state.

[0285] A "displaying means" is a device such as a display or screen that visually presents suggested information or messages to a user.

[0286] The "means of suggestion" refers to an algorithm or system that selects appropriate dishes and nutritional supplementation methods based on the analysis results and notifies the user of them.

[0287] The "server" is a computer system that processes blood test results and emotion data and generates proposals based on the analysis results.

[0288] The present invention provides a system for providing personalized nutritional support based on a user's blood test results and emotional state. The system mainly includes a user, a terminal, and a server.

[0289] First, users enter their blood test results using the device. There are two ways to enter blood test results: manual entry or file upload. For manual entry, users enter the numerical values ​​of each nutrient directly into the input form on the device. For file upload, users can upload files in CSV or Excel format.

[0290] The device is equipped with a camera and microphone, which are used to recognize the user's emotional state. The emotion engine uses facial recognition and voice analysis technologies to determine emotions such as smile, anger, and sadness, making it possible to grasp the user's emotional state in real time.

[0291] The device sends the input blood test data and recognized emotion data to the server. The server analyzes the received blood test data and connects to a database to obtain normal nutrient range information. It then compares the user's blood test results with the normal range and identifies any nutrient deficiencies.

[0292] The server then suggests dishes based on the nutritional deficiencies of the user. These suggestions are customized based on the user's emotional state as recognized by the emotion engine. For example, if the user is under stress, the server will suggest ingredients and dishes that have a relaxing effect.

[0293] To give a concrete example, suppose a user enters the following blood test results into a terminal:

[0294] Iron: 30 μg / dL

[0295] Vitamin D: 15 ng / mL

[0296] Calcium: 10 mg / dL

[0297] The device sends this data to the server, which analyzes it and identifies the iron deficiency. The emotion engine recognizes the user's stress level from their facial expressions and voice.

[0298] The server selects a dish of stir-fried spinach and lean meat to supplement iron, and suggests adding mint tea to reduce stress. The server sends this information to the terminal, which displays the following to the user:

[0299] "You're iron deficient. You also seem to be under stress. I'd recommend making a spinach and lean meat stir fry and a relaxing mint tea."

[0300] Here's an example prompt for a generative AI model:

[0301] Based on the user's blood test results and emotional data, we want to identify the nutrients that are lacking and suggest dishes that correspond to the user's emotional state. For example, the user is lacking iron and vitamin D, and is under stress. Please take this into consideration when suggesting appropriate dishes.

[0302] In this way, the present invention can suggest personalized recipes for nutritional supplementation based on the user's blood test results and emotional data, allowing the user to obtain specific methods for efficiently supplementing their nutritional deficiencies and receive appropriate advice based on their emotional state.

[0303] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0304] Step 1:

[0305] The user enters the blood test results.

[0306] Input: Blood test results (e.g., iron, vitamin D, calcium levels)

[0307] How it works: The user manually enters each value into the input form on the device, or selects and uploads a CSV / Excel file.

[0308] Output: The entered blood test result data is saved on the terminal.

[0309] Step 2:

[0310] The device uses a camera and microphone to recognize the user's emotional state.

[0311] Input: Camera video, microphone audio

[0312] How it works: The device's camera captures the user's facial expressions, and the microphone records the user's voice. These data are then sent to the emotion engine.

[0313] Output: User's emotional state (e.g., stress, sadness, joy, etc.) via the emotion engine

[0314] Step 3:

[0315] The terminal transmits the input blood test data and emotion data to the server.

[0316] Input: Blood test result data, emotional state data

[0317] How it works: The device encrypts this data and sends it to the server.

[0318] Output: The server receives the blood test data and emotion data.

[0319] Step 4:

[0320] The server analyzes the received blood test data.

[0321] Input: Blood test data

[0322] How it works: The server connects to the database and retrieves normal range information for each nutrient. This is then compared with the user's blood test results to identify any nutrient deficiencies.

[0323] Output: List of nutrients that are lacking

[0324] Step 5:

[0325] The server customizes food suggestions based on data from the emotion engine.

[0326] Input: List of nutrient deficiencies, emotional state data

[0327] How it works: The server applies an algorithm that suggests dishes based on the user's emotional state. For example, if stress is detected, it will suggest dishes that have a relaxing effect.

[0328] Output: A customized list of food suggestions (e.g., spinach stir-fry with lean meat, mint tea, etc.)

[0329] Step 6:

[0330] The server generates a list of suitable dishes and sends it to the device.

[0331] Input: Customized food suggestion list

[0332] Operation: The server converts the recipe list into a data format and sends it to the terminal.

[0333] Output: The device receives the list of recipe suggestions.

[0334] Step 7:

[0335] The terminal displays the cooking suggestions received from the server to the user.

[0336] Input: list of dish suggestions

[0337] How it works: The device decodes the suggestion list and visually displays it to the user.

[0338] Output: The user can visually see the suggested recipes (e.g., "You're low in iron and appear to be under stress. I suggest you make a spinach and lean meat stir fry and a relaxing mint tea.")

[0339] (Application example 2)

[0340] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0341] Conventional nutritional supplement suggestion systems identify nutrient deficiencies based on a user's blood test results and suggest recipes to supplement them. However, a user's emotional state can affect nutrient absorption and effectiveness, and suggestions that do not take emotional state into account may not achieve optimal results. Furthermore, entering blood test results can be cumbersome, and suggested dishes may not be appropriate for the user's emotional state. Furthermore, in the field of food delivery, there is a lack of recipe suggestions based on health status, which means that the system is unable to meet the diverse needs of users.

[0342] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for analyzing blood test results to identify nutrient deficiencies, means for suggesting dishes to supplement the identified nutrient deficiencies, and means for recognizing the user's emotions and customizing the dishes based on the user's emotional state. This makes it possible to suggest more personalized dishes that provide optimal nutrition based on the blood test results and the user's emotional state. Furthermore, by displaying the suggested dishes to the user and allowing them to directly order them through a food delivery service, a system is realized that reduces the user's effort and provides dishes that are suitable for their health and emotional state.

[0343] "Blood test results" is information that indicates the numerical values ​​of each nutrient analyzed based on the user's blood data.

[0344] "Means for identifying" refers to methods or devices that analyze blood test results to identify nutrients that are lacking.

[0345] The "means for suggesting recipes" refers to a method or device that provides the user with the optimum recipe to supplement the nutrients that are lacking.

[0346] The "display means" refers to a method or device that visually shows the suggested dishes and related information to the user.

[0347] "Means for recognizing emotions" refers to a method or device for determining the emotional state of a user from their facial expressions and voice.

[0348] The "means for customizing dishes according to emotional state" refers to a method or device for selecting and suggesting dishes that best suit the user's emotions based on the recognized emotions.

[0349] The "server for analysis" is a computer system that receives the transmitted blood test results and emotion data and performs analysis.

[0350] This invention is a system that suggests optimal meals for nutrient replenishment based on the user's blood test results and emotional state. This system is mainly composed of a terminal such as a smartphone and a server that performs analysis. Detailed embodiments of this invention are described below.

[0351] First, the user uses the device to enter the blood test results. There are two input methods: manual input and file upload. With manual input, the user enters the numerical values ​​of each nutrient into the input form on the device, and with file upload, the user selects and uploads a CSV or Excel file containing the blood test results.

[0352] The device is also equipped with a camera and microphone, and the emotion recognition engine uses these devices to recognize emotions from the user's facial expressions and voice. Facial recognition technology is used to determine emotions such as smile, anger, and sadness, and voice analysis is used to analyze the user's intentions and emotions. Specific technologies used here include OpenCV and machine learning models.

[0353] The device sends the entered blood test data and recognized emotion data to a server. The server analyzes the received blood test data and connects to a database to obtain normal nutrient range information. It then compares the user's blood test results with the normal range to identify nutrient deficiencies. It then creates a list of nutrients that are deficient (e.g., iron, vitamin D).

[0354] The emotion recognition engine customizes the suggested dishes according to the user's emotional state. For example, if the system detects that the user is under stress, it will suggest ingredients and dishes that have a relaxing effect. The server creates a list of suitable dishes and compiles information about the ingredients, cooking steps, and nutritional value of each dish. This information includes the name of the dish, a list of ingredients, cooking steps, and nutritional value.

[0355] Finally, the server sends the generated cooking suggestions to the device, and the device displays the received suggestions to the user. For example, it might display on the screen, "You are low in iron and appear to be under stress. I recommend making a stir-fry of spinach and lean meat and a cup of mint tea for relaxation."

[0356] This allows users to accurately identify their nutritional deficiencies, easily select and execute specific meals to supplement them, and even receive advice based on their emotional state. Furthermore, users can also order the suggested meals directly from a food delivery application.

[0357] As a concrete example, suppose a user enters their blood test results into an application, smiles or makes other facial expressions in front of the camera, and the blood test results indicate an iron deficiency. The emotion engine recognizes that the user is stressed. Based on this information, the application suggests a spinach and lean meat stir-fry with a relaxing mint tea and offers the option to order the meal from a food delivery service.

[0358] The following sentences are examples of prompt sentences:

[0359] "Enter your blood test results and smile or make other facial expressions for the camera."

[0360] "Vitamin D is 15 ng / mL, calcium is 10 mg / dL, and iron is 30 μg / dL."

[0361] "The application will analyze it and suggest dishes."

[0362] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0363] Step 1:

[0364] Users enter blood test results using a terminal. There are two input methods: manual input, where the numerical values ​​of each nutrient are entered into an input form, and file upload, where a CSV or Excel file is selected and uploaded. This input data is used for subsequent analysis.

[0365] Step 2:

[0366] The terminal receives the blood test data and processes it accordingly, primarily converting the entered nutrient values ​​into a standard format. This processed data is then sent to the server.

[0367] Step 3:

[0368] The device's camera and microphone are used to capture the user's facial expressions and voice. Using facial recognition technology such as OpenCV, the system recognizes the user's emotional state in real time. This emotional recognition data is also sent to the server.

[0369] Step 4:

[0370] The server analyzes the received blood test data and connects to a database to obtain normal nutrient range information. This allows the server to compare the user's blood test results with the normal ranges and identify which nutrients are deficient. A list of deficient nutrients is generated.

[0371] Step 5:

[0372] The server analyzes the emotion recognition data and determines the user's emotional state. This allows the server to understand whether the user is feeling stressed or relaxed. A machine learning model is used to achieve highly accurate emotion determination.

[0373] Step 6:

[0374] The server selects the most appropriate dish based on the user's lacking nutrients and emotional state. For example, if the user is lacking in iron and is under stress, the server will suggest stir-fried spinach and lean meat and a cup of relaxing mint tea. A list of dishes is generated based on the user's lacking nutrients and emotional state.

[0375] Step 7:

[0376] The server then sends the generated recipe list, ingredients, instructions, and nutritional information to the device, where it is formatted for display and transmitted in a format that is easily understood by the user.

[0377] Step 8:

[0378] The device receives recipe suggestions from the server and displays them on the screen, such as a message like, "You're low in iron and appear to be under stress. I recommend making a stir-fry of spinach and lean meat, and a cup of relaxing mint tea."

[0379] Step 9:

[0380] Users can select suggested dishes on their device and order them through a food delivery service, allowing them to consume optimal nutrients based on their health and mood.

[0381] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0382] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0383] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0384] [Second embodiment]

[0385] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0386] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0387] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0388] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0389] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0390] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0391] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0392] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0393] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0394] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0395] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0396] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0397] This invention is a system that identifies nutrient deficiencies based on a user's blood test results and suggests recipes to supplement those nutrients. This system supports the user's input of blood test data, analysis, suggestion generation, and result display.

[0398] 1. Data Entry

[0399] Users use the device to input or upload their blood test results. For manual input, users enter values ​​for iron, vitamin D, calcium, etc. into a form displayed on the device screen. For file upload, users upload an electronic file in CSV or Excel format to the device.

[0400] 2. Data Analysis

[0401] The terminal receives the entered blood test data, performs basic validation (checking the format and checking for abnormal values), and then sends the data to the server, which then begins processing to analyze the received blood test results.

[0402] The server first retrieves normal ranges for nutrient levels stored in a database—for example, "normal range for iron is 50-150 μg / dL" or "normal range for vitamin D is 20-50 ng / mL"—and then compares the user's blood test results with these normal ranges to identify any nutrient deficiencies.

[0403] 3. Proposal generation

[0404] Based on the identified nutrient deficiencies, the server searches its database for ingredients and dishes to fill them. For example, if an individual is iron deficient, it will select dishes containing spinach, lean meat, lentils, etc. The server creates a list of suitable dishes and compiles information about the ingredients, instructions, and nutritional value of each dish.

[0405] 4. Results display

[0406] The server sends the generated recipe suggestions to the device, which then displays the received suggestions on the screen for easy viewing by the user.

[0407] Specific examples

[0408] 1. The user enters the following blood test results into the terminal:

[0409] Iron: 30 μg / dL

[0410] Vitamin D: 15 ng / mL

[0411] Calcium: 10 mg / dL

[0412] 2. The device receives this data and sends it to the server.

[0413] 3. The server performs the analysis and identifies that there is an iron deficiency.

[0414] 4. Servers can choose dishes like spinach and lean meat stir-fry or lentil soup to help them get iron.

[0415] 5. The server sends this information to the terminal and displays it to the user:

[0416] "You're low in iron. Try making a spinach and lean meat stir fry or lentil soup."

[0417] This allows users to accurately understand their nutritional deficiencies and easily select and execute specific meals to supplement them.

[0418] The processing flow will be explained below.

[0419] Step 1: Data entry

[0420] Users enter blood test results on the device or upload them as a file.

[0421] In the case of manual input, the user inputs the numerical value of each nutrient into an input form on the terminal.

[0422] When uploading a file, the user selects and uploads a CSV or Excel file containing the blood test results.

[0423] Step 2: Send data

[0424] The terminal receives the entered blood test data and validates its format and content, for example, checking that the values ​​are entered in the correct format and that they do not contain any abnormal values.

[0425] Data that passes validation is converted into an appropriate format and sent to the server.

[0426] Step 3: Data analysis

[0427] The server connects to a database to obtain normal range information for nutrients in order to analyze the received blood test data.

[0428] The server compares the user's blood test results with the normal range and identifies the deficiency of each nutrient. For example, if the user's iron level is 30 μg / dL and the normal range is 50-150 μg / dL, it will determine that the user is iron deficient.

[0429] Step 4: Identify nutrient deficiencies

[0430] The server creates a list of nutrients (e.g., iron, vitamin D) that are lacking from the analysis results.

[0431] For each identified nutrient deficiency, the server searches its database for data on appropriate ingredients and dishes to compensate for it.

[0432] Step 5: Food suggestions

[0433] The server generates cooking recipes to supplement missing nutrients, including the name of the dish, a list of ingredients, cooking instructions, and nutritional information.

[0434] For example, dishes that can help replenish iron include "stir-fried spinach and lean meat" and "lentil soup."

[0435] Step 6: Send results

[0436] The server sends the generated recipe suggestions to the terminal.

[0437] The suggestions are formatted in a clear and user-friendly way.

[0438] Step 7: View the results

[0439] The device will then display the received recipe suggestions to the user. For example, it might say, "You're low in iron. Try making a stir-fry of spinach and lean meat or lentil soup."

[0440] Based on the displayed information, users can create suggested dishes and supplement any missing nutrients.

