System

Abstract

A system is provided.SOLUTION: A system comprising: means for a user to input body shape and constitution data; means for receiving the input data and storing the data in a database; means for analyzing the stored data with a generative AI model and collating with past success cases to generate diet advice; and means for displaying the generated advice on a user device.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] Many people struggle to find the optimal diet method for themselves, and if they fail to find the right method, they may not see any results or even harm their health. This can often lead to a loss of motivation to continue dieting. The present invention aims to efficiently and statistically find the optimal diet method for each user's body shape and constitution, enabling many people to achieve their diet goals in a healthy manner. [Means for solving the problem]

[0005] The present invention is a system that includes a means for a user to input body shape and constitution data, a means for receiving the input data and saving it in a database, a means for analyzing the saved data using a generative AI model and comparing it with past success cases to generate diet advice, and a means for displaying the generated advice on a user device. The system also includes a means for displaying a form for collecting the user's body shape and constitution data and providing a data submission function, a means for preprocessing the data to be analyzed by the generative AI model, and a means for generating advice based on the analysis results. This makes it possible to provide each user with an individually optimized diet method and increase the success rate of dieting.

[0006] "User" refers to an individual who utilizes the system to input their own body shape and constitution data and receive optimal diet advice.

[0007] "Body shape and constitution data" refers to information related to the user's physical characteristics and constitution, such as height, weight, age, and bone structure.

[0008] "Input means" refers to the interface or form that a user uses to provide their body shape and constitution data to the system.

[0009] "Means for receiving" refers to a function for receiving data entered by a user within the system and sending it to the next processing step.

[0010] "Database" refers to a storage device that stores data on a user's body shape and constitution, and that can be searched and retrieved as needed.

[0011] A "generative AI model" refers to an algorithm or program that learns from past successful dieting cases and suggests the optimal dieting method based on user data.

[0012] "Means of analysis" refers to the process of using a generative AI model to analyze the user's input data and generate diet advice.

[0013] "Means of matching" refers to the process of comparing the data analyzed by the generative AI model with data from past successful diet cases to determine the optimal diet method for the user.

[0014] "Diet advice" refers to guidance and suggestions regarding optimal diet methods that are analyzed and presented based on the user's body shape and constitution data.

[0015] "Means for displaying" refers to a function for displaying the analyzed diet advice on the user's device so that the user can easily access it.

[0016] "Form" refers to a digital interface through which a user enters their body type and constitution data.

[0017] "Data transmission function" refers to a function for transmitting data entered by a user to a server.

[0018] "Preprocessing means" refers to the process of formatting or performing any necessary transformations on user data before it is analyzed by the generative AI model. [Brief explanation of the drawings]

[0019] [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

[0020] 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.

[0021] First, the terms used in the following description will be explained.

[0022] 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).

[0023] 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.

[0024] 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.

[0025] 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.

[0026] 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."

[0027] [First embodiment]

[0028] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0029] 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.

[0030] 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).

[0031] 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.

[0032] 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.

[0033] 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.

[0034] 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.

[0035] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0036] 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.

[0037] 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.

[0038] 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.

[0039] 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."

[0040] The present invention is a personal diet application that uses a generative AI model to provide optimal dieting methods based on a user's body shape and constitution data. The system includes a series of steps to receive data entered by the user, analyze it, and generate optimal diet advice.

[0041] First, users log in to the application and enter their skeletal and physical data, including height, weight, age, and gender, which are designed to reflect the user's physical details.

[0042] Next, the terminal receives the data entered by the user and sends it to the server. A data entry form is displayed on the terminal, and when the user enters data and presses the submit button, the terminal sends the data to the server as a POST request. The server saves the received data in a database. The database centrally manages body shape and constitution data for each user and retains the data for use in subsequent analysis.

[0043] The server retrieves user data from the database and passes it to the generative AI model for analysis. The generative AI model has learned successful diet methods for users with similar body types and constitutions in the past, and generates optimal diet advice using new user data as input. Based on the results of this analysis, the server constructs advice on the optimal diet method for the user.

[0044] The server then returns the generated advice to the user's device. The device then displays the advice data received from the server so that the user can easily view it. For example, if a diet method that combines both "fat-burning exercise" and "balanced diet intake" is recommended to the user, the user can check the detailed steps and precautions within the application.

[0045] As a concrete example, suppose a user enters data such as height 170 cm, weight 75 kg, and age 30. This data is sent to the server, which stores it in a database. The stored data is analyzed by a generative AI model, which generates advice such as "exercise aerobically three times a week and limit calorie intake to 1800 kcal" based on past success stories of people with a similar body shape and constitution. The server then sends this advice back to the user's device and displays it on the user's device. The user can then follow this advice and put the specific diet plan into practice.

[0046] In this way, the present invention provides a system that helps users achieve their diet goals in an efficient and healthy manner.

[0047] The processing flow will be explained below.

[0048] Step 1:

[0049] User

[0050] Users log in to the application and enter their skeletal and physical data (height, weight, age, etc.).

[0051] Step 2:

[0052] Terminal

[0053] The terminal provides the user with an input form and prompts them to enter data.

[0054] Provide a button (submit button) for users to submit input data.

[0055] Step 3:

[0056] Terminal

[0057] When the user presses the send button, the terminal sends the input data to the server as a POST request.

[0058] Step 4:

[0059] server

[0060] The server receives the POST request, analyzes the contents of the request, and obtains the user's input data.

[0061] Step 5:

[0062] server

[0063] The server stores the retrieved user data in a database.

[0064] Step 6:

[0065] server

[0066] The server retrieves the necessary user data from the database and passes it to the generative AI model for analysis.

[0067] Step 7:

[0068] server

[0069] The generative AI model analyzes user data based on past success stories and generates optimal diet advice.

[0070] Step 8:

[0071] server

[0072] The server sends the generated diet advice back to the user device.

[0073] Step 9:

[0074] Terminal

[0075] The terminal receives the advice data from the server and displays it in a format that is easy for the user to view.

[0076] Step 10:

[0077] User

[0078] The user checks the displayed diet advice and puts into practice the specific diet plan.

[0079] Example 1

[0080] 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."

[0081] Conventional diet support systems have had difficulty providing personalized diet advice based on the user's body shape and constitution. In particular, they lacked the means to generate individually optimized advice in real time based on body shape and constitution data, forcing users to follow general advice, resulting in low success rates for dieting. There is a need to solve this problem and provide more accurate personalized diet advice to efficiently support users in maintaining and improving their health.

[0082] 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.

[0083] In this invention, the server includes a means for sending data entered by the user as an HTTP POST request, a means for storing the data received by the server in a database via a query, a means for sending an analysis request to the generative AI model using a prompt sentence, and a means for returning the generated advice in JSON format, thereby enabling the generation and provision of highly accurate personalized diet advice in real time based on the user's body shape and constitution data.

[0084] "User" refers to an individual who uses the personal diet application to input their own body shape and constitution data and receive diet advice.

[0085] The "server" is a computer system that has the function of receiving data sent by users, storing it in a database, analyzing the data using a generative AI model, generating optimal diet advice, and delivering it to the user's device.

[0086] "Terminal" refers to a device used by a user to input body shape and constitution data, and includes smartphones, tablets, PCs, etc.

[0087] A "database" is a system for centrally managing and storing each user's body shape and constitution data, and includes relational databases such as MySQL and PostgreSQL.

[0088] A "generative AI model" is an artificial intelligence model that learns from past body shape and constitution data and successful diet cases, and analyzes new data to generate optimal diet advice.

[0089] A "prompt sentence" is an instruction sentence used when sending an analysis request to a generative AI model, and is a sentence that instructs the model to generate diet advice based on the user's specific body shape and constitution data.

[0090] An "HTTP POST request" is a communication method for sending data entered by a user to a server, and is a protocol for sending data in JSON format or other formats.

[0091] The "JSON format" is a lightweight text-based data format used for data exchange, and is primarily composed of key-value pairs.

[0092] The present invention is a personal diet application that uses a generative AI model to provide optimal diet advice based on a user's body shape and constitution data. The system includes a series of steps to receive data entered by the user, analyze it, and generate optimal diet advice.

[0093] First, the user logs in to the application using a dedicated device (smartphone, tablet, PC) and enters their body shape and constitution data. The input data includes height, weight, age, gender, etc. This input form is designed to allow the user to easily enter the required information. For example, suppose a user enters data such as height 170 cm, weight 75 kg, and age 30.