[0441] Example 1

[0442] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0443] Nutritional deficiencies can cause a variety of health problems, but it can be difficult to independently identify specific nutritional supplementation strategies based on individual blood test results. Furthermore, conventional methods require a lot of time and effort for manual data entry and analysis. Therefore, there is a need for a system that allows users to easily identify nutrient deficiencies based on their own blood test results and receive specific recipe suggestions based on those deficiencies.

[0444] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0445] In this invention, the server includes: means for a user to input or upload blood test results using a terminal; means for the terminal to validate the input blood test results and check for abnormal values; means for the terminal to send the validated blood test results to the server; means for the server to obtain normal range nutrient levels from a database and compare them with the blood test results to identify deficient nutrients; means for the server to search for ingredients and dishes to supplement the deficient nutrients and generate a list of dishes; and means for sending the recipe suggestions generated by the server to the terminal and displaying them to the user. This allows users to quickly and accurately identify deficient nutrients based on their blood test results and easily receive specific recipe suggestions to supplement them.

[0446] "User" refers to an individual who uses the system to input blood test results and receive information about nutrient deficiencies and recipe suggestions to compensate for them.

[0447] "Terminal" refers to a device used by a user to input or upload blood test results, and is responsible for validating input data and transmitting the data to a server.

[0448] The "server" refers to a computer that analyzes the received blood test results, identifies any missing nutrients, generates recipe suggestions to compensate for those nutrients, and sends them to the terminal.

[0449] "Blood test results" refers to data that indicates the numerical information of nutrients contained in the user's blood.

[0450] "Validation" refers to the process of checking whether the data format of the entered blood test results is correct and whether it contains any invalid or abnormal values.

[0451] "Database" refers to the collection of data that the server accesses to obtain normal range nutrient levels and cooking suggestion information.

[0452] "Normal range" refers to a value that represents a particular nutrient within a standard range of healthy levels.

[0453] "Deficient nutrients" refers to nutrients that are not within the normal range based on the user's blood test results.

[0454] "Cooking suggestions" refers to a list of specific dishes generated by the server to supplement missing nutrients, along with detailed information (ingredients, cooking instructions, nutritional information).

[0455] "Input" refers to the act of a user providing blood test results to the system via a terminal.

[0456] "Upload" refers to the act of a user sending blood test results in electronic file format to a terminal and having them imported into the system.

[0457] "Manual input" refers to the act of a user directly entering blood test result values ​​into a form displayed on the terminal screen.

[0458] "Comparison" refers to the process in which the server compares the user's blood test results with the normal range values ​​stored in the database to identify any nutrients that are lacking.

[0459] This invention relates to a system that identifies nutrient deficiencies based on a user's blood test results and suggests recipes to supplement those nutrients. This system supports the user's blood test data entry, analysis, suggestion generation, and result display.

[0460] Users use the device to input or upload their blood test results. For manual input, users enter values ​​such as iron, vitamin D, and calcium into a form displayed on the device screen. For file upload, users upload electronic files in CSV or Excel format to the device.

[0461] The terminal receives the entered blood test data and, after basic validation (checking the format and abnormal values), sends the data to the server. The server then begins the process of analyzing the received blood test results. The server first obtains the normal range of nutrient levels stored in the database. For example, it obtains values ​​such as "the normal range for iron is 50-150 μg / dL" and "the normal range for vitamin D is 20-50 ng / mL." It then compares the user's blood test results with these normal ranges to identify any nutrient deficiencies.

[0462] Based on the identified nutrient deficiencies, the server searches its database for ingredients and dishes to fill them. For example, if an individual is iron deficient, it will select dishes containing spinach, lean meat, lentils, etc. The server creates a list of suitable dishes and compiles information about the ingredients, instructions, and nutritional value of each dish.

[0463] The server sends the generated recipe suggestions to the device, which then displays the received suggestions on the screen for easy viewing by the user.

[0464] Specific examples

[0465] Specific examples are shown below.

[0466] 1. The user enters the following blood test results into the terminal:

[0467] Iron: 30 μg / dL

[0468] Vitamin D: 15 ng / mL

[0469] Calcium: 10 mg / dL

[0470] 2. The device receives this data and sends it to the server.

[0471] 3. The server performs the analysis and identifies that there is an iron deficiency.

[0472] 4. Servers can choose dishes like spinach and lean meat stir-fry or lentil soup to help them get iron.

[0473] 5. The server sends this information to the terminal and displays it to the user:

[0474] "You're low in iron. Try making a spinach and lean meat stir fry or lentil soup."

[0475] This allows users to accurately understand their nutritional deficiencies and easily select and execute specific meals to supplement them.

[0476] Prompt Sentence Examples

[0477] The following prompts can be used for the generative AI model:

[0478] "Please suggest meals for iron deficiency. User's blood test results are: Iron 30 μg / dL, Vitamin D 15 ng / mL, Calcium 10 mg / dL."

[0479] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0480] Step 1: Data entry

[0481] Users use the device to input or upload blood test results. For manual input, users enter values ​​such as iron, vitamin D, and calcium into a form displayed on the device screen. For file upload, users upload electronic files in CSV or Excel format to the device.

[0482] Specifically, when the user clicks the "Input" button, the data is imported into the system. The input data includes the numerical values ​​of the blood test results and is sent directly to the next step.

[0483] Step 2: Data Validation

[0484] The terminal receives the blood test data entered and performs basic validation. Validation includes checking the format and checking for abnormal values. For example, it checks whether the iron value is a number and whether the vitamin D value is entered in the correct units. Abnormally high or low values ​​are also detected.

[0485] Specifically, the terminal checks the data format and range, and if it detects any abnormal data, it displays an error message to the user. Only normal data will proceed to the next step.

[0486] Step 3: Send data

[0487] If the validation is successful, the terminal sends the data to the server. The data sent is the numerical information of the blood test results that have passed validation.

[0488] Specifically, the device displays a success message to the user and sends the data to the server via an HTTP request. The server receives this request and prepares to process the data.

[0489] Step 4: Database access and analysis

[0490] The server begins analyzing the received blood test results. First, it retrieves normal range nutrient levels from the database. Then it compares the user's blood test results with these normal ranges to identify any nutrient deficiencies. The input is the blood test results and the normal range data retrieved from the database. The output is a list of nutrients that are deficient.

[0491] Specifically, the server uses an SQL query to retrieve data from the database and performs a comparison operation to identify nutrient deficiencies.

[0492] Step 5: Proposal Generation

[0493] Based on the identified nutrient deficiencies, the server searches the database for ingredients and dishes to fill them. For example, if there is an iron deficiency, it will select dishes containing spinach, lean meat, lentils, etc. The server creates a list of appropriate dishes and compiles information about the ingredients, steps, and nutritional value of each dish. The input is a list of nutrient deficiencies, and the output is a list of dish suggestions.

[0494] Specifically, the server uses SQL queries and AI models to generate recipe suggestions, then formats them into a data list.

[0495] Step 6: View the results

[0496] The server sends the generated recipe suggestions to the device, which displays the received suggestions on the screen for easy viewing by the user. The input is a list of recipe suggestions, and the output is specific suggestion information displayed to the user.

[0497] Specifically, the device analyzes the data received from the server and displays it on the screen in an easy-to-read format for the user. The user can then check the screen and select and execute the suggested dishes.

[0498] (Application example 1)

[0499] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0500] In modern society, many people suffer from unbalanced diets and inadequate nutritional intake. It is difficult to accurately identify missing nutrients and select appropriate meals to supplement them. Furthermore, the time and effort required to actually prepare and cook such meals is a significant burden. This creates problems that hinder the maintenance and improvement of health.

[0501] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0502] In this invention, the server includes means for analyzing blood test results when the blood test results are input and identifying nutrient deficiencies, means for suggesting dishes to supplement the nutrient deficiencies based on the nutrient deficiencies, means for displaying the suggested dishes to the user, means for the user to directly order the suggested dishes, and means for delivering the ordered dishes to the user. This allows the user to easily understand their nutritional status, easily select and order appropriate meals to supplement their nutritional status, and further receive cooked meals at home.

[0503] "Blood test results" are data showing the values ​​of various nutrients and components measured using blood.

[0504] "Nutrients" are substances necessary for the growth and maintenance of the human body, and particularly refer to trace elements such as vitamins, minerals, and proteins.

[0505] "Analysis" is the process of breaking down and analyzing input data using calculations and statistics to extract meaningful information.

[0506] "Deficient nutrients" refers to nutrients that are determined to be deficient relative to the normal range reference value.

[0507] "Means for suggesting dishes" refers to methods for displaying and providing meal menus and combinations of ingredients suitable for supplementing specified nutrients.

[0508] A "means for displaying dishes" is a system or device that allows a user to visually view details of suggested dishes.

[0509] "Direct ordering" refers to the ability for a user to receive a dish suggestion and then immediately order the dish through an application or device.

[0510] "Delivery means" refers to the logistics system or delivery service used to deliver the ordered food to the specified location.

[0511] A "server" is a computer system that stores, processes, and communicates data over a network.

[0512] The present invention is a system that identifies nutrient deficiencies based on the user's blood test results, suggests meals to supplement those nutrients, and delivers the meals. An embodiment of the system will be described in detail below.

[0513] 1. Data Entry

[0514] Users input or upload their blood test results using a smartphone or other device. For manual input, users enter the required nutrient values ​​(e.g., iron, vitamin D, calcium, etc.) into a form displayed on the device screen. For file upload, users can upload electronic files in CSV or Excel format to the device.

[0515] 2. Data Analysis

[0516] The server receives the blood test data sent from the device and begins analysis. First, the server performs basic validation (checking the format and abnormal values). Then, it retrieves normal range nutrient levels from a database and compares them with the input blood test data to identify nutrient deficiencies. Here, data analysis tools such as Python and TensorFlow are used.

[0517] 3. Proposal generation

[0518] The server searches the database for appropriate ingredients and dishes based on the identified nutrient deficiencies. For example, if you are iron deficient, it will select dishes containing spinach, lean meat, and lentils. The list of selected dishes includes ingredients, instructions, and nutritional information. Based on this information, it generates recipe suggestions for the user.

[0519] 4. Displaying results and ordering

[0520] The device will display the generated dish suggestions to the user, who can then review the suggested dishes and place an order directly from the device. By tapping the order button, the corresponding order information will be sent via the food delivery partner's API.

[0521] 5. Food delivery

[0522] The food delivery partner will prepare the ordered food and deliver it to the user's designated location. Users can check the delivery status in real time through the application.

[0523] Specific examples

[0524] For example, suppose a user enters blood test results showing iron levels of 30 μg / dL, vitamin D levels of 15 ng / mL, and calcium levels of 10 mg / dL. The system analyzes this and determines that the user is iron deficient. The system then suggests dishes to supplement the iron levels, such as spinach and lean meat stir-fry and lentil soup. The user can view these dishes on their smartphone screen and order the dishes displayed with the message, "You are low in iron. Try spinach and lean meat stir-fry or lentil soup."

[0525] Prompt Sentence Examples

[0526] "I have entered my blood test results, which show iron at 30 μg / dL, vitamin D at 15 ng / mL, and calcium at 10 mg / dL. Please suggest appropriate dishes based on the nutrients I am lacking."

[0527] This system allows users to easily understand their nutritional status, select the appropriate meals to supplement it, and order and receive them without any hassle.

[0528] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0529] Step 1: Data entry

[0530] Users can input or upload their blood test results using a smartphone or other device. For manual input, users enter values ​​such as iron, vitamin D, and calcium into the application form. For file upload, users can upload CSV or Excel format files to their device.

[0531] Input: Blood test results entered or uploaded by the user

[0532] Output: Blood test result data (manually entered values ​​or uploaded file contents)

[0533] Specific processing behavior:

[0534] The user opens the app on their smartphone and accesses the blood test result input screen.

[0535] Enter the required values ​​or select a file, then press the send button.

[0536] Step 2: Data validation and submission

[0537] The terminal receives the entered blood test data, performs basic validation, and then sends the validated data to the server.

[0538] Input: Blood test result data

[0539] Output: Validated blood test data sent to the server

[0540] Specific processing behavior:

[0541] The terminal checks the format of the input data and checks for invalid or missing values.

[0542] Data that has been successfully validated is sent to the server via an HTTP request.

[0543] Step 3: Data analysis and identification of nutrient deficiencies

[0544] The server analyzes the received blood test data and identifies any deficiencies in nutrients by comparing it with a database of nutrients in the normal range.

[0545] Input: Blood test data sent to the server

[0546] Output: List of nutrients that are lacking

[0547] Specific processing behavior:

[0548] The server retrieves the normal range for each nutrient from the database.

[0549] Compare received blood test results with normal ranges to identify nutrient deficiencies.

[0550] List any identified nutrient deficiencies.

[0551] Step 4: Generate dish suggestions

[0552] Based on the identified nutrient deficiencies, the server searches the database for appropriate ingredients and dishes to compensate for them and generates a list of dishes.

[0553] Input: List of nutrients you are deficient in

[0554] Output: A list of suggested dishes, including ingredients, instructions, and nutritional information

[0555] Specific processing behavior:

[0556] Search the database for ingredients and dishes that address missing nutrients.

[0557] Get detailed recipe information (ingredients, instructions, nutritional information).

[0558] Generate a list of suggested dishes.

[0559] Step 5: View results and place orders

[0560] The device displays the generated food suggestions to the user, who has the option to review the suggested dishes and order directly. The user's order information is sent via the food delivery partner's API.

[0561] Input: A list of dishes to suggest

[0562] Output: Detailed information about the food selected and ordered by the user

[0563] Specific processing behavior:

[0564] The terminal displays a list of suggested dishes to the user.

[0565] The user selects a dish and presses the order button.

[0566] Order information is sent to the food delivery partner's API.

[0567] Step 6: Food preparation and delivery

[0568] The food delivery partner prepares the ordered food and delivers it to the user's designated location. The user can check the delivery status in real time through the application.

[0569] Input: Details of the food ordered by the user

[0570] Output: The meal delivered to the user

[0571] Specific processing behavior:

[0572] The food delivery partner receives the order information and prepares the corresponding food.

[0573] The cooked food is delivered to the user's specified address.

[0574] The user can check the delivery status on the application and receive a notification that delivery has been completed.

[0575] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0576] This invention combines a system that identifies nutrient deficiencies based on the user's blood test results and suggests recipes to supplement those deficiencies with an emotion engine that recognizes the user's emotions. This system makes suggestions based on the user's emotional state, thereby realizing more personalized nutritional supplementation suggestions.

[0577] Data Entry and Emotion Recognition

[0578] Users use a device to input their blood test results or upload them as a file. There are two input methods: manual input and file upload. With manual input, users input the numerical values ​​of each nutrient into the input form on the device. With file upload, users select and upload a CSV or Excel file containing the blood test results.

[0579] Furthermore, the device is equipped with a camera and microphone, and the emotion engine uses these devices to recognize emotions from the user's facial expressions and voice. Facial recognition technology is used to determine emotions such as smile, anger, and sadness, and voice analysis is used to analyze the user's intentions and emotions.

[0580] Data transmission and analysis

[0581] The device sends the input blood test data and recognized emotion data to the server. The server analyzes the received blood test data and connects to a database to obtain normal nutrient range information. It then compares the user's blood test results with the normal range and identifies nutrient deficiencies.

[0582] Identifying nutrient deficiencies and suggesting recipes

[0583] The server uses the analysis results to create a list of nutrients (e.g., iron, vitamin D) that are lacking. Furthermore, the emotion engine customizes the suggested dishes based on the user's emotional state. For example, if the server detects that the user is under stress, it will suggest ingredients and dishes that have a relaxing effect.