[0094] Next, the device sends the data entered by the user to the server. The data is sent in JSON format using an HTTP POST request. Specifically, the data format is as follows:

[0095] json

[0096] {

[0097] "Height": 170,

[0098] "Weight": 75,

[0099] "Age": 30,

[0100] "Gender": "Male"

[0101] }

[0102] The server receives the data sent from the device and stores it in a database (MySQL, PostgreSQL, etc.). The database serves to centrally manage the body shape and constitution data for each user.

[0103] Based on the saved data, the server sends an analysis request to the generative AI model. For example, OpenAI's GPT-4 can be used as the generative AI model. The prompt statement input to the generative AI model is, "Please provide the optimal diet plan for a user who is 170 cm tall, weighs 75 kg, and is 30 years old." The generative AI model performs analysis based on this prompt statement and generates optimal diet advice.

[0104] For example, the generative AI model generates advice such as "Do aerobic exercise three times a week and limit your calorie intake to 1,800 kcal." The analysis results are then sent back to the server.

[0105] The server returns the diet advice received from the generative AI model to the user device (terminal) in JSON format. The terminal analyzes the advice data received from the server and displays it so that the user can easily view it. For example, the user's screen might display a specific plan and its details, such as "Perform aerobic exercise three times a week and keep calorie intake below 1800 kcal."

[0106] This allows users to implement an individually optimized diet plan based on their own body shape and constitution, and the system helps users achieve their diet goals efficiently and healthily.

[0107] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0108] Step 1:

[0109] The user logs in to the personal diet application on their device. After logging in, a form is displayed in which they can enter their body shape and constitution data, such as height, weight, age, and gender. The user enters this data into the form and presses the "Submit" button. The input here is assumed to be height 170cm, weight 75kg, age 30, and gender male.

[0110] Input: User data (height, weight, age, gender)

[0111] Output: Data sent from the device to the server

[0112] Specific operation: The user enters their height (170 cm), weight (75 kg), age (30 years old, etc.) into the input fields displayed on the screen of their smartphone or PC, and presses the "Send" button.

[0113] Step 2:

[0114] The terminal sends the data entered by the user to the server in JSON format as an HTTP POST request. The JSON data has the following format:

[0115] json

[0116] {

[0117] "Height": 170,

[0118] "Weight": 75,

[0119] "Age": 30,

[0120] "Gender": "Male"

[0121] }

[0122] Input: User data formatted in JSON format

[0123] Output: User data sent to the server

[0124] Specific operation: The terminal converts the information entered by the user into JSON format and sends it as an HTTP POST request.

[0125] Step 3:

[0126] The server receives the data sent from the device and stores it in a database (e.g., MySQL or PostgreSQL). The server analyzes the received data and stores it through appropriate database queries.

[0127] Input: Received user data (JSON format)

[0128] Output: User data stored in the database

[0129] Specific operation: The server receives the HTTP request, parses the JSON data, and stores it in a database, for example, by executing an INSERT query using an SQL statement.

[0130] Step 4:

[0131] The server retrieves the user data stored in the database and sends an analysis request to the generative AI model. The generative AI model (for example, OpenAI's GPT-4) is asked to perform the analysis using a prompt sentence. An example of a prompt sentence is, "Please provide the optimal diet plan for a user who is 170 cm tall, weighs 75 kg, and is 30 years old."

[0132] Input: User data retrieved from the database

[0133] Output: The analysis request sent to the generative AI model

[0134] Specific operation: The server retrieves user data from the database and creates and sends the API requests required for the generative AI model.

[0135] Step 5:

[0136] The generative AI model generates optimal diet advice based on the prompt and input data, such as "exercise aerobic exercise three times a week and limit your calorie intake to 1,800 kcal."

[0137] Input: Parse request (prompt statement and user data)

[0138] Output: Generated diet advice

[0139] How it works: The generative AI model analyzes user data based on prompt statements and generates specific diet advice.

[0140] Step 6:

[0141] The server then sends diet advice obtained from the generative AI model back to the user device. The advice is sent from the server to the device in JSON format.

[0142] Input: Generated diet advice

[0143] Output: Advice data sent to the terminal

[0144] Specific operation: The server converts the generated advice into JSON format and sends it to the user device as an HTTP response.

[0145] Step 7:

[0146] The device analyzes the advice data received from the server and displays it on the screen. The user can then check the specific diet plan that has been generated on the application screen and implement it.

[0147] Input: Advice data received from the server

[0148] Output: Diet advice displayed to the user

[0149] Specific operation: The device decodes the received advice and displays it on the screen. For example, it displays a specific plan such as "Do aerobic exercise three times a week and keep your calorie intake below 1800 kcal."

[0150] (Application example 1)

[0151] 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."

[0152] Health management is an important issue for factory workers in harsh working environments. However, conventional health management methods are difficult to provide individualized support and are unable to provide efficient advice. For this reason, there is a need for a system that can provide optimal health advice based on the body shape and constitution of each individual employee.

[0153] 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.

[0154] In this invention, the server includes means for a user to input body shape and constitution data, means for receiving the input data and storing it in a database, means for analyzing the stored data using a generative AI model and comparing it with past success cases to generate diet advice, means for displaying the generated advice on a user device, means for providing health advice for managing the health of factory employees, and means for displaying the health advice on a factory robot or smart glasses. This makes it possible to provide optimal health advice to factory employees based on their individual body shape and constitution data.

[0155] The "means for the user to input data on body shape and constitution" is an interface for the user to input physical information such as their height, weight, age, and sex.

[0156] The "means for receiving the input data and storing it in a database" is a function for transmitting the physical information input by the user to a server and storing the data in a database.

[0157] A "generative AI model" is an artificial intelligence model that learns from past success stories and analyzes new user data to provide optimal diet and health management advice.

[0158] "Past success stories" are cases where optimal health advice based on body shape and constitution was successfully provided based on data collected to date.

[0159] "Diet advice" involves a generative AI model analyzing user data and providing optimal diet and exercise guidelines for managing the user's health.

[0160] "User Device" means the device used by the user to view the advice, such as a smartphone, tablet, or other display device.

[0161] "Means for providing health advice for health management" refers to means for providing guidelines on lifestyle, exercise, and diet necessary for maintaining and improving the health of factory employees.

[0162] "Means for displaying on factory robots and smart glasses" refers to a function that displays the generated health advice on robots and wearable devices used in factories, allowing employees to visually receive the advice.

[0163] This invention is designed as a system to support the health management of factory workers. First, the user, a factory worker, accesses an interface to input their own body shape and physical characteristics data (height, weight, age, gender, etc.). This is done using a form displayed on the display of the smart glasses or robot terminal. Once the user enters and submits the data, it is sent to the server and stored in a database.

[0164] The server retrieves the stored data and passes it to a generative AI model for analysis. The generative AI model, trained on past success stories, generates optimal health advice based on new user data. This generated advice is then sent by the server back to the user's device (smart glasses or factory robot) and displayed to the user.

[0165] As a concrete example, suppose a user enters the following data: height 170 cm, weight 75 kg, age 30, and gender: male. This data is sent to the server, which stores it in a database. As a result of analysis by the generative AI model, health management advice is generated, such as "do aerobic exercise three times a week for 30 minutes each time" and "limit daily calorie intake to 1800 kcal." This advice is sent back from the server to the user's device and displayed on the display of smart glasses or a factory robot.

[0166] This system makes it possible to provide optimal health advice based on individual body shapes and constitutions, and efficiently manage and improve the health of factory employees, which is expected to improve work efficiency and employee satisfaction.

[0167] An example prompt is:

[0168] "Based on the given user data (health profile: height 170cm, weight 75kg, age 30, gender: male), please provide advice on optimal lifestyle, exercise, and dietary habits to maintain physical health."

[0169] The hardware uses smart glasses and factory robots, and the software uses generative AI models, which can handle everything from data input to generating and displaying advice.

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

[0171] Step 1:

[0172] Users input their body shape and physical characteristics (height, weight, age, gender, etc.) using an interface displayed on the display of smart glasses or a factory robot, and the input data is temporarily stored in the device's memory.

[0173] Step 2:

[0174] The terminal sends the data entered by the user to the server. Specifically, the input data is sent to the server as an HTTP POST request. The input data is encoded in JSON format and sent.

[0175] Step 3:

[0176] The server stores the received data in a database. The server's receiving process extracts the data from the POST request and stores it in the database in the appropriate format. At this point, the user's body shape and constitution data are saved in the system.

[0177] Step 4:

[0178] The server retrieves user data from the database and performs preprocessing to pass it to the generative AI model. This preprocessing includes enhancing the data and removing unnecessary information. The preprocessed data is then formatted in a way that is suitable for the generative AI model.

[0179] Step 5:

[0180] The server inputs the preprocessed data into a generative AI model to generate optimal health advice. The generative AI model generates personalized advice based on the given prompt, referencing past success stories. This process results in specific exercise and dietary guidelines.

[0181] Step 6:

[0182] The server retrieves the generated advice and sends it to the user device as an HTTP response, returning the advice content encoded in JSON format.

[0183] Step 7:

[0184] The device decodes the advice data received from the server and displays it to the user. Specific health advice is then visually displayed on the display of smart glasses or a factory robot.

[0185] This allows users to receive optimal health advice based on their individual body shape and physical constitution data, and manage their health based on that advice.

[0186] 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.

[0187] The present invention is a system that combines a personal diet application that uses a generative AI model to provide an optimal diet method based on the user's body shape and constitution data with an emotion engine that recognizes the user's emotions. This system includes a series of steps: receiving user input data, analyzing it, generating optimal diet advice using the generative AI model, and further adjusting it according to the user's emotional state.

[0188] First, the user logs in to the application and enters their skeletal and physical data, including height, weight, age, and gender, which are designed to reflect the user's physical details. The terminal then provides the user with an input form and prompts them to enter the data. A button is also provided for the user to submit the data they have entered.

[0189] The device then sends the entered data to the server. The server receives the POST request, analyzes the request, and retrieves the user's input data. The retrieved data is then stored in a database. The database centrally manages each user's body shape and constitution data and retains the data for use in subsequent analyses.

[0190] Furthermore, the server is equipped with an emotion engine that analyzes the user's voice and face to recognize the user's current emotional state. Using voice and face recognition technology, it determines the user's emotional state and stores that information in a database.

[0191] The server then retrieves the necessary user data from the database and passes it to the generative AI model for analysis. The generative AI model has learned successful diet methods from users with similar body types and constitutions in the past, and generates optimal diet advice based on new user data. The generated advice is adjusted based on the analysis results of the emotion engine. For example, if the user is feeling stressed, the system can add advice to reduce stress.

[0192] The server then sends the adjusted diet advice back to the user device. The terminal then displays the advice data received from the server so that the user can easily view it. Specifically, the user may be recommended a diet method that combines both "fat-burning exercise" and "balanced diet intake," and additional advice such as "relaxation meditation techniques" may be displayed based on the user's emotional state.

[0193] In this way, the present invention provides a system that helps users achieve their diet goals efficiently and healthily. The combination of a generative AI model and an emotion engine provides highly accurate advice, contributing to increased user satisfaction. Furthermore, by taking the user's emotional state into account, more personalized support can be achieved.

[0194] The processing flow will be explained below.

[0195] Step 1:

[0196] User

[0197] Users log in to the application and enter their skeletal and physical data, such as height, weight, age, and gender.

[0198] Step 2:

[0199] Terminal

[0200] The terminal presents the user with an input form, prompting them to enter data, indicating which fields are required, and prompting them to press a submit button once the data is complete.

[0201] Step 3:

[0202] Terminal

[0203] When the user presses the send button, the device sends the entered data to the server as a POST request. HTTPS is used for data transmission to ensure security.

[0204] Step 4:

[0205] server

[0206] The server receives the POST request, analyzes the request, and retrieves the user's input data. The retrieved data is stored in a temporary variable.

[0207] Step 5:

[0208] server

[0209] The server stores the acquired user data in a database, which centrally manages each user's body shape and constitution data.

[0210] Step 6:

[0211] server

[0212] After the server has finished saving the user data, it starts the emotion engine, which analyzes the user's voice and face data to recognize the user's current emotional state.

[0213] Step 7:

[0214] Terminal

[0215] The device displays an interface to collect the user's voice and facial data for the emotion engine. The user provides the data using the camera and microphone.

[0216] Step 8:

[0217] server

[0218] The server receives and analyzes the voice and face data sent from the device, and the result of the analysis is the user's emotional state (e.g., stress or fatigue).

[0219] Step 9:

[0220] server

[0221] The server retrieves the necessary user data from the database and passes it to the generative AI model for analysis. The generative AI model generates optimal diet advice for each user based on past success stories.

[0222] Step 10:

[0223] server

[0224] The server combines the results of the emotion engine's analysis with those of the generative AI model to tailor advice based on the user's emotional state. For example, if a user is feeling stressed, it might also provide advice on how to relax.

[0225] Step 11:

[0226] server

[0227] The server then sends tailored advice back to the user's device, including specific exercise regimes, meal plans, relaxation techniques, and more.

[0228] Step 12:

[0229] Terminal

[0230] The device displays the received advice data and presents it in an easy-to-read format for easy user access. Each piece of advice is provided with a detailed explanation and implementation method.

[0231] Step 13:

[0232] User

[0233] Users can check the displayed diet advice and put it into practice. By incorporating the recommended exercise, diet, and relaxation methods into their daily lives, they can achieve their diet goals efficiently and healthily.

[0234] Example 2

[0235] 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."

[0236] Conventional personalized diet applications provide diet advice based on a user's body shape and constitution data, but they fail to take the user's emotional state into account. As a result, the advice provided may be ineffective when the user is stressed or in other emotional states. Therefore, there is a need for a system that recognizes a user's emotional state and adjusts diet advice based on that state.

[0237] 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.

[0238] In this invention, the server includes means for a user to input body shape and constitution data, means for receiving the input data and storing it in a database, means for analyzing the stored data using a generative AI model and comparing it with past success cases to generate diet advice, means for analyzing the user's voice and face to recognize their emotional state and adjusting the generated diet advice based on the emotional state, and means for displaying the adjusted diet advice on the user device, thereby enabling the provision of personalized diet advice that takes the user's emotional state into consideration.

[0239] "User device" refers to an electronic device used by a user, including a smartphone, tablet, personal computer, etc.

[0240] A "generative AI model" refers to an artificial intelligence model that learns from large amounts of data and generates optimal diet advice from the input data.

[0241] An "emotion engine" refers to a device or software that analyzes a user's voice and face to recognize their emotional state.

[0242] "Database" refers to a system for centrally managing and storing data on a user's body shape and constitution, as well as data on their emotional state.

[0243] "Body shape and constitution data" refers to information intended to reflect the user's physical details, such as the user's height, weight, age, and gender.

[0244] "Diet Advice" refers to instructions or suggestions regarding optimal diet methods provided to a user based on the analysis results of a generative AI model.

[0245] "Speech recognition" refers to technology for analyzing a user's vocalizations to determine their emotional state.

[0246] "Facial recognition" refers to technology that analyzes a user's facial expressions to determine their emotional state.

[0247] The "data transmission function" refers to a function for transmitting the body shape and constitution data entered by the user to the server.

[0248] This invention is a system that combines a personal diet application that uses a generative AI model to provide an optimal diet method based on the user's body shape and constitution data with an emotion engine that recognizes the user's emotions. Specific embodiments of the present invention are described below.

[0249] User Data Entry

[0250] The user first logs in to the application and enters their body shape and constitution data, including height, weight, age, gender, etc. These data are necessary to reflect the user's physical details. The terminal provides the user with an easy-to-understand input form and prompts them to enter data. After the user completes the input, they press the submit button and the data is sent to the server.

[0251] Data transmission and storage

[0252] The terminal sends the data entered by the user to the server. This transmission is performed using a POST request. The server receives the POST request, analyzes the request contents, and obtains the data entered by the user. The obtained data is saved in a database, and the body shape and constitution data for each user are managed in a unified manner.

[0253] Emotion engine recognizes emotional states

[0254] The server is equipped with an emotion engine that analyzes the user's voice and facial data to recognize their emotional state. It uses voice and facial recognition technology to determine the user's emotional state. This information is stored in a database for later analysis.

[0255] Diet advice generation using generative AI models

[0256] The server retrieves the necessary user data from the database and inputs it into the generative AI model. The generative AI model has learned successful diet methods from users with similar body types and constitutions in the past, and generates optimal diet advice using new user data as input. For example, the generated advice might be specific, such as "jog three times a week as a fat-burning exercise."

[0257] Adjusting diet advice

[0258] The server adjusts the diet advice generated based on the analysis results of the emotion engine. For example, if the user is feeling stressed, advice to reduce stress will be added. Specific advice such as "Meditate for 10 minutes a day to reduce stress" may be added.

[0259] Providing diet advice

[0260] The adjusted diet advice is sent back from the server to the terminal, which displays the received advice data to the user, who can then directly confirm the displayed advice.