[0584] The server creates a list of suitable dishes and compiles the ingredients, instructions, and nutritional information for each dish, including the dish name, ingredient list, cooking instructions, and nutritional information.

[0585] Results display

[0586] The server sends the generated cooking suggestions to the device, which then displays the received suggestions to the user. For example, it might say, "You're low in iron, and you seem to be under stress. I recommend making a stir-fry of spinach and lean meat, and some relaxing mint tea."

[0587] Specific examples

[0588] 1. The user enters the following blood test results into the terminal:

[0589] Iron: 30 μg / dL

[0590] Vitamin D: 15 ng / mL

[0591] Calcium: 10 mg / dL

[0592] 2. The device receives this data and sends it to the server.

[0593] 3. The server performs the analysis and identifies that there is an iron deficiency.

[0594] 4. The emotion engine recognizes the user's stress level from their facial expressions and voice.

[0595] 5. The server recommends a spinach and lean meat stir fry as a way to supplement iron intake, and also suggests adding mint tea to reduce stress.

[0596] 6. The server sends this information to the terminal and displays it to the user:

[0597] "You're iron deficient. You also seem to be under stress. I'd recommend making a spinach and lean meat stir fry and a relaxing mint tea."

[0598] This allows users to accurately identify their nutritional deficiencies, easily select and execute specific meals to supplement them, and even receive advice based on their emotional state.

[0599] The processing flow will be explained below.

[0600] Step 1: Data entry

[0601] Users enter blood test results on the device or upload them as a file.

[0602] In the case of manual input, the user inputs the numerical value of each nutrient (e.g., iron, vitamin D, calcium, etc.) into an input form on the terminal.

[0603] When uploading a file, the user selects and uploads a CSV or Excel file containing the blood test results.

[0604] Step 2: Recognize emotions

[0605] Using the device's built-in camera and microphone, the emotion engine analyzes the user's facial expressions and voice.

[0606] In facial expression analysis, the emotion engine identifies emotions from the user's facial micro-expressions (e.g., smile, sadness, anger).

[0607] In voice analysis, the emotion engine determines emotions from the user's voice patterns and tone.

[0608] Step 3: Send data

[0609] The terminal transmits the input blood test data and the recognized emotion data to the server.

[0610] The transmitted information includes the values ​​of each nutrient in the blood test and the determined emotion (e.g., stressed, relaxed).

[0611] Step 4: Data analysis

[0612] The server analyzes the received blood test data and connects to a database to obtain normal range information for nutrients.

[0613] The server compares the user's blood test results with normal ranges and identifies deficiencies in each nutrient.

[0614] Example: "The user's iron level is 30 μg / dL, which is insufficient compared to the normal range of 50-150 μg / dL."

[0615] Step 5: Identify nutrient deficiencies and choose meals

[0616] The server searches a database for appropriate ingredients and recipes based on the identified nutrient deficiencies.

[0617] The system customizes the best meal for the user based on the identified emotional state (e.g., stress state).

[0618] For example: "Choose a spinach and lean meat stir fry for iron and add mint tea to reduce stress."

[0619] Step 6: Generate dish suggestions

[0620] The server generates a recommendation containing details of the selected dish (dish name, ingredient list, cooking instructions, nutritional information).

[0621] Examples: "Spinach and lean meat stir fry" and "Relaxing mint tea."

[0622] Step 7: Send results

[0623] The server sends the generated recipe suggestions to the terminal.

[0624] The proposed information is formatted in a way that is easy for the user to review.

[0625] Step 8: View the results

[0626] The terminal displays the received recipe suggestions to the user.

[0627] For example: "You're low in iron and seem to be under stress. I'd recommend making a spinach and lean meat stir fry and a relaxing cup of mint tea."

[0628] Step 9: Execution

[0629] The user prepares the necessary ingredients and creates the dish based on the suggested recipe.

[0630] This allows users to supplement missing nutrients and eat meals that take into consideration their emotional state.

[0631] Example 2

[0632] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0633] While conventional systems can identify nutritional deficiencies based on blood test results and suggest meals to replenish these nutrients, they are unable to provide personalized recommendations that take into account the user's emotional state. As a result, they lack the information needed for users to truly achieve optimal nutritional intake.

[0634] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0635] In this invention, the server includes means for analyzing blood test results when the blood test results are input and identifying deficient nutrients, means for suggesting dishes to supplement the deficient nutrients based on the deficient nutrients, and means for recognizing the user's emotional state and making suggestions according to the emotional state. This makes it possible to not only suggest dishes to appropriately supplement the user's deficient nutrients, but also to make personalized suggestions according to the user's emotional state.

[0636] "Blood test results" are numerical information on various nutrients obtained by analyzing the user's blood.

[0637] "Nutrients" are chemicals or compounds that the body needs to maintain normal function.

[0638] A "deficient nutrient" is a nutrient that is identified as being below a standard value based on blood test results.

[0639] The "means of analysis" refers to devices and algorithms that analyze blood test results and evaluate the values ​​of each nutrient.

[0640] "Cooking" refers to food made to supplement the nutrients that the user is lacking, and specific ingredients and cooking methods are suggested.

[0641] The "user's emotional state" refers to the user's psychological or emotional state as recognized through facial expressions, voice analysis, etc.

[0642] An "emotion engine" is a software or hardware system for recognizing and analyzing a user's emotional state.

[0643] A "displaying means" is a device such as a display or screen that visually presents suggested information or messages to a user.

[0644] The "means of suggestion" refers to an algorithm or system that selects appropriate dishes and nutritional supplementation methods based on the analysis results and notifies the user of them.

[0645] The "server" is a computer system that processes blood test results and emotion data and generates proposals based on the analysis results.

[0646] The present invention provides a system for providing personalized nutritional support based on a user's blood test results and emotional state. The system mainly includes a user, a terminal, and a server.

[0647] First, users enter their blood test results using the device. There are two ways to enter blood test results: manual entry or file upload. For manual entry, users enter the numerical values ​​of each nutrient directly into the input form on the device. For file upload, users can upload files in CSV or Excel format.

[0648] The device is equipped with a camera and microphone, which are used to recognize the user's emotional state. The emotion engine uses facial recognition and voice analysis technologies to determine emotions such as smile, anger, and sadness, making it possible to grasp the user's emotional state in real time.

[0649] The device sends the input blood test data and recognized emotion data to the server. The server analyzes the received blood test data and connects to a database to obtain normal nutrient range information. It then compares the user's blood test results with the normal range and identifies any nutrient deficiencies.

[0650] The server then suggests dishes based on the nutritional deficiencies of the user. These suggestions are customized based on the user's emotional state as recognized by the emotion engine. For example, if the user is under stress, the server will suggest ingredients and dishes that have a relaxing effect.

[0651] To give a concrete example, suppose a user enters the following blood test results into a terminal:

[0652] Iron: 30 μg / dL

[0653] Vitamin D: 15 ng / mL

[0654] Calcium: 10 mg / dL

[0655] The device sends this data to the server, which analyzes it and identifies the iron deficiency. The emotion engine recognizes the user's stress level from their facial expressions and voice.

[0656] The server selects a dish of stir-fried spinach and lean meat to supplement iron, and suggests adding mint tea to reduce stress. The server sends this information to the terminal, which displays the following to the user:

[0657] "You're iron deficient. You also seem to be under stress. I'd recommend making a spinach and lean meat stir fry and a relaxing mint tea."

[0658] Here's an example prompt for a generative AI model:

[0659] Based on the user's blood test results and emotional data, we want to identify the nutrients that are lacking and suggest dishes that correspond to the user's emotional state. For example, the user is lacking iron and vitamin D, and is under stress. Please take this into consideration when suggesting appropriate dishes.

[0660] In this way, the present invention can suggest personalized recipes for nutritional supplementation based on the user's blood test results and emotional data, allowing the user to obtain specific methods for efficiently supplementing their nutritional deficiencies and receive appropriate advice based on their emotional state.

[0661] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0662] Step 1:

[0663] The user enters the blood test results.

[0664] Input: Blood test results (e.g., iron, vitamin D, calcium levels)

[0665] How it works: The user manually enters each value into the input form on the device, or selects and uploads a CSV / Excel file.

[0666] Output: The entered blood test result data is saved on the terminal.

[0667] Step 2:

[0668] The device uses a camera and microphone to recognize the user's emotional state.

[0669] Input: Camera video, microphone audio

[0670] How it works: The device's camera captures the user's facial expressions, and the microphone records the user's voice. These data are then sent to the emotion engine.

[0671] Output: User's emotional state (e.g., stress, sadness, joy, etc.) via the emotion engine

[0672] Step 3:

[0673] The terminal transmits the input blood test data and emotion data to the server.

[0674] Input: Blood test result data, emotional state data

[0675] How it works: The device encrypts this data and sends it to the server.

[0676] Output: The server receives the blood test data and emotion data.

[0677] Step 4:

[0678] The server analyzes the received blood test data.

[0679] Input: Blood test data

[0680] How it works: The server connects to the database and retrieves normal range information for each nutrient. This is then compared with the user's blood test results to identify any nutrient deficiencies.

[0681] Output: List of nutrients that are lacking

[0682] Step 5:

[0683] The server customizes food suggestions based on data from the emotion engine.

[0684] Input: List of nutrient deficiencies, emotional state data

[0685] How it works: The server applies an algorithm that suggests dishes based on the user's emotional state. For example, if stress is detected, it will suggest dishes that have a relaxing effect.

[0686] Output: A customized list of food suggestions (e.g., spinach stir-fry with lean meat, mint tea, etc.)

[0687] Step 6:

[0688] The server generates a list of suitable dishes and sends it to the device.

[0689] Input: Customized food suggestion list

[0690] Operation: The server converts the recipe list into a data format and sends it to the terminal.

[0691] Output: The device receives the list of recipe suggestions.

[0692] Step 7:

[0693] The terminal displays the cooking suggestions received from the server to the user.

[0694] Input: list of dish suggestions

[0695] How it works: The device decodes the suggestion list and visually displays it to the user.

[0696] Output: The user can visually see the suggested recipes (e.g., "You're low in iron and appear to be under stress. I suggest you make a spinach and lean meat stir fry and a relaxing mint tea.")

[0697] (Application example 2)

[0698] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0699] Conventional nutritional supplement suggestion systems identify nutrient deficiencies based on a user's blood test results and suggest recipes to supplement them. However, a user's emotional state can affect nutrient absorption and effectiveness, and suggestions that do not take emotional state into account may not achieve optimal results. Furthermore, entering blood test results can be cumbersome, and suggested dishes may not be appropriate for the user's emotional state. Furthermore, in the field of food delivery, there is a lack of recipe suggestions based on health status, which means that the system is unable to meet the diverse needs of users.

[0700] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for analyzing blood test results to identify nutrient deficiencies, means for suggesting dishes to supplement the identified nutrient deficiencies, and means for recognizing the user's emotions and customizing the dishes based on the user's emotional state. This makes it possible to suggest more personalized dishes that provide optimal nutrition based on the blood test results and the user's emotional state. Furthermore, by displaying the suggested dishes to the user and allowing them to directly order them through a food delivery service, a system is realized that reduces the user's effort and provides dishes that are suitable for their health and emotional state.

[0701] "Blood test results" is information that indicates the numerical values ​​of each nutrient analyzed based on the user's blood data.

[0702] "Means for identifying" refers to methods or devices that analyze blood test results to identify nutrients that are lacking.

[0703] The "means for suggesting recipes" refers to a method or device that provides the user with the optimum recipe to supplement the nutrients that are lacking.

[0704] The "display means" refers to a method or device that visually shows the suggested dishes and related information to the user.

[0705] "Means for recognizing emotions" refers to a method or device for determining the emotional state of a user from their facial expressions and voice.

[0706] The "means for customizing dishes according to emotional state" refers to a method or device for selecting and suggesting dishes that best suit the user's emotions based on the recognized emotions.

[0707] The "server for analysis" is a computer system that receives the transmitted blood test results and emotion data and performs analysis.

[0708] This invention is a system that suggests optimal meals for nutrient replenishment based on the user's blood test results and emotional state. This system is mainly composed of a terminal such as a smartphone and a server that performs analysis. Detailed embodiments of this invention are described below.

[0709] First, the user uses the device to enter the blood test results. There are two input methods: manual input and file upload. With manual input, the user enters the numerical values ​​of each nutrient into the input form on the device, and with file upload, the user selects and uploads a CSV or Excel file containing the blood test results.

[0710] The device is also equipped with a camera and microphone, and the emotion recognition engine uses these devices to recognize emotions from the user's facial expressions and voice. Facial recognition technology is used to determine emotions such as smile, anger, and sadness, and voice analysis is used to analyze the user's intentions and emotions. Specific technologies used here include OpenCV and machine learning models.

[0711] The device sends the entered blood test data and recognized emotion data to a server. The server analyzes the received blood test data and connects to a database to obtain normal nutrient range information. It then compares the user's blood test results with the normal range to identify nutrient deficiencies. It then creates a list of nutrients that are deficient (e.g., iron, vitamin D).

[0712] The emotion recognition engine customizes the suggested dishes according to the user's emotional state. For example, if the system detects that the user is under stress, it will suggest ingredients and dishes that have a relaxing effect. The server creates a list of suitable dishes and compiles information about the ingredients, cooking steps, and nutritional value of each dish. This information includes the name of the dish, a list of ingredients, cooking steps, and nutritional value.

[0713] Finally, the server sends the generated cooking suggestions to the device, and the device displays the received suggestions to the user. For example, it might display on the screen, "You are low in iron and appear to be under stress. I recommend making a stir-fry of spinach and lean meat and a cup of mint tea for relaxation."

[0714] This allows users to accurately identify their nutritional deficiencies, easily select and execute specific meals to supplement them, and even receive advice based on their emotional state. Furthermore, users can also order the suggested meals directly from a food delivery application.

[0715] As a concrete example, suppose a user enters their blood test results into an application, smiles or makes other facial expressions in front of the camera, and the blood test results indicate an iron deficiency. The emotion engine recognizes that the user is stressed. Based on this information, the application suggests a spinach and lean meat stir-fry with a relaxing mint tea and offers the option to order the meal from a food delivery service.

[0716] The following sentences are examples of prompt sentences:

[0717] "Enter your blood test results and smile or make other facial expressions for the camera."

[0718] "Vitamin D is 15 ng / mL, calcium is 10 mg / dL, and iron is 30 μg / dL."

[0719] "The application will analyze it and suggest dishes."

[0720] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0721] Step 1:

[0722] Users enter blood test results using a terminal. There are two input methods: manual input, where the numerical values ​​of each nutrient are entered into an input form, and file upload, where a CSV or Excel file is selected and uploaded. This input data is used for subsequent analysis.

[0723] Step 2:

[0724] The terminal receives the blood test data and processes it accordingly, primarily converting the entered nutrient values ​​into a standard format. This processed data is then sent to the server.

[0725] Step 3:

[0726] The device's camera and microphone are used to capture the user's facial expressions and voice. Using facial recognition technology such as OpenCV, the system recognizes the user's emotional state in real time. This emotional recognition data is also sent to the server.

[0727] Step 4:

[0728] The server analyzes the received blood test data and connects to a database to obtain normal nutrient range information. This allows the server to compare the user's blood test results with the normal ranges and identify which nutrients are deficient. A list of deficient nutrients is generated.

[0729] Step 5:

[0730] The server analyzes the emotion recognition data and determines the user's emotional state. This allows the server to understand whether the user is feeling stressed or relaxed. A machine learning model is used to achieve highly accurate emotion determination.