[0261] Examples of specific examples and prompts

[0262] For example, if a user enters the following data:

[0263] Height: 170 cm

[0264] Weight: 70 kg

[0265] Age: 30

[0266] Gender: Male

[0267] This data is sent to a server, where it is analyzed and the following diet advice is presented:

[0268] Jogging three times a week as a fat-burning exercise

[0269] A balanced diet centered around vegetables

[0270] Meditation techniques for relaxation

[0271] Example prompt sentence:

[0272] User data: Height 170 cm, Weight 70 kg, Age 30, Gender Male

[0273] User's emotional state: stressed

[0274] Generate optimal diet advice based on generative AI models and adjust based on emotional state.

[0275] In this way, the present invention realizes a system that combines a generative AI model and an emotion engine to provide optimal diet advice based on the user's body shape and constitution data. Personalized advice that takes into account the user's emotional state improves user satisfaction.

[0276] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0277] Step 1:

[0278] A user logs in to the application. After logging in, the user enters their body shape and physical constitution data, such as height, weight, age, and gender. The terminal provides a form for entering this data, and allows the user to press a submit button after completing the input.

[0279] Input: Data such as height, weight, age, and gender

[0280] Output: User-entered dataset

[0281] Specific action: The user enters "height 170cm" and presses the "Send" button.

[0282] Step 2:

[0283] The terminal sends the data entered by the user to the server using a POST request.

[0284] Input: A user-entered dataset

[0285] Output: Data sent to the server as a POST request

[0286] Specific operation: The device sends a POST request to the server containing data such as "User ID 123 - height 170cm".

[0287] Step 3:

[0288] The server analyzes the received POST request and extracts the user's input data, which is then stored in a database.

[0289] Input: User data sent as a POST request

[0290] Output: User data stored in the database

[0291] Specific operation: The server saves "User ID 123 - height 170cm, weight 70kg, age 30, gender male" in the database.

[0292] Step 4:

[0293] The emotion engine built into the server analyzes the user's voice and facial data to recognize their emotional state. It uses voice and facial recognition technology to determine the user's emotions and stores the information in a database.

[0294] Input: User voice and facial data

[0295] Output: Recognized emotional state data

[0296] Specific operation: The emotion engine recognizes "User ID 123 - Emotional state: Stress" and saves it in the database.

[0297] Step 5:

[0298] The server retrieves the necessary user data from the database and inputs it into the generative AI model, which learns from past success stories and generates optimal diet advice based on the user data.

[0299] Input: User data retrieved from the database

[0300] Output: Generated diet advice

[0301] Specific operation: The generative AI model generates advice such as "Jogging three times a week as a fat-burning exercise."

[0302] Step 6:

[0303] The server adjusts the diet advice generated based on the analysis results of the emotion engine, and can add advice for stress reduction depending on the emotional state.

[0304] Input: Generated diet advice and analysis results of the emotion engine

[0305] Output: Tailored diet advice

[0306] Specific behavior: An adjustment such as "User ID 123 - Add 10 minutes of meditation per day to reduce stress" will be made.

[0307] Step 7:

[0308] The server returns the adjusted diet advice to the terminal.

[0309] The terminal displays the received advice data to the user, so that the user can easily view it.

[0310] Enter: tailored diet advice

[0311] Output: Diet advice displayed to the user

[0312] Specific actions: The device will display a message such as "Jogging three times a week to burn fat and meditating for 10 minutes a day to reduce stress."

[0313] (Application example 2)

[0314] 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."

[0315] Conventional personalized diet applications provide optimal diet methods based on the user's body shape and physical constitution data, but because they do not take into account the user's current emotional state, users may become stressed or lose motivation, leading to problems with not being able to continue the diet. Furthermore, real-time feedback and individualized support are insufficient, making it difficult to improve user satisfaction.

[0316] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0317] In this invention, the server includes means for a user to input body shape and constitution data, means for receiving the input data and storing the input data in a database, means for analyzing the stored data using a generative AI model and comparing it with past success cases to generate diet advice, means for displaying the generated advice on a user device, means for adjusting the advice based on the user's emotional state, means for analyzing voice and face to recognize the emotional state, and means for storing the analyzed emotional state data in a database. This allows for the provision of specific and personalized diet advice tailored to the user's current situation and further enables individual responses based on the emotional state.

[0318] "User device" is a general term for electronic devices that users use to operate applications, including smartphones, tablets, smart glasses, etc.

[0319] A "database" is a system for efficiently storing, managing, and retrieving data such as a user's body shape, constitution, and emotional state.

[0320] A "generative AI model" is an artificial intelligence system that analyzes new data based on past success stories and generates appropriate diet advice.

[0321] The "emotion engine" is a system that analyzes the user's emotional state based on voice and facial information and recognizes that state.

[0322] "Preprocessing" refers to the initial data processing used to prepare the format and quality of the data to be analyzed by the generative AI model.

[0323] "Individualized" refers to providing advice and feedback that is customized to the user's individual characteristics, such as their current body shape, constitution, and emotional state.

[0324] "Real-time feedback" refers to responses and advice provided immediately to a user's actions or state.

[0325] The system embodying the present invention recognizes a user's body shape and constitution data, as well as their emotional state, in real time and provides diet advice. This system is composed of a user device, a server, a database, a generative AI model, and an emotion engine.

[0326] System Configuration

[0327] 1. User Devices

[0328] The user wears the smart glasses and inputs their body shape and physical characteristics. The smart glasses have a built-in camera that captures the user's face in real time.

[0329] The smart glasses display a form for inputting body shape and physical characteristics data, in which the user inputs data such as height, weight, age, and gender.

[0330] Additionally, data is collected through the smart glasses' camera and microphone to recognize the user's emotional state.

[0331] 2. Server

[0332] Body shape and constitution data and emotional state data transmitted from the user device are received.

[0333] The received data is stored in a database, which centrally manages this data.

[0334] 3. Database

[0335] Stores data on each user's body shape, constitution, and emotional state.

[0336] Data will be acquired as needed and used for analysis.

[0337] 4. Generative AI Models

[0338] The system analyzes user data obtained from the database and generates optimal diet advice based on past success stories.

[0339] The generated diet advice is adjusted based on the emotional state data analyzed by the emotion engine.

[0340] 5. Emotion Engine

[0341] It analyzes data collected by the camera and microphone of the user's device to recognize the user's emotional state.

[0342] The recognized emotion data is stored in a database.

[0343] Hardware and Software

[0344] The hardware and software used includes:

[0345] Smart glasses (with built-in camera and microphone)

[0346] Server (connected to database)

[0347] Database management system (MySQL, etc.)

[0348] Generative AI model (AI model that learns from past success stories)

[0349] Emotion Engine (EmotionRecognition Library)

[0350] Specific examples

[0351] Suppose a user is wearing smart glasses while training on a treadmill. The glasses' camera captures facial expressions in real time, and an emotion engine detects stress levels. This information is sent to a server and stored in a database. The generative AI model provides real-time advice to ease up on the training based on the user's body shape and stress level data. Feedback such as "Your current pace is appropriate. Take a few minutes to relax and take some deep breaths" appears on the smart glasses' display.

[0352] Prompt Sentence Examples

[0353] "I'm a 170cm tall, 70kg, 30-year-old male who is under stress. What is the best fitness advice?"

[0354] This allows for specific and personalized diet and fitness advice tailored to the user's current situation, and individualized responses based on emotional state.

[0355] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0356] Step 1:

[0357] The user puts on the smart glasses and inputs their body shape and constitution data.

[0358] Input: The user uses the input form on the smart glasses to enter data such as height, weight, age, and gender.

[0359] Data processing: The entered data is cached locally.

[0360] Output: Body shape and constitution data is input, and the terminal prepares to send it to the server.

[0361] Step 2:

[0362] The smart glasses' camera and microphone collect data on the user's emotional state.

[0363] Input: Camera video data and microphone audio data.

[0364] Data processing: Video data is analyzed using facial recognition software, and audio data is processed using audio analysis software.

[0365] Output: The generated emotional state data is sent to the device.

[0366] Step 3:

[0367] The terminal transmits the body shape and constitution data and the emotional state data to the server.

[0368] Input: Body shape and constitution data, emotional state data.

[0369] Data Calculation: The terminal organizes the data into a single JSON document.

[0370] Output: The constructed JSON document is sent to the server.

[0371] Step 4:

[0372] The server analyzes the received data and stores it in a database.

[0373] Input: A JSON document containing body shape and constitution data, and emotional state data.

[0374] Data Transformation: Data is deserialized and mapped to the appropriate fields to be saved in the database.