[0731] Step 6:

[0732] The server selects the most appropriate dish based on the user's lacking nutrients and emotional state. For example, if the user is lacking in iron and is under stress, the server will suggest stir-fried spinach and lean meat and a cup of relaxing mint tea. A list of dishes is generated based on the user's lacking nutrients and emotional state.

[0733] Step 7:

[0734] The server then sends the generated recipe list, ingredients, instructions, and nutritional information to the device, where it is formatted for display and transmitted in a format that is easily understood by the user.

[0735] Step 8:

[0736] The device receives recipe suggestions from the server and displays them on the screen, such as a message like, "You're low in iron and appear to be under stress. I recommend making a stir-fry of spinach and lean meat, and a cup of relaxing mint tea."

[0737] Step 9:

[0738] Users can select suggested dishes on their device and order them through a food delivery service, allowing them to consume optimal nutrients based on their health and mood.

[0739] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0740] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0741] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0742] [Third embodiment]

[0743] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0744] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0745] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0746] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0747] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0748] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0749] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0750] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0751] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0752] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0753] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0754] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0755] This invention is a system that identifies nutrient deficiencies based on a user's blood test results and suggests recipes to supplement those nutrients. This system supports the user's input of blood test data, analysis, suggestion generation, and result display.

[0756] 1. Data Entry

[0757] Users use the device to input or upload their blood test results. For manual input, users enter values ​​for iron, vitamin D, calcium, etc. into a form displayed on the device screen. For file upload, users upload an electronic file in CSV or Excel format to the device.

[0758] 2. Data Analysis

[0759] The terminal receives the entered blood test data, performs basic validation (checking the format and checking for abnormal values), and then sends the data to the server, which then begins processing to analyze the received blood test results.

[0760] The server first retrieves normal ranges for nutrient levels stored in a database—for example, "normal range for iron is 50-150 μg / dL" or "normal range for vitamin D is 20-50 ng / mL"—and then compares the user's blood test results with these normal ranges to identify any nutrient deficiencies.

[0761] 3. Proposal generation

[0762] Based on the identified nutrient deficiencies, the server searches its database for ingredients and dishes to fill them. For example, if an individual is iron deficient, it will select dishes containing spinach, lean meat, lentils, etc. The server creates a list of suitable dishes and compiles information about the ingredients, instructions, and nutritional value of each dish.

[0763] 4. Results display

[0764] The server sends the generated recipe suggestions to the device, which then displays the received suggestions on the screen for easy viewing by the user.

[0765] Specific examples

[0766] 1. The user enters the following blood test results into the terminal:

[0767] Iron: 30 μg / dL

[0768] Vitamin D: 15 ng / mL

[0769] Calcium: 10 mg / dL

[0770] 2. The device receives this data and sends it to the server.

[0771] 3. The server performs the analysis and identifies that there is an iron deficiency.

[0772] 4. Servers can choose dishes like spinach and lean meat stir-fry or lentil soup to help them get iron.

[0773] 5. The server sends this information to the terminal and displays it to the user:

[0774] "You're low in iron. Try making a spinach and lean meat stir fry or lentil soup."

[0775] This allows users to accurately understand their nutritional deficiencies and easily select and execute specific meals to supplement them.

[0776] The processing flow will be explained below.

[0777] Step 1: Data entry

[0778] Users enter blood test results on the device or upload them as a file.

[0779] In the case of manual input, the user inputs the numerical value of each nutrient into an input form on the terminal.

[0780] When uploading a file, the user selects and uploads a CSV or Excel file containing the blood test results.

[0781] Step 2: Send data

[0782] The terminal receives the entered blood test data and validates its format and content, for example, checking that the values ​​are entered in the correct format and that they do not contain any abnormal values.

[0783] Data that passes validation is converted into an appropriate format and sent to the server.

[0784] Step 3: Data analysis

[0785] The server connects to a database to obtain normal range information for nutrients in order to analyze the received blood test data.

[0786] The server compares the user's blood test results with the normal range and identifies the deficiency of each nutrient. For example, if the user's iron level is 30 μg / dL and the normal range is 50-150 μg / dL, it will determine that the user is iron deficient.

[0787] Step 4: Identify nutrient deficiencies

[0788] The server creates a list of nutrients (e.g., iron, vitamin D) that are lacking from the analysis results.

[0789] For each identified nutrient deficiency, the server searches its database for data on appropriate ingredients and dishes to compensate for it.

[0790] Step 5: Food suggestions

[0791] The server generates cooking recipes to supplement missing nutrients, including the name of the dish, a list of ingredients, cooking instructions, and nutritional information.

[0792] For example, dishes that can help replenish iron include "stir-fried spinach and lean meat" and "lentil soup."

[0793] Step 6: Send results

[0794] The server sends the generated recipe suggestions to the terminal.

[0795] The suggestions are formatted in a clear and user-friendly way.

[0796] Step 7: View the results

[0797] The device will then display the received recipe suggestions to the user. For example, it might say, "You're low in iron. Try making a stir-fry of spinach and lean meat or lentil soup."

[0798] Based on the displayed information, users can create suggested dishes and supplement any missing nutrients.

[0799] Example 1

[0800] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0801] Nutritional deficiencies can cause a variety of health problems, but it can be difficult to independently identify specific nutritional supplementation strategies based on individual blood test results. Furthermore, conventional methods require a lot of time and effort for manual data entry and analysis. Therefore, there is a need for a system that allows users to easily identify nutrient deficiencies based on their own blood test results and receive specific recipe suggestions based on those deficiencies.

[0802] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0803] In this invention, the server includes: means for a user to input or upload blood test results using a terminal; means for the terminal to validate the input blood test results and check for abnormal values; means for the terminal to send the validated blood test results to the server; means for the server to obtain normal range nutrient levels from a database and compare them with the blood test results to identify deficient nutrients; means for the server to search for ingredients and dishes to supplement the deficient nutrients and generate a list of dishes; and means for sending the recipe suggestions generated by the server to the terminal and displaying them to the user. This allows users to quickly and accurately identify deficient nutrients based on their blood test results and easily receive specific recipe suggestions to supplement them.

[0804] "User" refers to an individual who uses the system to input blood test results and receive information about nutrient deficiencies and recipe suggestions to compensate for them.

[0805] "Terminal" refers to a device used by a user to input or upload blood test results, and is responsible for validating input data and transmitting the data to a server.

[0806] The "server" refers to a computer that analyzes the received blood test results, identifies any missing nutrients, generates recipe suggestions to compensate for those nutrients, and sends them to the terminal.

[0807] "Blood test results" refers to data that indicates the numerical information of nutrients contained in the user's blood.

[0808] "Validation" refers to the process of checking whether the data format of the entered blood test results is correct and whether it contains any invalid or abnormal values.

[0809] "Database" refers to the collection of data that the server accesses to obtain normal range nutrient levels and cooking suggestion information.

[0810] "Normal range" refers to a value that represents a particular nutrient within a standard range of healthy levels.

[0811] "Deficient nutrients" refers to nutrients that are not within the normal range based on the user's blood test results.

[0812] "Cooking suggestions" refers to a list of specific dishes generated by the server to supplement missing nutrients, along with detailed information (ingredients, cooking instructions, nutritional information).

[0813] "Input" refers to the act of a user providing blood test results to the system via a terminal.

[0814] "Upload" refers to the act of a user sending blood test results in electronic file format to a terminal and having them imported into the system.

[0815] "Manual input" refers to the act of a user directly entering blood test result values ​​into a form displayed on the terminal screen.

[0816] "Comparison" refers to the process in which the server compares the user's blood test results with the normal range values ​​stored in the database to identify any nutrients that are lacking.

[0817] This invention relates to a system that identifies nutrient deficiencies based on a user's blood test results and suggests recipes to supplement those nutrients. This system supports the user's blood test data entry, analysis, suggestion generation, and result display.

[0818] Users use the device to input or upload their blood test results. For manual input, users enter values ​​such as iron, vitamin D, and calcium into a form displayed on the device screen. For file upload, users upload electronic files in CSV or Excel format to the device.

[0819] The terminal receives the entered blood test data and, after basic validation (checking the format and abnormal values), sends the data to the server. The server then begins the process of analyzing the received blood test results. The server first obtains the normal range of nutrient levels stored in the database. For example, it obtains values ​​such as "the normal range for iron is 50-150 μg / dL" and "the normal range for vitamin D is 20-50 ng / mL." It then compares the user's blood test results with these normal ranges to identify any nutrient deficiencies.

[0820] Based on the identified nutrient deficiencies, the server searches its database for ingredients and dishes to fill them. For example, if an individual is iron deficient, it will select dishes containing spinach, lean meat, lentils, etc. The server creates a list of suitable dishes and compiles information about the ingredients, instructions, and nutritional value of each dish.

[0821] The server sends the generated recipe suggestions to the device, which then displays the received suggestions on the screen for easy viewing by the user.

[0822] Specific examples

[0823] Specific examples are shown below.

[0824] 1. The user enters the following blood test results into the terminal:

[0825] Iron: 30 μg / dL

[0826] Vitamin D: 15 ng / mL

[0827] Calcium: 10 mg / dL

[0828] 2. The device receives this data and sends it to the server.

[0829] 3. The server performs the analysis and identifies that there is an iron deficiency.

[0830] 4. Servers can choose dishes like spinach and lean meat stir-fry or lentil soup to help them get iron.

[0831] 5. The server sends this information to the terminal and displays it to the user:

[0832] "You're low in iron. Try making a spinach and lean meat stir fry or lentil soup."

[0833] This allows users to accurately understand their nutritional deficiencies and easily select and execute specific meals to supplement them.

[0834] Prompt Sentence Examples

[0835] The following prompts can be used for the generative AI model:

[0836] "Please suggest meals for iron deficiency. User's blood test results are: Iron 30 μg / dL, Vitamin D 15 ng / mL, Calcium 10 mg / dL."

[0837] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0838] Step 1: Data entry

[0839] Users use the device to input or upload blood test results. For manual input, users enter values ​​such as iron, vitamin D, and calcium into a form displayed on the device screen. For file upload, users upload electronic files in CSV or Excel format to the device.

[0840] Specifically, when the user clicks the "Input" button, the data is imported into the system. The input data includes the numerical values ​​of the blood test results and is sent directly to the next step.

[0841] Step 2: Data Validation

[0842] The terminal receives the blood test data entered and performs basic validation. Validation includes checking the format and checking for abnormal values. For example, it checks whether the iron value is a number and whether the vitamin D value is entered in the correct units. Abnormally high or low values ​​are also detected.

[0843] Specifically, the terminal checks the data format and range, and if it detects any abnormal data, it displays an error message to the user. Only normal data will proceed to the next step.

[0844] Step 3: Send data

[0845] If the validation is successful, the terminal sends the data to the server. The data sent is the numerical information of the blood test results that have passed validation.

[0846] Specifically, the device displays a success message to the user and sends the data to the server via an HTTP request. The server receives this request and prepares to process the data.

[0847] Step 4: Database access and analysis

[0848] The server begins analyzing the received blood test results. First, it retrieves normal range nutrient levels from the database. Then it compares the user's blood test results with these normal ranges to identify any nutrient deficiencies. The input is the blood test results and the normal range data retrieved from the database. The output is a list of nutrients that are deficient.

[0849] Specifically, the server uses an SQL query to retrieve data from the database and performs a comparison operation to identify nutrient deficiencies.

[0850] Step 5: Proposal Generation

[0851] Based on the identified nutrient deficiencies, the server searches the database for ingredients and dishes to fill them. For example, if there is an iron deficiency, it will select dishes containing spinach, lean meat, lentils, etc. The server creates a list of appropriate dishes and compiles information about the ingredients, steps, and nutritional value of each dish. The input is a list of nutrient deficiencies, and the output is a list of dish suggestions.

[0852] Specifically, the server uses SQL queries and AI models to generate recipe suggestions, then formats them into a data list.

[0853] Step 6: View the results

[0854] The server sends the generated recipe suggestions to the device, which displays the received suggestions on the screen for easy viewing by the user. The input is a list of recipe suggestions, and the output is specific suggestion information displayed to the user.

[0855] Specifically, the device analyzes the data received from the server and displays it on the screen in an easy-to-read format for the user. The user can then check the screen and select and execute the suggested dishes.

[0856] (Application example 1)

[0857] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0858] In modern society, many people suffer from unbalanced diets and inadequate nutritional intake. It is difficult to accurately identify missing nutrients and select appropriate meals to supplement them. Furthermore, the time and effort required to actually prepare and cook such meals is a significant burden. This creates problems that hinder the maintenance and improvement of health.

[0859] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0860] In this invention, the server includes means for analyzing blood test results when the blood test results are input and identifying nutrient deficiencies, means for suggesting dishes to supplement the nutrient deficiencies based on the nutrient deficiencies, means for displaying the suggested dishes to the user, means for the user to directly order the suggested dishes, and means for delivering the ordered dishes to the user. This allows the user to easily understand their nutritional status, easily select and order appropriate meals to supplement their nutritional status, and further receive cooked meals at home.

[0861] "Blood test results" are data showing the values ​​of various nutrients and components measured using blood.

[0862] "Nutrients" are substances necessary for the growth and maintenance of the human body, and particularly refer to trace elements such as vitamins, minerals, and proteins.

[0863] "Analysis" is the process of breaking down and analyzing input data using calculations and statistics to extract meaningful information.

[0864] "Deficient nutrients" refers to nutrients that are determined to be deficient relative to the normal range reference value.

[0865] "Means for suggesting dishes" refers to methods for displaying and providing meal menus and combinations of ingredients suitable for supplementing specified nutrients.

[0866] A "means for displaying dishes" is a system or device that allows a user to visually view details of suggested dishes.

[0867] "Direct ordering" refers to the ability for a user to receive a dish suggestion and then immediately order the dish through an application or device.

[0868] "Delivery means" refers to the logistics system or delivery service used to deliver the ordered food to the specified location.

[0869] A "server" is a computer system that stores, processes, and communicates data over a network.

[0870] The present invention is a system that identifies nutrient deficiencies based on the user's blood test results, suggests meals to supplement those nutrients, and delivers the meals. An embodiment of the system will be described in detail below.

[0871] 1. Data Entry

[0872] Users input or upload their blood test results using a smartphone or other device. For manual input, users enter the required nutrient values ​​(e.g., iron, vitamin D, calcium, etc.) into a form displayed on the device screen. For file upload, users can upload electronic files in CSV or Excel format to the device.

[0873] 2. Data Analysis

[0874] The server receives the blood test data sent from the device and begins analysis. First, the server performs basic validation (checking the format and abnormal values). Then, it retrieves normal range nutrient levels from a database and compares them with the input blood test data to identify nutrient deficiencies. Here, data analysis tools such as Python and TensorFlow are used.

[0875] 3. Proposal generation

[0876] The server searches the database for appropriate ingredients and dishes based on the identified nutrient deficiencies. For example, if you are iron deficient, it will select dishes containing spinach, lean meat, and lentils. The list of selected dishes includes ingredients, instructions, and nutritional information. Based on this information, it generates recipe suggestions for the user.

[0877] 4. Displaying results and ordering

[0878] The device will display the generated dish suggestions to the user, who can then review the suggested dishes and place an order directly from the device. By tapping the order button, the corresponding order information will be sent via the food delivery partner's API.

[0879] 5. Food delivery

[0880] The food delivery partner will prepare the ordered food and deliver it to the user's designated location. Users can check the delivery status in real time through the application.