[0375] Output: User data is saved in the database.

[0376] Step 5:

[0377] The server retrieves the necessary data from the database and analyzes it using a generative AI model.

[0378] Input: User's body shape and constitution data.

[0379] Data calculation: The generative AI model compares input data with past success cases and generates optimal diet advice.

[0380] Output: The generated diet advice.

[0381] Step 6:

[0382] The server adjusts the generated diet advice using an emotion engine.

[0383] Input: diet advice data, emotional state data.

[0384] Data calculation: Advice content is adjusted according to emotional state (for example, adding relaxation techniques when stressed).

[0385] Output: Tailored diet advice.

[0386] Step 7:

[0387] The server sends the final diet advice back to the user device.

[0388] Enter: tailored diet advice.

[0389] Data Processing: Advice data is serialized into a format that can be displayed on the user device.

[0390] Output: The advice data is sent to the user device.

[0391] Step 8:

[0392] The user device displays the received advice on the display of the smart glasses.

[0393] Enter: tailored diet advice.

[0394] Data calculation: The rendering process is carried out to display the advice in a visually appealing way.

[0395] Output: The user can see the diet advice on the smart glasses.

[0396] 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.

[0397] 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.

[0398] 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.

[0399] [Second embodiment]

[0400] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0401] 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.

[0402] 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).

[0403] 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.

[0404] 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.

[0405] 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).

[0406] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0407] 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.

[0408] 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.

[0409] 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.

[0410] 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.

[0411] 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."

[0412] The present invention is a personal diet application that uses a generative AI model to provide optimal dieting methods based on a user's body shape and constitution data. The system includes a series of steps to receive data entered by the user, analyze it, and generate optimal diet advice.

[0413] First, users log in to the application and enter their skeletal and physical data, including height, weight, age, and gender, which are designed to reflect the user's physical details.

[0414] Next, the terminal receives the data entered by the user and sends it to the server. A data entry form is displayed on the terminal, and when the user enters data and presses the submit button, the terminal sends the data to the server as a POST request. The server saves the received data in a database. The database centrally manages body shape and constitution data for each user and retains the data for use in subsequent analysis.

[0415] The server retrieves user data from the database and passes it to the generative AI model for analysis. The generative AI model has learned successful diet methods for users with similar body types and constitutions in the past, and generates optimal diet advice using new user data as input. Based on the results of this analysis, the server constructs advice on the optimal diet method for the user.

[0416] The server then returns the generated advice to the user's device. The device then displays the advice data received from the server so that the user can easily view it. For example, if a diet method that combines both "fat-burning exercise" and "balanced diet intake" is recommended to the user, the user can check the detailed steps and precautions within the application.

[0417] As a concrete example, suppose a user enters data such as height 170 cm, weight 75 kg, and age 30. This data is sent to the server, which stores it in a database. The stored data is analyzed by a generative AI model, which generates advice such as "exercise aerobically three times a week and limit calorie intake to 1800 kcal" based on past success stories of people with a similar body shape and constitution. The server then sends this advice back to the user's device and displays it on the user's device. The user can then follow this advice and put the specific diet plan into practice.

[0418] In this way, the present invention provides a system that helps users achieve their diet goals in an efficient and healthy manner.

[0419] The processing flow will be explained below.

[0420] Step 1:

[0421] User

[0422] Users log in to the application and enter their skeletal and physical data (height, weight, age, etc.).

[0423] Step 2:

[0424] Terminal

[0425] The terminal provides the user with an input form and prompts them to enter data.

[0426] Provide a button (submit button) for users to submit input data.

[0427] Step 3:

[0428] Terminal

[0429] When the user presses the send button, the terminal sends the input data to the server as a POST request.

[0430] Step 4:

[0431] server

[0432] The server receives the POST request, analyzes the contents of the request, and obtains the user's input data.

[0433] Step 5:

[0434] server

[0435] The server stores the retrieved user data in a database.

[0436] Step 6:

[0437] server

[0438] The server retrieves the necessary user data from the database and passes it to the generative AI model for analysis.

[0439] Step 7:

[0440] server

[0441] The generative AI model analyzes user data based on past success stories and generates optimal diet advice.

[0442] Step 8:

[0443] server

[0444] The server sends the generated diet advice back to the user device.

[0445] Step 9:

[0446] Terminal

[0447] The terminal receives the advice data from the server and displays it in a format that is easy for the user to view.

[0448] Step 10:

[0449] User

[0450] The user checks the displayed diet advice and puts into practice the specific diet plan.

[0451] Example 1

[0452] 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."

[0453] Conventional diet support systems have had difficulty providing personalized diet advice based on the user's body shape and constitution. In particular, they lacked the means to generate individually optimized advice in real time based on body shape and constitution data, forcing users to follow general advice, resulting in low success rates for dieting. There is a need to solve this problem and provide more accurate personalized diet advice to efficiently support users in maintaining and improving their health.

[0454] 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.

[0455] In this invention, the server includes a means for sending data entered by the user as an HTTP POST request, a means for storing the data received by the server in a database via a query, a means for sending an analysis request to the generative AI model using a prompt sentence, and a means for returning the generated advice in JSON format, thereby enabling the generation and provision of highly accurate personalized diet advice in real time based on the user's body shape and constitution data.

[0456] "User" refers to an individual who uses the personal diet application to input their own body shape and constitution data and receive diet advice.

[0457] The "server" is a computer system that has the function of receiving data sent by users, storing it in a database, analyzing the data using a generative AI model, generating optimal diet advice, and delivering it to the user's device.

[0458] "Terminal" refers to a device used by a user to input body shape and constitution data, and includes smartphones, tablets, PCs, etc.

[0459] A "database" is a system for centrally managing and storing each user's body shape and constitution data, and includes relational databases such as MySQL and PostgreSQL.

[0460] A "generative AI model" is an artificial intelligence model that learns from past body shape and constitution data and successful diet cases, and analyzes new data to generate optimal diet advice.

[0461] A "prompt sentence" is an instruction sentence used when sending an analysis request to a generative AI model, and is a sentence that instructs the model to generate diet advice based on the user's specific body shape and constitution data.

[0462] An "HTTP POST request" is a communication method for sending data entered by a user to a server, and is a protocol for sending data in JSON format or other formats.

[0463] The "JSON format" is a lightweight text-based data format used for data exchange, and is primarily composed of key-value pairs.

[0464] The present invention is a personal diet application that uses a generative AI model to provide optimal diet advice based on a user's body shape and constitution data. The system includes a series of steps to receive data entered by the user, analyze it, and generate optimal diet advice.

[0465] First, the user logs in to the application using a dedicated device (smartphone, tablet, PC) and enters their body shape and constitution data. The input data includes height, weight, age, gender, etc. This input form is designed to allow the user to easily enter the required information. For example, suppose a user enters data such as height 170 cm, weight 75 kg, and age 30.

[0466] Next, the device sends the data entered by the user to the server. The data is sent in JSON format using an HTTP POST request. Specifically, the data format is as follows:

[0467] json

[0468] {

[0469] "Height": 170,

[0470] "Weight": 75,

[0471] "Age": 30,

[0472] "Gender": "Male"

[0473] }

[0474] The server receives the data sent from the device and stores it in a database (MySQL, PostgreSQL, etc.). The database serves to centrally manage the body shape and constitution data for each user.

[0475] Based on the saved data, the server sends an analysis request to the generative AI model. For example, OpenAI's GPT-4 can be used as the generative AI model. The prompt statement input to the generative AI model is, "Please provide the optimal diet plan for a user who is 170 cm tall, weighs 75 kg, and is 30 years old." The generative AI model performs analysis based on this prompt statement and generates optimal diet advice.

[0476] For example, the generative AI model generates advice such as "Do aerobic exercise three times a week and limit your calorie intake to 1,800 kcal." The analysis results are then sent back to the server.

[0477] The server returns the diet advice received from the generative AI model to the user device (terminal) in JSON format. The terminal analyzes the advice data received from the server and displays it so that the user can easily view it. For example, the user's screen might display a specific plan and its details, such as "Perform aerobic exercise three times a week and keep calorie intake below 1800 kcal."

[0478] This allows users to implement an individually optimized diet plan based on their own body shape and constitution, and the system helps users achieve their diet goals efficiently and healthily.

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

[0480] Step 1:

[0481] The user logs in to the personal diet application on their device. After logging in, a form is displayed in which they can enter their body shape and constitution data, such as height, weight, age, and gender. The user enters this data into the form and presses the "Submit" button. The input here is assumed to be height 170cm, weight 75kg, age 30, and gender male.

[0482] Input: User data (height, weight, age, gender)

[0483] Output: Data sent from the device to the server

[0484] Specific operation: The user enters their height (170 cm), weight (75 kg), age (30 years old, etc.) into the input fields displayed on the screen of their smartphone or PC, and presses the "Send" button.

[0485] Step 2:

[0486] The terminal sends the data entered by the user to the server in JSON format as an HTTP POST request. The JSON data has the following format:

[0487] json

[0488] {

[0489] "Height": 170,

[0490] "Weight": 75,

[0491] "Age": 30,

[0492] "Gender": "Male"

[0493] }

[0494] Input: User data formatted in JSON format

[0495] Output: User data sent to the server

[0496] Specific operation: The terminal converts the information entered by the user into JSON format and sends it as an HTTP POST request.

[0497] Step 3:

[0498] The server receives the data sent from the device and stores it in a database (e.g., MySQL or PostgreSQL). The server analyzes the received data and stores it through appropriate database queries.

[0499] Input: Received user data (JSON format)

[0500] Output: User data stored in the database

[0501] Specific operation: The server receives the HTTP request, parses the JSON data, and stores it in a database, for example, by executing an INSERT query using an SQL statement.

[0502] Step 4:

[0503] The server retrieves the user data stored in the database and sends an analysis request to the generative AI model. The generative AI model (for example, OpenAI's GPT-4) is asked to perform the analysis using a prompt sentence. An example of a prompt sentence is, "Please provide the optimal diet plan for a user who is 170 cm tall, weighs 75 kg, and is 30 years old."

[0504] Input: User data retrieved from the database

[0505] Output: The analysis request sent to the generative AI model

[0506] Specific operation: The server retrieves user data from the database and creates and sends the API requests required for the generative AI model.

[0507] Step 5:

[0508] The generative AI model generates optimal diet advice based on the prompt and input data, such as "exercise aerobic exercise three times a week and limit your calorie intake to 1,800 kcal."

[0509] Input: Parse request (prompt statement and user data)

[0510] Output: Generated diet advice

[0511] How it works: The generative AI model analyzes user data based on prompt statements and generates specific diet advice.

[0512] Step 6:

[0513] The server then sends diet advice obtained from the generative AI model back to the user device. The advice is sent from the server to the device in JSON format.

[0514] Input: Generated diet advice

[0515] Output: Advice data sent to the terminal

[0516] Specific operation: The server converts the generated advice into JSON format and sends it to the user device as an HTTP response.

[0517] Step 7:

[0518] The device analyzes the advice data received from the server and displays it on the screen. The user can then check the specific diet plan that has been generated on the application screen and implement it.

[0519] Input: Advice data received from the server

[0520] Output: Diet advice displayed to the user

[0521] Specific operation: The device decodes the received advice and displays it on the screen. For example, it displays a specific plan such as "Do aerobic exercise three times a week and keep your calorie intake below 1800 kcal."

[0522] (Application example 1)

[0523] 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."

[0524] Health management is an important issue for factory workers in harsh working environments. However, conventional health management methods are difficult to provide individualized support and are unable to provide efficient advice. For this reason, there is a need for a system that can provide optimal health advice based on the body shape and constitution of each individual employee.

[0525] 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.

[0526] In this invention, the server includes means for a user to input body shape and constitution data, means for receiving the input data and storing it in a database, means for analyzing the stored data using a generative AI model and comparing it with past success cases to generate diet advice, means for displaying the generated advice on a user device, means for providing health advice for managing the health of factory employees, and means for displaying the health advice on a factory robot or smart glasses. This makes it possible to provide optimal health advice to factory employees based on their individual body shape and constitution data.

[0527] The "means for the user to input data on body shape and constitution" is an interface for the user to input physical information such as their height, weight, age, and sex.

[0528] The "means for receiving the input data and storing it in a database" is a function for transmitting the physical information input by the user to a server and storing the data in a database.

[0529] A "generative AI model" is an artificial intelligence model that learns from past success stories and analyzes new user data to provide optimal diet and health management advice.

[0530] "Past success stories" are cases where optimal health advice based on body shape and constitution was successfully provided based on data collected to date.

[0531] "Diet advice" involves a generative AI model analyzing user data and providing optimal diet and exercise guidelines for managing the user's health.

[0532] "User Device" means the device used by the user to view the advice, such as a smartphone, tablet, or other display device.

[0533] "Means for providing health advice for health management" refers to means for providing guidelines on lifestyle, exercise, and diet necessary for maintaining and improving the health of factory employees.

[0534] "Means for displaying on factory robots and smart glasses" refers to a function that displays the generated health advice on robots and wearable devices used in factories, allowing employees to visually receive the advice.

[0535] This invention is designed as a system to support the health management of factory workers. First, the user, a factory worker, accesses an interface to input their own body shape and physical characteristics data (height, weight, age, gender, etc.). This is done using a form displayed on the display of the smart glasses or robot terminal. Once the user enters and submits the data, it is sent to the server and stored in a database.

[0536] The server retrieves the stored data and passes it to a generative AI model for analysis. The generative AI model, trained on past success stories, generates optimal health advice based on new user data. This generated advice is then sent by the server back to the user's device (smart glasses or factory robot) and displayed to the user.

[0537] As a concrete example, suppose a user enters the following data: height 170 cm, weight 75 kg, age 30, and gender: male. This data is sent to the server, which stores it in a database. As a result of analysis by the generative AI model, health management advice is generated, such as "do aerobic exercise three times a week for 30 minutes each time" and "limit daily calorie intake to 1800 kcal." This advice is sent back from the server to the user's device and displayed on the display of smart glasses or a factory robot.

[0538] This system makes it possible to provide optimal health advice based on individual body shapes and constitutions, and efficiently manage and improve the health of factory employees, which is expected to improve work efficiency and employee satisfaction.

[0539] An example prompt is:

[0540] "Based on the given user data (health profile: height 170cm, weight 75kg, age 30, gender: male), please provide advice on optimal lifestyle, exercise, and dietary habits to maintain physical health."

[0541] The hardware uses smart glasses and factory robots, and the software uses generative AI models, which can handle everything from data input to generating and displaying advice.

[0542] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0543] Step 1:

[0544] Users input their body shape and physical characteristics (height, weight, age, gender, etc.) using an interface displayed on the display of smart glasses or a factory robot, and the input data is temporarily stored in the device's memory.

[0545] Step 2:

[0546] The terminal sends the data entered by the user to the server. Specifically, the input data is sent to the server as an HTTP POST request. The input data is encoded in JSON format and sent.

[0547] Step 3:

[0548] The server stores the received data in a database. The server's receiving process extracts the data from the POST request and stores it in the database in the appropriate format. At this point, the user's body shape and constitution data are saved in the system.

[0549] Step 4:

[0550] The server retrieves user data from the database and performs preprocessing to pass it to the generative AI model. This preprocessing includes enhancing the data and removing unnecessary information. The preprocessed data is then formatted in a way that is suitable for the generative AI model.

[0551] Step 5:

[0552] The server inputs the preprocessed data into a generative AI model to generate optimal health advice. The generative AI model generates personalized advice based on the given prompt, referencing past success stories. This process results in specific exercise and dietary guidelines.

[0553] Step 6:

[0554] The server retrieves the generated advice and sends it to the user device as an HTTP response, returning the advice content encoded in JSON format.

[0555] Step 7:

[0556] The device decodes the advice data received from the server and displays it to the user. Specific health advice is then visually displayed on the display of smart glasses or a factory robot.

[0557] This allows users to receive optimal health advice based on their individual body shape and physical constitution data, and manage their health based on that advice.

[0558] 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.

[0559] The present invention is a system that combines a personal diet application that uses a generative AI model to provide an optimal diet method based on the user's body shape and constitution data with an emotion engine that recognizes the user's emotions. This system includes a series of steps: receiving user input data, analyzing it, generating optimal diet advice using the generative AI model, and further adjusting it according to the user's emotional state.

[0560] First, the user logs in to the application and enters their skeletal and physical data, including height, weight, age, and gender, which are designed to reflect the user's physical details. The terminal then provides the user with an input form and prompts them to enter the data. A button is also provided for the user to submit the data they have entered.

[0561] The device then sends the entered data to the server. The server receives the POST request, analyzes the request, and retrieves the user's input data. The retrieved data is then stored in a database. The database centrally manages each user's body shape and constitution data and retains the data for use in subsequent analyses.