[0881] Specific examples

[0882] For example, suppose a user enters blood test results showing iron levels of 30 μg / dL, vitamin D levels of 15 ng / mL, and calcium levels of 10 mg / dL. The system analyzes this and determines that the user is iron deficient. The system then suggests dishes to supplement the iron levels, such as spinach and lean meat stir-fry and lentil soup. The user can view these dishes on their smartphone screen and order the dishes displayed with the message, "You are low in iron. Try spinach and lean meat stir-fry or lentil soup."

[0883] Prompt Sentence Examples

[0884] "I have entered my blood test results, which show iron at 30 μg / dL, vitamin D at 15 ng / mL, and calcium at 10 mg / dL. Please suggest appropriate dishes based on the nutrients I am lacking."

[0885] This system allows users to easily understand their nutritional status, select the appropriate meals to supplement it, and order and receive them without any hassle.

[0886] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0887] Step 1: Data entry

[0888] Users can input or upload their blood test results using a smartphone or other device. For manual input, users enter values ​​such as iron, vitamin D, and calcium into the application form. For file upload, users can upload CSV or Excel format files to their device.

[0889] Input: Blood test results entered or uploaded by the user

[0890] Output: Blood test result data (manually entered values ​​or uploaded file contents)

[0891] Specific processing behavior:

[0892] The user opens the app on their smartphone and accesses the blood test result input screen.

[0893] Enter the required values ​​or select a file, then press the send button.

[0894] Step 2: Data validation and submission

[0895] The terminal receives the entered blood test data, performs basic validation, and then sends the validated data to the server.

[0896] Input: Blood test result data

[0897] Output: Validated blood test data sent to the server

[0898] Specific processing behavior:

[0899] The terminal checks the format of the input data and checks for invalid or missing values.

[0900] Data that has been successfully validated is sent to the server via an HTTP request.

[0901] Step 3: Data analysis and identification of nutrient deficiencies

[0902] The server analyzes the received blood test data and identifies any deficiencies in nutrients by comparing it with a database of nutrients in the normal range.

[0903] Input: Blood test data sent to the server

[0904] Output: List of nutrients that are lacking

[0905] Specific processing behavior:

[0906] The server retrieves the normal range for each nutrient from the database.

[0907] Compare received blood test results with normal ranges to identify nutrient deficiencies.

[0908] List any identified nutrient deficiencies.

[0909] Step 4: Generate dish suggestions

[0910] Based on the identified nutrient deficiencies, the server searches the database for appropriate ingredients and dishes to compensate for them and generates a list of dishes.

[0911] Input: List of nutrients you are deficient in

[0912] Output: A list of suggested dishes, including ingredients, instructions, and nutritional information

[0913] Specific processing behavior:

[0914] Search the database for ingredients and dishes that address missing nutrients.

[0915] Get detailed recipe information (ingredients, instructions, nutritional information).

[0916] Generate a list of suggested dishes.

[0917] Step 5: View results and place orders

[0918] The device displays the generated food suggestions to the user, who has the option to review the suggested dishes and order directly. The user's order information is sent via the food delivery partner's API.

[0919] Input: A list of dishes to suggest

[0920] Output: Detailed information about the food selected and ordered by the user

[0921] Specific processing behavior:

[0922] The terminal displays a list of suggested dishes to the user.

[0923] The user selects a dish and presses the order button.

[0924] Order information is sent to the food delivery partner's API.

[0925] Step 6: Food preparation and delivery

[0926] The food delivery partner prepares the ordered food and delivers it to the user's designated location. The user can check the delivery status in real time through the application.

[0927] Input: Details of the food ordered by the user

[0928] Output: The meal delivered to the user

[0929] Specific processing behavior:

[0930] The food delivery partner receives the order information and prepares the corresponding food.

[0931] The cooked food is delivered to the user's specified address.

[0932] The user can check the delivery status on the application and receive a notification that delivery has been completed.

[0933] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0934] This invention combines a system that identifies nutrient deficiencies based on the user's blood test results and suggests recipes to supplement those deficiencies with an emotion engine that recognizes the user's emotions. This system makes suggestions based on the user's emotional state, thereby realizing more personalized nutritional supplementation suggestions.

[0935] Data Entry and Emotion Recognition

[0936] Users use a device to input their blood test results or upload them as a file. There are two input methods: manual input and file upload. With manual input, users input the numerical values ​​of each nutrient into the input form on the device. With file upload, users select and upload a CSV or Excel file containing the blood test results.

[0937] Furthermore, the device is equipped with a camera and microphone, and the emotion engine uses these devices to recognize emotions from the user's facial expressions and voice. Facial recognition technology is used to determine emotions such as smile, anger, and sadness, and voice analysis is used to analyze the user's intentions and emotions.

[0938] Data transmission and analysis

[0939] The device sends the input blood test data and recognized emotion data to the server. The server analyzes the received blood test data and connects to a database to obtain normal nutrient range information. It then compares the user's blood test results with the normal range and identifies nutrient deficiencies.

[0940] Identifying nutrient deficiencies and suggesting recipes

[0941] The server uses the analysis results to create a list of nutrients (e.g., iron, vitamin D) that are lacking. Furthermore, the emotion engine customizes the suggested dishes based on the user's emotional state. For example, if the server detects that the user is under stress, it will suggest ingredients and dishes that have a relaxing effect.

[0942] The server creates a list of suitable dishes and compiles the ingredients, instructions, and nutritional information for each dish, including the dish name, ingredient list, cooking instructions, and nutritional information.

[0943] Results display

[0944] The server sends the generated cooking suggestions to the device, which then displays the received suggestions to the user. For example, it might say, "You're low in iron, and you seem to be under stress. I recommend making a stir-fry of spinach and lean meat, and some relaxing mint tea."

[0945] Specific examples

[0946] 1. The user enters the following blood test results into the terminal:

[0947] Iron: 30 μg / dL

[0948] Vitamin D: 15 ng / mL

[0949] Calcium: 10 mg / dL

[0950] 2. The device receives this data and sends it to the server.

[0951] 3. The server performs the analysis and identifies that there is an iron deficiency.

[0952] 4. The emotion engine recognizes the user's stress level from their facial expressions and voice.

[0953] 5. The server recommends a spinach and lean meat stir fry as a way to supplement iron intake, and also suggests adding mint tea to reduce stress.

[0954] 6. The server sends this information to the terminal and displays it to the user:

[0955] "You're iron deficient. You also seem to be under stress. I'd recommend making a spinach and lean meat stir fry and a relaxing mint tea."

[0956] This allows users to accurately identify their nutritional deficiencies, easily select and execute specific meals to supplement them, and even receive advice based on their emotional state.

[0957] The processing flow will be explained below.

[0958] Step 1: Data entry

[0959] Users enter blood test results on the device or upload them as a file.

[0960] In the case of manual input, the user inputs the numerical value of each nutrient (e.g., iron, vitamin D, calcium, etc.) into an input form on the terminal.

[0961] When uploading a file, the user selects and uploads a CSV or Excel file containing the blood test results.

[0962] Step 2: Recognize emotions

[0963] Using the device's built-in camera and microphone, the emotion engine analyzes the user's facial expressions and voice.

[0964] In facial expression analysis, the emotion engine identifies emotions from the user's facial micro-expressions (e.g., smile, sadness, anger).

[0965] In voice analysis, the emotion engine determines emotions from the user's voice patterns and tone.

[0966] Step 3: Send data

[0967] The terminal transmits the input blood test data and the recognized emotion data to the server.

[0968] The transmitted information includes the values ​​of each nutrient in the blood test and the determined emotion (e.g., stressed, relaxed).

[0969] Step 4: Data analysis

[0970] The server analyzes the received blood test data and connects to a database to obtain normal range information for nutrients.

[0971] The server compares the user's blood test results with normal ranges and identifies deficiencies in each nutrient.

[0972] Example: "The user's iron level is 30 μg / dL, which is insufficient compared to the normal range of 50-150 μg / dL."

[0973] Step 5: Identify nutrient deficiencies and choose meals

[0974] The server searches a database for appropriate ingredients and recipes based on the identified nutrient deficiencies.

[0975] The system customizes the best meal for the user based on the identified emotional state (e.g., stress state).

[0976] For example: "Choose a spinach and lean meat stir fry for iron and add mint tea to reduce stress."

[0977] Step 6: Generate dish suggestions

[0978] The server generates a recommendation containing details of the selected dish (dish name, ingredient list, cooking instructions, nutritional information).

[0979] Examples: "Spinach and lean meat stir fry" and "Relaxing mint tea."

[0980] Step 7: Send results

[0981] The server sends the generated recipe suggestions to the terminal.

[0982] The proposed information is formatted in a way that is easy for the user to review.

[0983] Step 8: View the results

[0984] The terminal displays the received recipe suggestions to the user.

[0985] For example: "You're low in iron and seem to be under stress. I'd recommend making a spinach and lean meat stir fry and a relaxing cup of mint tea."

[0986] Step 9: Execution

[0987] The user prepares the necessary ingredients and creates the dish based on the suggested recipe.

[0988] This allows users to supplement missing nutrients and eat meals that take into consideration their emotional state.

[0989] Example 2

[0990] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0991] While conventional systems can identify nutritional deficiencies based on blood test results and suggest meals to replenish these nutrients, they are unable to provide personalized recommendations that take into account the user's emotional state. As a result, they lack the information needed for users to truly achieve optimal nutritional intake.

[0992] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0993] In this invention, the server includes means for analyzing blood test results when the blood test results are input and identifying deficient nutrients, means for suggesting dishes to supplement the deficient nutrients based on the deficient nutrients, and means for recognizing the user's emotional state and making suggestions according to the emotional state. This makes it possible to not only suggest dishes to appropriately supplement the user's deficient nutrients, but also to make personalized suggestions according to the user's emotional state.

[0994] "Blood test results" are numerical information on various nutrients obtained by analyzing the user's blood.

[0995] "Nutrients" are chemicals or compounds that the body needs to maintain normal function.

[0996] A "deficient nutrient" is a nutrient that is identified as being below a standard value based on blood test results.

[0997] The "means of analysis" refers to devices and algorithms that analyze blood test results and evaluate the values ​​of each nutrient.

[0998] "Cooking" refers to food made to supplement the nutrients that the user is lacking, and specific ingredients and cooking methods are suggested.

[0999] The "user's emotional state" refers to the user's psychological or emotional state as recognized through facial expressions, voice analysis, etc.

[1000] An "emotion engine" is a software or hardware system for recognizing and analyzing a user's emotional state.

[1001] A "displaying means" is a device such as a display or screen that visually presents suggested information or messages to a user.

[1002] The "means of suggestion" refers to an algorithm or system that selects appropriate dishes and nutritional supplementation methods based on the analysis results and notifies the user of them.

[1003] The "server" is a computer system that processes blood test results and emotion data and generates proposals based on the analysis results.

[1004] The present invention provides a system for providing personalized nutritional support based on a user's blood test results and emotional state. The system mainly includes a user, a terminal, and a server.

[1005] First, users enter their blood test results using the device. There are two ways to enter blood test results: manual entry or file upload. For manual entry, users enter the numerical values ​​of each nutrient directly into the input form on the device. For file upload, users can upload files in CSV or Excel format.

[1006] The device is equipped with a camera and microphone, which are used to recognize the user's emotional state. The emotion engine uses facial recognition and voice analysis technologies to determine emotions such as smile, anger, and sadness, making it possible to grasp the user's emotional state in real time.

[1007] The device sends the input blood test data and recognized emotion data to the server. The server analyzes the received blood test data and connects to a database to obtain normal nutrient range information. It then compares the user's blood test results with the normal range and identifies any nutrient deficiencies.

[1008] The server then suggests dishes based on the nutritional deficiencies of the user. These suggestions are customized based on the user's emotional state as recognized by the emotion engine. For example, if the user is under stress, the server will suggest ingredients and dishes that have a relaxing effect.

[1009] To give a concrete example, suppose a user enters the following blood test results into a terminal:

[1010] Iron: 30 μg / dL

[1011] Vitamin D: 15 ng / mL

[1012] Calcium: 10 mg / dL

[1013] The device sends this data to the server, which analyzes it and identifies the iron deficiency. The emotion engine recognizes the user's stress level from their facial expressions and voice.

[1014] The server selects a dish of stir-fried spinach and lean meat to supplement iron, and suggests adding mint tea to reduce stress. The server sends this information to the terminal, which displays the following to the user:

[1015] "You're iron deficient. You also seem to be under stress. I'd recommend making a spinach and lean meat stir fry and a relaxing mint tea."

[1016] Here's an example prompt for a generative AI model:

[1017] Based on the user's blood test results and emotional data, we want to identify the nutrients that are lacking and suggest dishes that correspond to the user's emotional state. For example, the user is lacking iron and vitamin D, and is under stress. Please take this into consideration when suggesting appropriate dishes.

[1018] In this way, the present invention can suggest personalized recipes for nutritional supplementation based on the user's blood test results and emotional data, allowing the user to obtain specific methods for efficiently supplementing their nutritional deficiencies and receive appropriate advice based on their emotional state.

[1019] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1020] Step 1:

[1021] The user enters the blood test results.

[1022] Input: Blood test results (e.g., iron, vitamin D, calcium levels)

[1023] How it works: The user manually enters each value into the input form on the device, or selects and uploads a CSV / Excel file.

[1024] Output: The entered blood test result data is saved on the terminal.

[1025] Step 2:

[1026] The device uses a camera and microphone to recognize the user's emotional state.

[1027] Input: Camera video, microphone audio

[1028] How it works: The device's camera captures the user's facial expressions, and the microphone records the user's voice. These data are then sent to the emotion engine.

[1029] Output: User's emotional state (e.g., stress, sadness, joy, etc.) via the emotion engine

[1030] Step 3:

[1031] The terminal transmits the input blood test data and emotion data to the server.

[1032] Input: Blood test result data, emotional state data

[1033] How it works: The device encrypts this data and sends it to the server.

[1034] Output: The server receives the blood test data and emotion data.

[1035] Step 4:

[1036] The server analyzes the received blood test data.

[1037] Input: Blood test data

[1038] How it works: The server connects to the database and retrieves normal range information for each nutrient. This is then compared with the user's blood test results to identify any nutrient deficiencies.

[1039] Output: List of nutrients that are lacking

[1040] Step 5:

[1041] The server customizes food suggestions based on data from the emotion engine.

[1042] Input: List of nutrient deficiencies, emotional state data

[1043] How it works: The server applies an algorithm that suggests dishes based on the user's emotional state. For example, if stress is detected, it will suggest dishes that have a relaxing effect.

[1044] Output: A customized list of food suggestions (e.g., spinach stir-fry with lean meat, mint tea, etc.)

[1045] Step 6:

[1046] The server generates a list of suitable dishes and sends it to the device.

[1047] Input: Customized food suggestion list

[1048] Operation: The server converts the recipe list into a data format and sends it to the terminal.

[1049] Output: The device receives the list of recipe suggestions.

[1050] Step 7:

[1051] The terminal displays the cooking suggestions received from the server to the user.

[1052] Input: list of dish suggestions

[1053] How it works: The device decodes the suggestion list and visually displays it to the user.

[1054] Output: The user can visually see the suggested recipes (e.g., "You're low in iron and appear to be under stress. I suggest you make a spinach and lean meat stir fry and a relaxing mint tea.")

[1055] (Application example 2)

[1056] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1057] Conventional nutritional supplement suggestion systems identify nutrient deficiencies based on a user's blood test results and suggest recipes to supplement them. However, a user's emotional state can affect nutrient absorption and effectiveness, and suggestions that do not take emotional state into account may not achieve optimal results. Furthermore, entering blood test results can be cumbersome, and suggested dishes may not be appropriate for the user's emotional state. Furthermore, in the field of food delivery, there is a lack of recipe suggestions based on health status, which means that the system is unable to meet the diverse needs of users.