[0562] Furthermore, the server is equipped with an emotion engine that analyzes the user's voice and face to recognize the user's current emotional state. Using voice and face recognition technology, it determines the user's emotional state and stores that information in a database.

[0563] The server then retrieves the necessary user data from the database and passes it to the generative AI model for analysis. The generative AI model has learned successful diet methods from users with similar body types and constitutions in the past, and generates optimal diet advice based on new user data. The generated advice is adjusted based on the analysis results of the emotion engine. For example, if the user is feeling stressed, the system can add advice to reduce stress.

[0564] The server then sends the adjusted diet advice back to the user device. The terminal then displays the advice data received from the server so that the user can easily view it. Specifically, the user may be recommended a diet method that combines both "fat-burning exercise" and "balanced diet intake," and additional advice such as "relaxation meditation techniques" may be displayed based on the user's emotional state.

[0565] In this way, the present invention provides a system that helps users achieve their diet goals efficiently and healthily. The combination of a generative AI model and an emotion engine provides highly accurate advice, contributing to increased user satisfaction. Furthermore, by taking the user's emotional state into account, more personalized support can be achieved.

[0566] The processing flow will be explained below.

[0567] Step 1:

[0568] User

[0569] Users log in to the application and enter their skeletal and physical data, such as height, weight, age, and gender.

[0570] Step 2:

[0571] Terminal

[0572] The terminal presents the user with an input form, prompting them to enter data, indicating which fields are required, and prompting them to press a submit button once the data is complete.

[0573] Step 3:

[0574] Terminal

[0575] When the user presses the send button, the device sends the entered data to the server as a POST request. HTTPS is used for data transmission to ensure security.

[0576] Step 4:

[0577] server

[0578] The server receives the POST request, analyzes the request, and retrieves the user's input data. The retrieved data is stored in a temporary variable.

[0579] Step 5:

[0580] server

[0581] The server stores the acquired user data in a database, which centrally manages each user's body shape and constitution data.

[0582] Step 6:

[0583] server

[0584] After the server has finished saving the user data, it starts the emotion engine, which analyzes the user's voice and face data to recognize the user's current emotional state.

[0585] Step 7:

[0586] Terminal

[0587] The device displays an interface to collect the user's voice and facial data for the emotion engine. The user provides the data using the camera and microphone.

[0588] Step 8:

[0589] server

[0590] The server receives and analyzes the voice and face data sent from the device, and the result of the analysis is the user's emotional state (e.g., stress or fatigue).

[0591] Step 9:

[0592] server

[0593] The server retrieves the necessary user data from the database and passes it to the generative AI model for analysis. The generative AI model generates optimal diet advice for each user based on past success stories.

[0594] Step 10:

[0595] server

[0596] The server combines the results of the emotion engine's analysis with those of the generative AI model to tailor advice based on the user's emotional state. For example, if a user is feeling stressed, it might also provide advice on how to relax.

[0597] Step 11:

[0598] server

[0599] The server then sends tailored advice back to the user's device, including specific exercise regimes, meal plans, relaxation techniques, and more.

[0600] Step 12:

[0601] Terminal

[0602] The device displays the received advice data and presents it in an easy-to-read format for easy user access. Each piece of advice is provided with a detailed explanation and implementation method.

[0603] Step 13:

[0604] User

[0605] Users can check the displayed diet advice and put it into practice. By incorporating the recommended exercise, diet, and relaxation methods into their daily lives, they can achieve their diet goals efficiently and healthily.

[0606] Example 2

[0607] 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."

[0608] Conventional personalized diet applications provide diet advice based on a user's body shape and constitution data, but they fail to take the user's emotional state into account. As a result, the advice provided may be ineffective when the user is stressed or in other emotional states. Therefore, there is a need for a system that recognizes a user's emotional state and adjusts diet advice based on that state.

[0609] 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.

[0610] In this invention, the server includes means for a user to input body shape and constitution data, means for receiving the input data and storing it in a database, means for analyzing the stored data using a generative AI model and comparing it with past success cases to generate diet advice, means for analyzing the user's voice and face to recognize their emotional state and adjusting the generated diet advice based on the emotional state, and means for displaying the adjusted diet advice on the user device, thereby enabling the provision of personalized diet advice that takes the user's emotional state into consideration.

[0611] "User device" refers to an electronic device used by a user, including a smartphone, tablet, personal computer, etc.

[0612] A "generative AI model" refers to an artificial intelligence model that learns from large amounts of data and generates optimal diet advice from the input data.

[0613] An "emotion engine" refers to a device or software that analyzes a user's voice and face to recognize their emotional state.

[0614] "Database" refers to a system for centrally managing and storing data on a user's body shape and constitution, as well as data on their emotional state.

[0615] "Body shape and constitution data" refers to information intended to reflect the user's physical details, such as the user's height, weight, age, and gender.

[0616] "Diet Advice" refers to instructions or suggestions regarding optimal diet methods provided to a user based on the analysis results of a generative AI model.

[0617] "Speech recognition" refers to technology for analyzing a user's vocalizations to determine their emotional state.

[0618] "Facial recognition" refers to technology that analyzes a user's facial expressions to determine their emotional state.

[0619] The "data transmission function" refers to a function for transmitting the body shape and constitution data entered by the user to the server.

[0620] This invention is a system that combines a personal diet application that uses a generative AI model to provide an optimal diet method based on the user's body shape and constitution data with an emotion engine that recognizes the user's emotions. Specific embodiments of the present invention are described below.

[0621] User Data Entry

[0622] The user first logs in to the application and enters their body shape and constitution data, including height, weight, age, gender, etc. These data are necessary to reflect the user's physical details. The terminal provides the user with an easy-to-understand input form and prompts them to enter data. After the user completes the input, they press the submit button and the data is sent to the server.

[0623] Data transmission and storage

[0624] The terminal sends the data entered by the user to the server. This transmission is performed using a POST request. The server receives the POST request, analyzes the request contents, and obtains the data entered by the user. The obtained data is saved in a database, and the body shape and constitution data for each user are managed in a unified manner.

[0625] Emotion engine recognizes emotional states

[0626] The server is equipped with an emotion engine that analyzes the user's voice and facial data to recognize their emotional state. It uses voice and facial recognition technology to determine the user's emotional state. This information is stored in a database for later analysis.

[0627] Diet advice generation using generative AI models

[0628] The server retrieves the necessary user data from the database and inputs it into the generative AI model. The generative AI model has learned successful diet methods from users with similar body types and constitutions in the past, and generates optimal diet advice using new user data as input. For example, the generated advice might be specific, such as "jog three times a week as a fat-burning exercise."

[0629] Adjusting diet advice

[0630] The server adjusts the diet advice generated based on the analysis results of the emotion engine. For example, if the user is feeling stressed, advice to reduce stress will be added. Specific advice such as "Meditate for 10 minutes a day to reduce stress" may be added.

[0631] Providing diet advice

[0632] The adjusted diet advice is sent back from the server to the terminal, which displays the received advice data to the user, who can then directly confirm the displayed advice.

[0633] Examples of specific examples and prompts

[0634] For example, if a user enters the following data:

[0635] Height: 170 cm

[0636] Weight: 70 kg

[0637] Age: 30

[0638] Gender: Male

[0639] This data is sent to a server, where it is analyzed and the following diet advice is presented:

[0640] Jogging three times a week as a fat-burning exercise

[0641] A balanced diet centered around vegetables

[0642] Meditation techniques for relaxation

[0643] Example prompt sentence:

[0644] User data: Height 170 cm, Weight 70 kg, Age 30, Gender Male

[0645] User's emotional state: stressed

[0646] Generate optimal diet advice based on generative AI models and adjust based on emotional state.

[0647] In this way, the present invention realizes a system that combines a generative AI model and an emotion engine to provide optimal diet advice based on the user's body shape and constitution data. Personalized advice that takes into account the user's emotional state improves user satisfaction.

[0648] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0649] Step 1:

[0650] A user logs in to the application. After logging in, the user enters their body shape and physical constitution data, such as height, weight, age, and gender. The terminal provides a form for entering this data, and allows the user to press a submit button after completing the input.

[0651] Input: Data such as height, weight, age, and gender

[0652] Output: User-entered dataset

[0653] Specific action: The user enters "height 170cm" and presses the "Send" button.

[0654] Step 2:

[0655] The terminal sends the data entered by the user to the server using a POST request.

[0656] Input: A user-entered dataset

[0657] Output: Data sent to the server as a POST request

[0658] Specific operation: The device sends a POST request to the server containing data such as "User ID 123 - height 170cm".

[0659] Step 3:

[0660] The server analyzes the received POST request and extracts the user's input data, which is then stored in a database.