[1058] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for analyzing blood test results to identify nutrient deficiencies, means for suggesting dishes to supplement the identified nutrient deficiencies, and means for recognizing the user's emotions and customizing the dishes based on the user's emotional state. This makes it possible to suggest more personalized dishes that provide optimal nutrition based on the blood test results and the user's emotional state. Furthermore, by displaying the suggested dishes to the user and allowing them to directly order them through a food delivery service, a system is realized that reduces the user's effort and provides dishes that are suitable for their health and emotional state.

[1059] "Blood test results" is information that indicates the numerical values ​​of each nutrient analyzed based on the user's blood data.

[1060] "Means for identifying" refers to methods or devices that analyze blood test results to identify nutrients that are lacking.

[1061] The "means for suggesting recipes" refers to a method or device that provides the user with the optimum recipe to supplement the nutrients that are lacking.

[1062] The "display means" refers to a method or device that visually shows the suggested dishes and related information to the user.

[1063] "Means for recognizing emotions" refers to a method or device for determining the emotional state of a user from their facial expressions and voice.

[1064] The "means for customizing dishes according to emotional state" refers to a method or device for selecting and suggesting dishes that best suit the user's emotions based on the recognized emotions.

[1065] The "server for analysis" is a computer system that receives the transmitted blood test results and emotion data and performs analysis.

[1066] This invention is a system that suggests optimal meals for nutrient replenishment based on the user's blood test results and emotional state. This system is mainly composed of a terminal such as a smartphone and a server that performs analysis. Detailed embodiments of this invention are described below.

[1067] First, the user uses the device to enter the blood test results. There are two input methods: manual input and file upload. With manual input, the user enters the numerical values ​​of each nutrient into the input form on the device, and with file upload, the user selects and uploads a CSV or Excel file containing the blood test results.

[1068] The device is also equipped with a camera and microphone, and the emotion recognition engine uses these devices to recognize emotions from the user's facial expressions and voice. Facial recognition technology is used to determine emotions such as smile, anger, and sadness, and voice analysis is used to analyze the user's intentions and emotions. Specific technologies used here include OpenCV and machine learning models.

[1069] The device sends the entered blood test data and recognized emotion data to a server. The server analyzes the received blood test data and connects to a database to obtain normal nutrient range information. It then compares the user's blood test results with the normal range to identify nutrient deficiencies. It then creates a list of nutrients that are deficient (e.g., iron, vitamin D).

[1070] The emotion recognition engine customizes the suggested dishes according to the user's emotional state. For example, if the system detects that the user is under stress, it will suggest ingredients and dishes that have a relaxing effect. The server creates a list of suitable dishes and compiles information about the ingredients, cooking steps, and nutritional value of each dish. This information includes the name of the dish, a list of ingredients, cooking steps, and nutritional value.

[1071] Finally, the server sends the generated cooking suggestions to the device, and the device displays the received suggestions to the user. For example, it might display on the screen, "You are low in iron and appear to be under stress. I recommend making a stir-fry of spinach and lean meat and a cup of mint tea for relaxation."

[1072] This allows users to accurately identify their nutritional deficiencies, easily select and execute specific meals to supplement them, and even receive advice based on their emotional state. Furthermore, users can also order the suggested meals directly from a food delivery application.

[1073] As a concrete example, suppose a user enters their blood test results into an application, smiles or makes other facial expressions in front of the camera, and the blood test results indicate an iron deficiency. The emotion engine recognizes that the user is stressed. Based on this information, the application suggests a spinach and lean meat stir-fry with a relaxing mint tea and offers the option to order the meal from a food delivery service.

[1074] The following sentences are examples of prompt sentences:

[1075] "Enter your blood test results and smile or make other facial expressions for the camera."

[1076] "Vitamin D is 15 ng / mL, calcium is 10 mg / dL, and iron is 30 μg / dL."

[1077] "The application will analyze it and suggest dishes."

[1078] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1079] Step 1:

[1080] Users enter blood test results using a terminal. There are two input methods: manual input, where the numerical values ​​of each nutrient are entered into an input form, and file upload, where a CSV or Excel file is selected and uploaded. This input data is used for subsequent analysis.

[1081] Step 2:

[1082] The terminal receives the blood test data and processes it accordingly, primarily converting the entered nutrient values ​​into a standard format. This processed data is then sent to the server.

[1083] Step 3:

[1084] The device's camera and microphone are used to capture the user's facial expressions and voice. Using facial recognition technology such as OpenCV, the system recognizes the user's emotional state in real time. This emotional recognition data is also sent to the server.

[1085] Step 4:

[1086] The server analyzes the received blood test data and connects to a database to obtain normal nutrient range information. This allows the server to compare the user's blood test results with the normal ranges and identify which nutrients are deficient. A list of deficient nutrients is generated.

[1087] Step 5:

[1088] The server analyzes the emotion recognition data and determines the user's emotional state. This allows the server to understand whether the user is feeling stressed or relaxed. A machine learning model is used to achieve highly accurate emotion determination.

[1089] Step 6:

[1090] The server selects the most appropriate dish based on the user's lacking nutrients and emotional state. For example, if the user is lacking in iron and is under stress, the server will suggest stir-fried spinach and lean meat and a cup of relaxing mint tea. A list of dishes is generated based on the user's lacking nutrients and emotional state.

[1091] Step 7:

[1092] The server then sends the generated recipe list, ingredients, instructions, and nutritional information to the device, where it is formatted for display and transmitted in a format that is easily understood by the user.

[1093] Step 8:

[1094] The device receives recipe suggestions from the server and displays them on the screen, such as a message like, "You're low in iron and appear to be under stress. I recommend making a stir-fry of spinach and lean meat, and a cup of relaxing mint tea."

[1095] Step 9:

[1096] Users can select suggested dishes on their device and order them through a food delivery service, allowing them to consume optimal nutrients based on their health and mood.

[1097] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1098] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1099] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1100] [Fourth embodiment]

[1101] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1102] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1103] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1104] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1105] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1106] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1107] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1108] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1109] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1110] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1111] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1112] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1113] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1114] This invention is a system that identifies nutrient deficiencies based on a user's blood test results and suggests recipes to supplement those nutrients. This system supports the user's input of blood test data, analysis, suggestion generation, and result display.

[1115] 1. Data Entry

[1116] Users use the device to input or upload their blood test results. For manual input, users enter values ​​for iron, vitamin D, calcium, etc. into a form displayed on the device screen. For file upload, users upload an electronic file in CSV or Excel format to the device.

[1117] 2. Data Analysis

[1118] The terminal receives the entered blood test data, performs basic validation (checking the format and checking for abnormal values), and then sends the data to the server, which then begins processing to analyze the received blood test results.

[1119] The server first retrieves normal ranges for nutrient levels stored in a database—for example, "normal range for iron is 50-150 μg / dL" or "normal range for vitamin D is 20-50 ng / mL"—and then compares the user's blood test results with these normal ranges to identify any nutrient deficiencies.

[1120] 3. Proposal generation

[1121] Based on the identified nutrient deficiencies, the server searches its database for ingredients and dishes to fill them. For example, if an individual is iron deficient, it will select dishes containing spinach, lean meat, lentils, etc. The server creates a list of suitable dishes and compiles information about the ingredients, instructions, and nutritional value of each dish.

[1122] 4. Results display

[1123] The server sends the generated recipe suggestions to the device, which then displays the received suggestions on the screen for easy viewing by the user.

[1124] Specific examples

[1125] 1. The user enters the following blood test results into the terminal:

[1126] Iron: 30 μg / dL

[1127] Vitamin D: 15 ng / mL

[1128] Calcium: 10 mg / dL

[1129] 2. The device receives this data and sends it to the server.

[1130] 3. The server performs the analysis and identifies that there is an iron deficiency.

[1131] 4. Servers can choose dishes like spinach and lean meat stir-fry or lentil soup to help them get iron.

[1132] 5. The server sends this information to the terminal and displays it to the user:

[1133] "You're low in iron. Try making a spinach and lean meat stir fry or lentil soup."

[1134] This allows users to accurately understand their nutritional deficiencies and easily select and execute specific meals to supplement them.

[1135] The processing flow will be explained below.

[1136] Step 1: Data entry

[1137] Users enter blood test results on the device or upload them as a file.

[1138] In the case of manual input, the user inputs the numerical value of each nutrient into an input form on the terminal.

[1139] When uploading a file, the user selects and uploads a CSV or Excel file containing the blood test results.

[1140] Step 2: Send data

[1141] The terminal receives the entered blood test data and validates its format and content, for example, checking that the values ​​are entered in the correct format and that they do not contain any abnormal values.

[1142] Data that passes validation is converted into an appropriate format and sent to the server.

[1143] Step 3: Data analysis

[1144] The server connects to a database to obtain normal range information for nutrients in order to analyze the received blood test data.

[1145] The server compares the user's blood test results with the normal range and identifies the deficiency of each nutrient. For example, if the user's iron level is 30 μg / dL and the normal range is 50-150 μg / dL, it will determine that the user is iron deficient.

[1146] Step 4: Identify nutrient deficiencies

[1147] The server creates a list of nutrients (e.g., iron, vitamin D) that are lacking from the analysis results.

[1148] For each identified nutrient deficiency, the server searches its database for data on appropriate ingredients and dishes to compensate for it.

[1149] Step 5: Food suggestions

[1150] The server generates cooking recipes to supplement missing nutrients, including the name of the dish, a list of ingredients, cooking instructions, and nutritional information.

[1151] For example, dishes that can help replenish iron include "stir-fried spinach and lean meat" and "lentil soup."

[1152] Step 6: Send results

[1153] The server sends the generated recipe suggestions to the terminal.

[1154] The suggestions are formatted in a clear and user-friendly way.

[1155] Step 7: View the results

[1156] The device will then display the received recipe suggestions to the user. For example, it might say, "You're low in iron. Try making a stir-fry of spinach and lean meat or lentil soup."

[1157] Based on the displayed information, users can create suggested dishes and supplement any missing nutrients.

[1158] Example 1

[1159] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1160] Nutritional deficiencies can cause a variety of health problems, but it can be difficult to independently identify specific nutritional supplementation strategies based on individual blood test results. Furthermore, conventional methods require a lot of time and effort for manual data entry and analysis. Therefore, there is a need for a system that allows users to easily identify nutrient deficiencies based on their own blood test results and receive specific recipe suggestions based on those deficiencies.

[1161] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1162] In this invention, the server includes: means for a user to input or upload blood test results using a terminal; means for the terminal to validate the input blood test results and check for abnormal values; means for the terminal to send the validated blood test results to the server; means for the server to obtain normal range nutrient levels from a database and compare them with the blood test results to identify deficient nutrients; means for the server to search for ingredients and dishes to supplement the deficient nutrients and generate a list of dishes; and means for sending the recipe suggestions generated by the server to the terminal and displaying them to the user. This allows users to quickly and accurately identify deficient nutrients based on their blood test results and easily receive specific recipe suggestions to supplement them.

[1163] "User" refers to an individual who uses the system to input blood test results and receive information about nutrient deficiencies and recipe suggestions to compensate for them.

[1164] "Terminal" refers to a device used by a user to input or upload blood test results, and is responsible for validating input data and transmitting the data to a server.

[1165] The "server" refers to a computer that analyzes the received blood test results, identifies any missing nutrients, generates recipe suggestions to compensate for those nutrients, and sends them to the terminal.

[1166] "Blood test results" refers to data that indicates the numerical information of nutrients contained in the user's blood.

[1167] "Validation" refers to the process of checking whether the data format of the entered blood test results is correct and whether it contains any invalid or abnormal values.

[1168] "Database" refers to the collection of data that the server accesses to obtain normal range nutrient levels and cooking suggestion information.

[1169] "Normal range" refers to a value that represents a particular nutrient within a standard range of healthy levels.

[1170] "Deficient nutrients" refers to nutrients that are not within the normal range based on the user's blood test results.

[1171] "Cooking suggestions" refers to a list of specific dishes generated by the server to supplement missing nutrients, along with detailed information (ingredients, cooking instructions, nutritional information).

[1172] "Input" refers to the act of a user providing blood test results to the system via a terminal.

[1173] "Upload" refers to the act of a user sending blood test results in electronic file format to a terminal and having them imported into the system.

[1174] "Manual input" refers to the act of a user directly entering blood test result values ​​into a form displayed on the terminal screen.

[1175] "Comparison" refers to the process in which the server compares the user's blood test results with the normal range values ​​stored in the database to identify any nutrients that are lacking.

[1176] This invention relates to a system that identifies nutrient deficiencies based on a user's blood test results and suggests recipes to supplement those nutrients. This system supports the user's blood test data entry, analysis, suggestion generation, and result display.

[1177] Users use the device to input or upload their blood test results. For manual input, users enter values ​​such as iron, vitamin D, and calcium into a form displayed on the device screen. For file upload, users upload electronic files in CSV or Excel format to the device.

[1178] The terminal receives the entered blood test data and, after basic validation (checking the format and abnormal values), sends the data to the server. The server then begins the process of analyzing the received blood test results. The server first obtains the normal range of nutrient levels stored in the database. For example, it obtains values ​​such as "the normal range for iron is 50-150 μg / dL" and "the normal range for vitamin D is 20-50 ng / mL." It then compares the user's blood test results with these normal ranges to identify any nutrient deficiencies.

[1179] Based on the identified nutrient deficiencies, the server searches its database for ingredients and dishes to fill them. For example, if an individual is iron deficient, it will select dishes containing spinach, lean meat, lentils, etc. The server creates a list of suitable dishes and compiles information about the ingredients, instructions, and nutritional value of each dish.

[1180] The server sends the generated recipe suggestions to the device, which then displays the received suggestions on the screen for easy viewing by the user.

[1181] Specific examples

[1182] Specific examples are shown below.

[1183] 1. The user enters the following blood test results into the terminal:

[1184] Iron: 30 μg / dL

[1185] Vitamin D: 15 ng / mL

[1186] Calcium: 10 mg / dL

[1187] 2. The device receives this data and sends it to the server.

[1188] 3. The server performs the analysis and identifies that there is an iron deficiency.

[1189] 4. Servers can choose dishes like spinach and lean meat stir-fry or lentil soup to help them get iron.

[1190] 5. The server sends this information to the terminal and displays it to the user:

[1191] "You're low in iron. Try making a spinach and lean meat stir fry or lentil soup."

[1192] This allows users to accurately understand their nutritional deficiencies and easily select and execute specific meals to supplement them.

[1193] Prompt Sentence Examples

[1194] The following prompts can be used for the generative AI model:

[1195] "Please suggest meals for iron deficiency. User's blood test results are: Iron 30 μg / dL, Vitamin D 15 ng / mL, Calcium 10 mg / dL."

[1196] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1197] Step 1: Data entry

[1198] Users use the device to input or upload blood test results. For manual input, users enter values ​​such as iron, vitamin D, and calcium into a form displayed on the device screen. For file upload, users upload electronic files in CSV or Excel format to the device.

[1199] Specifically, when the user clicks the "Input" button, the data is imported into the system. The input data includes the numerical values ​​of the blood test results and is sent directly to the next step.

[1200] Step 2: Data Validation

[1201] The terminal receives the blood test data entered and performs basic validation. Validation includes checking the format and checking for abnormal values. For example, it checks whether the iron value is a number and whether the vitamin D value is entered in the correct units. Abnormally high or low values ​​are also detected.

[1202] Specifically, the terminal checks the data format and range, and if it detects any abnormal data, it displays an error message to the user. Only normal data will proceed to the next step.

[1203] Step 3: Send data

[1204] If the validation is successful, the terminal sends the data to the server. The data sent is the numerical information of the blood test results that have passed validation.

[1205] Specifically, the device displays a success message to the user and sends the data to the server via an HTTP request. The server receives this request and prepares to process the data.

[1206] Step 4: Database access and analysis

[1207] The server begins analyzing the received blood test results. First, it retrieves normal range nutrient levels from the database. Then it compares the user's blood test results with these normal ranges to identify any nutrient deficiencies. The input is the blood test results and the normal range data retrieved from the database. The output is a list of nutrients that are deficient.

[1208] Specifically, the server uses an SQL query to retrieve data from the database and performs a comparison operation to identify nutrient deficiencies.

[1209] Step 5: Proposal Generation

[1210] Based on the identified nutrient deficiencies, the server searches the database for ingredients and dishes to fill them. For example, if there is an iron deficiency, it will select dishes containing spinach, lean meat, lentils, etc. The server creates a list of appropriate dishes and compiles information about the ingredients, steps, and nutritional value of each dish. The input is a list of nutrient deficiencies, and the output is a list of dish suggestions.

[1211] Specifically, the server uses SQL queries and AI models to generate recipe suggestions, then formats them into a data list.

[1212] Step 6: View the results

[1213] The server sends the generated recipe suggestions to the device, which displays the received suggestions on the screen for easy viewing by the user. The input is a list of recipe suggestions, and the output is specific suggestion information displayed to the user.

[1214] Specifically, the device analyzes the data received from the server and displays it on the screen in an easy-to-read format for the user. The user can then check the screen and select and execute the suggested dishes.

[1215] (Application example 1)

[1216] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1217] In modern society, many people suffer from unbalanced diets and inadequate nutritional intake. It is difficult to accurately identify missing nutrients and select appropriate meals to supplement them. Furthermore, the time and effort required to actually prepare and cook such meals is a significant burden. This creates problems that hinder the maintenance and improvement of health.

[1218] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1219] In this invention, the server includes means for analyzing blood test results when the blood test results are input and identifying nutrient deficiencies, means for suggesting dishes to supplement the nutrient deficiencies based on the nutrient deficiencies, means for displaying the suggested dishes to the user, means for the user to directly order the suggested dishes, and means for delivering the ordered dishes to the user. This allows the user to easily understand their nutritional status, easily select and order appropriate meals to supplement their nutritional status, and further receive cooked meals at home.

[1220] "Blood test results" are data showing the values ​​of various nutrients and components measured using blood.

[1221] "Nutrients" are substances necessary for the growth and maintenance of the human body, and particularly refer to trace elements such as vitamins, minerals, and proteins.

[1222] "Analysis" is the process of breaking down and analyzing input data using calculations and statistics to extract meaningful information.

[1223] "Deficient nutrients" refers to nutrients that are determined to be deficient relative to the normal range reference value.

[1224] "Means for suggesting dishes" refers to methods for displaying and providing meal menus and combinations of ingredients suitable for supplementing specified nutrients.

[1225] A "means for displaying dishes" is a system or device that allows a user to visually view details of suggested dishes.

[1226] "Direct ordering" refers to the ability for a user to receive a dish suggestion and then immediately order the dish through an application or device.

[1227] "Delivery means" refers to the logistics system or delivery service used to deliver the ordered food to the specified location.

[1228] A "server" is a computer system that stores, processes, and communicates data over a network.

[1229] The present invention is a system that identifies nutrient deficiencies based on the user's blood test results, suggests meals to supplement those nutrients, and delivers the meals. An embodiment of the system will be described in detail below.

[1230] 1. Data Entry

[1231] Users input or upload their blood test results using a smartphone or other device. For manual input, users enter the required nutrient values ​​(e.g., iron, vitamin D, calcium, etc.) into a form displayed on the device screen. For file upload, users can upload electronic files in CSV or Excel format to the device.

[1232] 2. Data Analysis

[1233] The server receives the blood test data sent from the device and begins analysis. First, the server performs basic validation (checking the format and abnormal values). Then, it retrieves normal range nutrient levels from a database and compares them with the input blood test data to identify nutrient deficiencies. Here, data analysis tools such as Python and TensorFlow are used.

[1234] 3. Proposal generation

[1235] The server searches the database for appropriate ingredients and dishes based on the identified nutrient deficiencies. For example, if you are iron deficient, it will select dishes containing spinach, lean meat, and lentils. The list of selected dishes includes ingredients, instructions, and nutritional information. Based on this information, it generates recipe suggestions for the user.

[1236] 4. Displaying results and ordering

[1237] The device will display the generated dish suggestions to the user, who can then review the suggested dishes and place an order directly from the device. By tapping the order button, the corresponding order information will be sent via the food delivery partner's API.

[1238] 5. Food delivery

[1239] The food delivery partner will prepare the ordered food and deliver it to the user's designated location. Users can check the delivery status in real time through the application.

[1240] Specific examples

[1241] For example, suppose a user enters blood test results showing iron levels of 30 μg / dL, vitamin D levels of 15 ng / mL, and calcium levels of 10 mg / dL. The system analyzes this and determines that the user is iron deficient. The system then suggests dishes to supplement the iron levels, such as spinach and lean meat stir-fry and lentil soup. The user can view these dishes on their smartphone screen and order the dishes displayed with the message, "You are low in iron. Try spinach and lean meat stir-fry or lentil soup."

[1242] Prompt Sentence Examples

[1243] "I have entered my blood test results, which show iron at 30 μg / dL, vitamin D at 15 ng / mL, and calcium at 10 mg / dL. Please suggest appropriate dishes based on the nutrients I am lacking."

[1244] This system allows users to easily understand their nutritional status, select the appropriate meals to supplement it, and order and receive them without any hassle.

[1245] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1246] Step 1: Data entry

[1247] Users can input or upload their blood test results using a smartphone or other device. For manual input, users enter values ​​such as iron, vitamin D, and calcium into the application form. For file upload, users can upload CSV or Excel format files to their device.

[1248] Input: Blood test results entered or uploaded by the user

[1249] Output: Blood test result data (manually entered values ​​or uploaded file contents)

[1250] Specific processing behavior:

[1251] The user opens the app on their smartphone and accesses the blood test result input screen.

[1252] Enter the required values ​​or select a file, then press the send button.

[1253] Step 2: Data validation and submission

[1254] The terminal receives the entered blood test data, performs basic validation, and then sends the validated data to the server.

[1255] Input: Blood test result data

[1256] Output: Validated blood test data sent to the server

[1257] Specific processing behavior:

[1258] The terminal checks the format of the input data and checks for invalid or missing values.

[1259] Data that has been successfully validated is sent to the server via an HTTP request.

[1260] Step 3: Data analysis and identification of nutrient deficiencies

[1261] The server analyzes the received blood test data and identifies any deficiencies in nutrients by comparing it with a database of nutrients in the normal range.

[1262] Input: Blood test data sent to the server

[1263] Output: List of nutrients that are lacking

[1264] Specific processing behavior:

[1265] The server retrieves the normal range for each nutrient from the database.

[1266] Compare received blood test results with normal ranges to identify nutrient deficiencies.

[1267] List any identified nutrient deficiencies.

[1268] Step 4: Generate dish suggestions

[1269] Based on the identified nutrient deficiencies, the server searches the database for appropriate ingredients and dishes to compensate for them and generates a list of dishes.

[1270] Input: List of nutrients you are deficient in

[1271] Output: A list of suggested dishes, including ingredients, instructions, and nutritional information

[1272] Specific processing behavior:

[1273] Search the database for ingredients and dishes that address missing nutrients.

[1274] Get detailed recipe information (ingredients, instructions, nutritional information).

[1275] Generate a list of suggested dishes.

[1276] Step 5: View results and place orders

[1277] The device displays the generated food suggestions to the user, who has the option to review the suggested dishes and order directly. The user's order information is sent via the food delivery partner's API.

[1278] Input: A list of dishes to suggest

[1279] Output: Detailed information about the food selected and ordered by the user

[1280] Specific processing behavior:

[1281] The terminal displays a list of suggested dishes to the user.

[1282] The user selects a dish and presses the order button.

[1283] Order information is sent to the food delivery partner's API.

[1284] Step 6: Food preparation and delivery

[1285] The food delivery partner prepares the ordered food and delivers it to the user's designated location. The user can check the delivery status in real time through the application.

[1286] Input: Details of the food ordered by the user

[1287] Output: The meal delivered to the user

[1288] Specific processing behavior:

[1289] The food delivery partner receives the order information and prepares the corresponding food.

[1290] The cooked food is delivered to the user's specified address.

[1291] The user can check the delivery status on the application and receive a notification that delivery has been completed.

[1292] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1293] This invention combines a system that identifies nutrient deficiencies based on the user's blood test results and suggests recipes to supplement those deficiencies with an emotion engine that recognizes the user's emotions. This system makes suggestions based on the user's emotional state, thereby realizing more personalized nutritional supplementation suggestions.

[1294] Data Entry and Emotion Recognition

[1295] Users use a device to input their blood test results or upload them as a file. There are two input methods: manual input and file upload. With manual input, users input the numerical values ​​of each nutrient into the input form on the device. With file upload, users select and upload a CSV or Excel file containing the blood test results.

[1296] Furthermore, the device is equipped with a camera and microphone, and the emotion engine uses these devices to recognize emotions from the user's facial expressions and voice. Facial recognition technology is used to determine emotions such as smile, anger, and sadness, and voice analysis is used to analyze the user's intentions and emotions.

[1297] Data transmission and analysis

[1298] The device sends the input blood test data and recognized emotion data to the server. The server analyzes the received blood test data and connects to a database to obtain normal nutrient range information. It then compares the user's blood test results with the normal range and identifies nutrient deficiencies.

[1299] Identifying nutrient deficiencies and suggesting recipes

[1300] The server uses the analysis results to create a list of nutrients (e.g., iron, vitamin D) that are lacking. Furthermore, the emotion engine customizes the suggested dishes based on the user's emotional state. For example, if the server detects that the user is under stress, it will suggest ingredients and dishes that have a relaxing effect.

[1301] The server creates a list of suitable dishes and compiles the ingredients, instructions, and nutritional information for each dish, including the dish name, ingredient list, cooking instructions, and nutritional information.

[1302] Results display

[1303] The server sends the generated cooking suggestions to the device, which then displays the received suggestions to the user. For example, it might say, "You're low in iron, and you seem to be under stress. I recommend making a stir-fry of spinach and lean meat, and some relaxing mint tea."

[1304] Specific examples

[1305] 1. The user enters the following blood test results into the terminal:

[1306] Iron: 30 μg / dL

[1307] Vitamin D: 15 ng / mL

[1308] Calcium: 10 mg / dL

[1309] 2. The device receives this data and sends it to the server.

[1310] 3. The server performs the analysis and identifies that there is an iron deficiency.

[1311] 4. The emotion engine recognizes the user's stress level from their facial expressions and voice.

[1312] 5. The server recommends a spinach and lean meat stir fry as a way to supplement iron intake, and also suggests adding mint tea to reduce stress.

[1313] 6. The server sends this information to the terminal and displays it to the user:

[1314] "You're iron deficient. You also seem to be under stress. I'd recommend making a spinach and lean meat stir fry and a relaxing mint tea."

[1315] This allows users to accurately identify their nutritional deficiencies, easily select and execute specific meals to supplement them, and even receive advice based on their emotional state.

[1316] The processing flow will be explained below.

[1317] Step 1: Data entry

[1318] Users enter blood test results on the device or upload them as a file.

[1319] In the case of manual input, the user inputs the numerical value of each nutrient (e.g., iron, vitamin D, calcium, etc.) into an input form on the terminal.

[1320] When uploading a file, the user selects and uploads a CSV or Excel file containing the blood test results.

[1321] Step 2: Recognize emotions

[1322] Using the device's built-in camera and microphone, the emotion engine analyzes the user's facial expressions and voice.

[1323] In facial expression analysis, the emotion engine identifies emotions from the user's facial micro-expressions (e.g., smile, sadness, anger).

[1324] In voice analysis, the emotion engine determines emotions from the user's voice patterns and tone.

[1325] Step 3: Send data

[1326] The terminal transmits the input blood test data and the recognized emotion data to the server.

[1327] The transmitted information includes the values ​​of each nutrient in the blood test and the determined emotion (e.g., stressed, relaxed).

[1328] Step 4: Data analysis

[1329] The server analyzes the received blood test data and connects to a database to obtain normal range information for nutrients.

[1330] The server compares the user's blood test results with normal ranges and identifies deficiencies in each nutrient.

[1331] Example: "The user's iron level is 30 μg / dL, which is insufficient compared to the normal range of 50-150 μg / dL."

[1332] Step 5: Identify nutrient deficiencies and choose meals

[1333] The server searches a database for appropriate ingredients and recipes based on the identified nutrient deficiencies.

[1334] The system customizes the best meal for the user based on the identified emotional state (e.g., stress state).

[1335] For example: "Choose a spinach and lean meat stir fry for iron and add mint tea to reduce stress."

[1336] Step 6: Generate dish suggestions

[1337] The server generates a recommendation containing details of the selected dish (dish name, ingredient list, cooking instructions, nutritional information).

[1338] Examples: "Spinach and lean meat stir fry" and "Relaxing mint tea."

[1339] Step 7: Send results

[1340] The server sends the generated recipe suggestions to the terminal.

[1341] The proposed information is formatted in a way that is easy for the user to review.

[1342] Step 8: View the results

[1343] The terminal displays the received recipe suggestions to the user.

[1344] For example: "You're low in iron and seem to be under stress. I'd recommend making a spinach and lean meat stir fry and a relaxing cup of mint tea."

[1345] Step 9: Execution

[1346] The user prepares the necessary ingredients and creates the dish based on the suggested recipe.

[1347] This allows users to supplement missing nutrients and eat meals that take into consideration their emotional state.

[1348] Example 2

[1349] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1350] While conventional systems can identify nutritional deficiencies based on blood test results and suggest meals to replenish these nutrients, they are unable to provide personalized recommendations that take into account the user's emotional state. As a result, they lack the information needed for users to truly achieve optimal nutritional intake.

[1351] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1352] In this invention, the server includes means for analyzing blood test results when the blood test results are input and identifying deficient nutrients, means for suggesting dishes to supplement the deficient nutrients based on the deficient nutrients, and means for recognizing the user's emotional state and making suggestions according to the emotional state. This makes it possible to not only suggest dishes to appropriately supplement the user's deficient nutrients, but also to make personalized suggestions according to the user's emotional state.

[1353] "Blood test results" are numerical information on various nutrients obtained by analyzing the user's blood.

[1354] "Nutrients" are chemicals or compounds that the body needs to maintain normal function.

[1355] A "deficient nutrient" is a nutrient that is identified as being below a standard value based on blood test results.

[1356] The "means of analysis" refers to devices and algorithms that analyze blood test results and evaluate the values ​​of each nutrient.

[1357] "Cooking" refers to food made to supplement the nutrients that the user is lacking, and specific ingredients and cooking methods are suggested.

[1358] The "user's emotional state" refers to the user's psychological or emotional state as recognized through facial expressions, voice analysis, etc.

[1359] An "emotion engine" is a software or hardware system for recognizing and analyzing a user's emotional state.

[1360] A "displaying means" is a device such as a display or screen that visually presents suggested information or messages to a user.

[1361] The "means of suggestion" refers to an algorithm or system that selects appropriate dishes and nutritional supplementation methods based on the analysis results and notifies the user of them.

[1362] The "server" is a computer system that processes blood test results and emotion data and generates proposals based on the analysis results.

[1363] The present invention provides a system for providing personalized nutritional support based on a user's blood test results and emotional state. The system mainly includes a user, a terminal, and a server.

[1364] First, users enter their blood test results using the device. There are two ways to enter blood test results: manual entry or file upload. For manual entry, users enter the numerical values ​​of each nutrient directly into the input form on the device. For file upload, users can upload files in CSV or Excel format.

[1365] The device is equipped with a camera and microphone, which are used to recognize the user's emotional state. The emotion engine uses facial recognition and voice analysis technologies to determine emotions such as smile, anger, and sadness, making it possible to grasp the user's emotional state in real time.

[1366] The device sends the input blood test data and recognized emotion data to the server. The server analyzes the received blood test data and connects to a database to obtain normal nutrient range information. It then compares the user's blood test results with the normal range and identifies any nutrient deficiencies.

[1367] The server then suggests dishes based on the nutritional deficiencies of the user. These suggestions are customized based on the user's emotional state as recognized by the emotion engine. For example, if the user is under stress, the server will suggest ingredients and dishes that have a relaxing effect.

[1368] To give a concrete example, suppose a user enters the following blood test results into a terminal:

[1369] Iron: 30 μg / dL

[1370] Vitamin D: 15 ng / mL

[1371] Calcium: 10 mg / dL

[1372] The device sends this data to the server, which analyzes it and identifies the iron deficiency. The emotion engine recognizes the user's stress level from their facial expressions and voice.

[1373] The server selects a dish of stir-fried spinach and lean meat to supplement iron, and suggests adding mint tea to reduce stress. The server sends this information to the terminal, which displays the following to the user:

[1374] "You're iron deficient. You also seem to be under stress. I'd recommend making a spinach and lean meat stir fry and a relaxing mint tea."

[1375] Here's an example prompt for a generative AI model:

[1376] Based on the user's blood test results and emotional data, we want to identify the nutrients that are lacking and suggest dishes that correspond to the user's emotional state. For example, the user is lacking iron and vitamin D, and is under stress. Please take this into consideration when suggesting appropriate dishes.

[1377] In this way, the present invention can suggest personalized recipes for nutritional supplementation based on the user's blood test results and emotional data, allowing the user to obtain specific methods for efficiently supplementing their nutritional deficiencies and receive appropriate advice based on their emotional state.

[1378] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1379] Step 1:

[1380] The user enters the blood test results.

[1381] Input: Blood test results (e.g., iron, vitamin D, calcium levels)

[1382] How it works: The user manually enters each value into the input form on the device, or selects and uploads a CSV / Excel file.

[1383] Output: The entered blood test result data is saved on the terminal.

[1384] Step 2:

[1385] The device uses a camera and microphone to recognize the user's emotional state.

[1386] Input: Camera video, microphone audio

[1387] How it works: The device's camera captures the user's facial expressions, and the microphone records the user's voice. These data are then sent to the emotion engine.

[1388] Output: User's emotional state (e.g., stress, sadness, joy, etc.) via the emotion engine

[1389] Step 3:

[1390] The terminal transmits the input blood test data and emotion data to the server.

[1391] Input: Blood test result data, emotional state data

[1392] How it works: The device encrypts this data and sends it to the server.

[1393] Output: The server receives the blood test data and emotion data.

[1394] Step 4:

[1395] The server analyzes the received blood test data.

[1396] Input: Blood test data

[1397] How it works: The server connects to the database and retrieves normal range information for each nutrient. This is then compared with the user's blood test results to identify any nutrient deficiencies.

[1398] Output: List of nutrients that are lacking

[1399] Step 5:

[1400] The server customizes food suggestions based on data from the emotion engine.

[1401] Input: List of nutrient deficiencies, emotional state data

[1402] How it works: The server applies an algorithm that suggests dishes based on the user's emotional state. For example, if stress is detected, it will suggest dishes that have a relaxing effect.

[1403] Output: A customized list of food suggestions (e.g., spinach stir-fry with lean meat, mint tea, etc.)

[1404] Step 6:

[1405] The server generates a list of suitable dishes and sends it to the device.

[1406] Input: Customized food suggestion list

[1407] Operation: The server converts the recipe list into a data format and sends it to the terminal.

[1408] Output: The device receives the list of recipe suggestions.

[1409] Step 7:

[1410] The terminal displays the cooking suggestions received from the server to the user.

[1411] Input: list of dish suggestions

[1412] How it works: The device decodes the suggestion list and visually displays it to the user.

[1413] Output: The user can visually see the suggested recipes (e.g., "You're low in iron and appear to be under stress. I suggest you make a spinach and lean meat stir fry and a relaxing mint tea.")

[1414] (Application example 2)

[1415] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1416] Conventional nutritional supplement suggestion systems identify nutrient deficiencies based on a user's blood test results and suggest recipes to supplement them. However, a user's emotional state can affect nutrient absorption and effectiveness, and suggestions that do not take emotional state into account may not achieve optimal results. Furthermore, entering blood test results can be cumbersome, and suggested dishes may not be appropriate for the user's emotional state. Furthermore, in the field of food delivery, there is a lack of recipe suggestions based on health status, which means that the system is unable to meet the diverse needs of users.

[1417] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for analyzing blood test results to identify nutrient deficiencies, means for suggesting dishes to supplement the identified nutrient deficiencies, and means for recognizing the user's emotions and customizing the dishes based on the user's emotional state. This makes it possible to suggest more personalized dishes that provide optimal nutrition based on the blood test results and the user's emotional state. Furthermore, by displaying the suggested dishes to the user and allowing them to directly order them through a food delivery service, a system is realized that reduces the user's effort and provides dishes that are suitable for their health and emotional state.

[1418] "Blood test results" is information that indicates the numerical values ​​of each nutrient analyzed based on the user's blood data.

[1419] "Means for identifying" refers to methods or devices that analyze blood test results to identify nutrients that are lacking.

[1420] The "means for suggesting recipes" refers to a method or device that provides the user with the optimum recipe to supplement the nutrients that are lacking.

[1421] The "display means" refers to a method or device that visually shows the suggested dishes and related information to the user.

[1422] "Means for recognizing emotions" refers to a method or device for determining the emotional state of a user from their facial expressions and voice.

[1423] The "means for customizing dishes according to emotional state" refers to a method or device for selecting and suggesting dishes that best suit the user's emotions based on the recognized emotions.

[1424] The "server for analysis" is a computer system that receives the transmitted blood test results and emotion data and performs analysis.

[1425] This invention is a system that suggests optimal meals for nutrient replenishment based on the user's blood test results and emotional state. This system is mainly composed of a terminal such as a smartphone and a server that performs analysis. Detailed embodiments of this invention are described below.

[1426] First, the user uses the device to enter the blood test results. There are two input methods: manual input and file upload. With manual input, the user enters the numerical values ​​of each nutrient into the input form on the device, and with file upload, the user selects and uploads a CSV or Excel file containing the blood test results.

[1427] The device is also equipped with a camera and microphone, and the emotion recognition engine uses these devices to recognize emotions from the user's facial expressions and voice. Facial recognition technology is used to determine emotions such as smile, anger, and sadness, and voice analysis is used to analyze the user's intentions and emotions. Specific technologies used here include OpenCV and machine learning models.

[1428] The device sends the entered blood test data and recognized emotion data to a server. The server analyzes the received blood test data and connects to a database to obtain normal nutrient range information. It then compares the user's blood test results with the normal range to identify nutrient deficiencies. It then creates a list of nutrients that are deficient (e.g., iron, vitamin D).

[1429] The emotion recognition engine customizes the suggested dishes according to the user's emotional state. For example, if the system detects that the user is under stress, it will suggest ingredients and dishes that have a relaxing effect. The server creates a list of suitable dishes and compiles information about the ingredients, cooking steps, and nutritional value of each dish. This information includes the name of the dish, a list of ingredients, cooking steps, and nutritional value.

[1430] Finally, the server sends the generated cooking suggestions to the device, and the device displays the received suggestions to the user. For example, it might display on the screen, "You are low in iron and appear to be under stress. I recommend making a stir-fry of spinach and lean meat and a cup of mint tea for relaxation."

[1431] This allows users to accurately identify their nutritional deficiencies, easily select and execute specific meals to supplement them, and even receive advice based on their emotional state. Furthermore, users can also order the suggested meals directly from a food delivery application.

[1432] As a concrete example, suppose a user enters their blood test results into an application, smiles or makes other facial expressions in front of the camera, and the blood test results indicate an iron deficiency. The emotion engine recognizes that the user is stressed. Based on this information, the application suggests a spinach and lean meat stir-fry with a relaxing mint tea and offers the option to order the meal from a food delivery service.

[1433] The following sentences are examples of prompt sentences:

[1434] "Enter your blood test results and smile or make other facial expressions for the camera."

[1435] "Vitamin D is 15 ng / mL, calcium is 10 mg / dL, and iron is 30 μg / dL."

[1436] "The application will analyze it and suggest dishes."

[1437] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1438] Step 1:

[1439] Users enter blood test results using a terminal. There are two input methods: manual input, where the numerical values ​​of each nutrient are entered into an input form, and file upload, where a CSV or Excel file is selected and uploaded. This input data is used for subsequent analysis.

[1440] Step 2:

[1441] The terminal receives the blood test data and processes it accordingly, primarily converting the entered nutrient values ​​into a standard format. This processed data is then sent to the server.

[1442] Step 3:

[1443] The device's camera and microphone are used to capture the user's facial expressions and voice. Using facial recognition technology such as OpenCV, the system recognizes the user's emotional state in real time. This emotional recognition data is also sent to the server.

[1444] Step 4:

[1445] The server analyzes the received blood test data and connects to a database to obtain normal nutrient range information. This allows the server to compare the user's blood test results with the normal ranges and identify which nutrients are deficient. A list of deficient nutrients is generated.

[1446] Step 5:

[1447] The server analyzes the emotion recognition data and determines the user's emotional state. This allows the server to understand whether the user is feeling stressed or relaxed. A machine learning model is used to achieve highly accurate emotion determination.

[1448] Step 6:

[1449] The server selects the most appropriate dish based on the user's lacking nutrients and emotional state. For example, if the user is lacking in iron and is under stress, the server will suggest stir-fried spinach and lean meat and a cup of relaxing mint tea. A list of dishes is generated based on the user's lacking nutrients and emotional state.

[1450] Step 7:

[1451] The server then sends the generated recipe list, ingredients, instructions, and nutritional information to the device, where it is formatted for display and transmitted in a format that is easily understood by the user.

[1452] Step 8:

[1453] The device receives recipe suggestions from the server and displays them on the screen, such as a message like, "You're low in iron and appear to be under stress. I recommend making a stir-fry of spinach and lean meat, and a cup of relaxing mint tea."

[1454] Step 9:

[1455] Users can select suggested dishes on their device and order them through a food delivery service, allowing them to consume optimal nutrients based on their health and mood.

[1456] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1457] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1458] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1459] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1460] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1461] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1462] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1463] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1464] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1465] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1466] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1467] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1468] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1469] 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.

[1470] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1471] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1472] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1473] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1474] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1475] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1476] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1477] The following is further disclosed regarding the above embodiment.

[1478] (Claim 1)

[1479] a means for analyzing the blood test results when the blood test results are input and identifying nutrients that are deficient;

[1480] A means for suggesting a recipe for supplementing the lacking nutrients based on the lacking nutrients;

[1481] means for displaying the suggested dishes to a user;

[1482] A system including:

[1483] (Claim 2)

[1484] 2. The system according to claim 1, wherein the blood test results are input by manual input or file upload.

[1485] (Claim 3)

[1486] 2. The system of claim 1, further comprising means for comparing the blood test results with normal ranges for nutrients to identify nutrients that are deficient.

[1487] "Example 1"

[1488] (Claim 1)

[1489] a means for a user to input or upload blood test results using the terminal;

[1490] The terminal validates the blood test results entered and checks for abnormal values;

[1491] A means for the terminal to transmit the validated blood test results to a server;

[1492] a means for the server to retrieve normal range nutrient levels from the database and compare them with the blood test results to identify nutrient deficiencies;

[1493] A means for the server to search for ingredients and dishes to supplement the missing nutrients and generate a list of dishes;

[1494] means for transmitting the cooking suggestions generated by the server to the terminal and displaying them to the user;

[1495] A system including:

[1496] (Claim 2)

[1497] 2. The system according to claim 1, wherein blood test results are input by manual input or file upload.

[1498] (Claim 3)

[1499] 2. The system of claim 1, further comprising means for comparing blood test results with normal ranges for nutrients to identify nutrients that are deficient.

[1500] "Application Example 1"

[1501] (Claim 1)

[1502] a means for analyzing the blood test results when the blood test results are input and identifying nutrients that are deficient;

[1503] A means for suggesting a recipe for supplementing the lacking nutrients based on the lacking nutrients;

[1504] means for displaying the suggested dishes to a user;

[1505] a means for the user to directly order the suggested dishes;

[1506] means for delivering the ordered food to the user;

[1507] A system including:

[1508] (Claim 2)

[1509] 2. The system according to claim 1, wherein the blood test results are input by manual input or file upload.

[1510] (Claim 3)

[1511] 2. The system of claim 1, further comprising means for comparing the blood test results with normal ranges for nutrients to identify nutrients that are deficient.

[1512] "Example 2: Combining Emotion Engines"

[1513] (Claim 1)

[1514] a means for analyzing the blood test results when the blood test results are input and identifying nutrients that are deficient;

[1515] A means for suggesting a recipe for supplementing the lacking nutrients based on the lacking nutrients;

[1516] means for recognizing an emotional state of a user and making suggestions according to said emotional state;

[1517] means for displaying the suggested dishes to a user;

[1518] A system including:

[1519] (Claim 2)

[1520] 2. The system according to claim 1, wherein the blood test results are input by manual input or file upload.

[1521] (Claim 3)

[1522] 2. The system of claim 1, further comprising means for comparing the blood test results with normal ranges for nutrients to identify nutrients that are deficient.

[1523] "Application example 2 when combining emotion engines"

[1524] (Claim 1)

[1525] a means for analyzing the blood test results when the blood test results are input and identifying nutrients that are deficient;

[1526] A means for suggesting a recipe for supplementing the lacking nutrients based on the lacking nutrients;

[1527] means for displaying the suggested dishes to a user;

[1528] means for recognizing a user's emotion;

[1529] means for customizing a dish according to the emotional state of a user based on the emotion recognition;

[1530] a server for analyzing the blood test results and emotion data;

[1531] A system including:

[1532] (Claim 2)

[1533] The system according to claim 1, characterized in that the blood test results are input by manual input or file upload.

[1534] (Claim 3)

[1535] 2. The system of claim 1, further comprising means for identifying a nutrient that is deficient by comparing it with a normal range for that nutrient. [Explanation of symbols]

[1536] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. a means for analyzing the blood test results when the blood test results are input and identifying nutrients that are deficient; A means for suggesting a recipe for supplementing the lacking nutrients based on the lacking nutrients; means for displaying the suggested dishes to a user; A system including:

2. The system according to claim 1 , wherein the blood test results are input by manual input or file upload.

3. 2. The system of claim 1, further comprising means for comparing the blood test results with normal ranges for nutrients to identify nutrient deficiencies.

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