[0661] Input: User data sent as a POST request

[0662] Output: User data stored in the database

[0663] Specific operation: The server saves "User ID 123 - height 170cm, weight 70kg, age 30, gender male" in the database.

[0664] Step 4:

[0665] The emotion engine built into the server analyzes the user's voice and facial data to recognize their emotional state. It uses voice and facial recognition technology to determine the user's emotions and stores the information in a database.

[0666] Input: User voice and facial data

[0667] Output: Recognized emotional state data

[0668] Specific operation: The emotion engine recognizes "User ID 123 - Emotional state: Stress" and saves it in the database.

[0669] Step 5:

[0670] The server retrieves the necessary user data from the database and inputs it into the generative AI model, which learns from past success stories and generates optimal diet advice based on the user data.

[0671] Input: User data retrieved from the database

[0672] Output: Generated diet advice

[0673] Specific operation: The generative AI model generates advice such as "Jogging three times a week as a fat-burning exercise."

[0674] Step 6:

[0675] The server adjusts the diet advice generated based on the analysis results of the emotion engine, and can add advice for stress reduction depending on the emotional state.

[0676] Input: Generated diet advice and analysis results of the emotion engine

[0677] Output: Tailored diet advice

[0678] Specific behavior: An adjustment such as "User ID 123 - Add 10 minutes of meditation per day to reduce stress" will be made.

[0679] Step 7:

[0680] The server returns the adjusted diet advice to the terminal.

[0681] The terminal displays the received advice data to the user, so that the user can easily view it.

[0682] Enter: tailored diet advice

[0683] Output: Diet advice displayed to the user

[0684] Specific actions: The device will display a message such as "Jogging three times a week to burn fat and meditating for 10 minutes a day to reduce stress."

[0685] (Application example 2)

[0686] 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."

[0687] Conventional personalized diet applications provide optimal diet methods based on the user's body shape and physical constitution data, but because they do not take into account the user's current emotional state, users may become stressed or lose motivation, leading to problems with not being able to continue the diet. Furthermore, real-time feedback and individualized support are insufficient, making it difficult to improve user satisfaction.

[0688] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0689] In this invention, the server includes means for a user to input body shape and constitution data, means for receiving the input data and storing the input data in a database, means for analyzing the stored data using a generative AI model and comparing it with past success cases to generate diet advice, means for displaying the generated advice on a user device, means for adjusting the advice based on the user's emotional state, means for analyzing voice and face to recognize the emotional state, and means for storing the analyzed emotional state data in a database. This allows for the provision of specific and personalized diet advice tailored to the user's current situation and further enables individual responses based on the emotional state.

[0690] "User device" is a general term for electronic devices that users use to operate applications, including smartphones, tablets, smart glasses, etc.

[0691] A "database" is a system for efficiently storing, managing, and retrieving data such as a user's body shape, constitution, and emotional state.

[0692] A "generative AI model" is an artificial intelligence system that analyzes new data based on past success stories and generates appropriate diet advice.

[0693] The "emotion engine" is a system that analyzes the user's emotional state based on voice and facial information and recognizes that state.

[0694] "Preprocessing" refers to the initial data processing used to prepare the format and quality of the data to be analyzed by the generative AI model.

[0695] "Individualized" refers to providing advice and feedback that is customized to the user's individual characteristics, such as their current body shape, constitution, and emotional state.

[0696] "Real-time feedback" refers to responses and advice provided immediately to a user's actions or state.

[0697] The system embodying the present invention recognizes a user's body shape and constitution data, as well as their emotional state, in real time and provides diet advice. This system is composed of a user device, a server, a database, a generative AI model, and an emotion engine.

[0698] System Configuration

[0699] 1. User Devices

[0700] The user wears the smart glasses and inputs their body shape and physical characteristics. The smart glasses have a built-in camera that captures the user's face in real time.

[0701] The smart glasses display a form for inputting body shape and physical characteristics data, in which the user inputs data such as height, weight, age, and gender.

[0702] Additionally, data is collected through the smart glasses' camera and microphone to recognize the user's emotional state.

[0703] 2. Server

[0704] Body shape and constitution data and emotional state data transmitted from the user device are received.

[0705] The received data is stored in a database, which centrally manages this data.

[0706] 3. Database

[0707] Stores data on each user's body shape, constitution, and emotional state.

[0708] Data will be acquired as needed and used for analysis.

[0709] 4. Generative AI Models

[0710] The system analyzes user data obtained from the database and generates optimal diet advice based on past success stories.

[0711] The generated diet advice is adjusted based on the emotional state data analyzed by the emotion engine.

[0712] 5. Emotion Engine

[0713] It analyzes data collected by the camera and microphone of the user's device to recognize the user's emotional state.

[0714] The recognized emotion data is stored in a database.

[0715] Hardware and Software

[0716] The hardware and software used includes:

[0717] Smart glasses (with built-in camera and microphone)

[0718] Server (connected to database)

[0719] Database management system (MySQL, etc.)

[0720] Generative AI model (AI model that learns from past success stories)

[0721] Emotion Engine (EmotionRecognition Library)

[0722] Specific examples

[0723] Suppose a user is wearing smart glasses while training on a treadmill. The glasses' camera captures facial expressions in real time, and an emotion engine detects stress levels. This information is sent to a server and stored in a database. The generative AI model provides real-time advice to ease up on the training based on the user's body shape and stress level data. Feedback such as "Your current pace is appropriate. Take a few minutes to relax and take some deep breaths" appears on the smart glasses' display.

[0724] Prompt Sentence Examples

[0725] "I'm a 170cm tall, 70kg, 30-year-old male who is under stress. What is the best fitness advice?"

[0726] This allows for specific and personalized diet and fitness advice tailored to the user's current situation, and individualized responses based on emotional state.

[0727] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0728] Step 1:

[0729] The user puts on the smart glasses and inputs their body shape and constitution data.

[0730] Input: The user uses the input form on the smart glasses to enter data such as height, weight, age, and gender.

[0731] Data processing: The entered data is cached locally.

[0732] Output: Body shape and constitution data is input, and the terminal prepares to send it to the server.

[0733] Step 2:

[0734] The smart glasses' camera and microphone collect data on the user's emotional state.

[0735] Input: Camera video data and microphone audio data.

[0736] Data processing: Video data is analyzed using facial recognition software, and audio data is processed using audio analysis software.

[0737] Output: The generated emotional state data is sent to the device.

[0738] Step 3:

[0739] The terminal transmits the body shape and constitution data and the emotional state data to the server.

[0740] Input: Body shape and constitution data, emotional state data.

[0741] Data Calculation: The terminal organizes the data into a single JSON document.

[0742] Output: The constructed JSON document is sent to the server.

[0743] Step 4:

[0744] The server analyzes the received data and stores it in a database.

[0745] Input: A JSON document containing body shape and constitution data, and emotional state data.

[0746] Data Transformation: Data is deserialized and mapped to the appropriate fields to be saved in the database.

[0747] Output: User data is saved in the database.

[0748] Step 5:

[0749] The server retrieves the necessary data from the database and analyzes it using a generative AI model.

[0750] Input: User's body shape and constitution data.

[0751] Data calculation: The generative AI model compares input data with past success cases and generates optimal diet advice.

[0752] Output: The generated diet advice.

[0753] Step 6:

[0754] The server adjusts the generated diet advice using an emotion engine.

[0755] Input: diet advice data, emotional state data.

[0756] Data calculation: Advice content is adjusted according to emotional state (for example, adding relaxation techniques when stressed).

[0757] Output: Tailored diet advice.

[0758] Step 7:

[0759] The server sends the final diet advice back to the user device.

[0760] Enter: tailored diet advice.

[0761] Data Processing: Advice data is serialized into a format that can be displayed on the user device.

[0762] Output: The advice data is sent to the user device.

[0763] Step 8:

[0764] The user device displays the received advice on the display of the smart glasses.

[0765] Enter: tailored diet advice.

[0766] Data calculation: The rendering process is carried out to display the advice in a visually appealing way.

[0767] Output: The user can see the diet advice on the smart glasses.

[0768] 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.

[0769] 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.

[0770] 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.

[0771] [Third embodiment]

[0772] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0773] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0774] 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).

[0775] 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.

[0776] 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.

[0777] 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).

[0778] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0779] 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.

[0780] 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.

[0781] 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.

[0782] 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.

[0783] 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."

[0784] The present invention is a personal diet application that uses a generative AI model to provide optimal dieting methods based on a user's body shape and constitution data. The system includes a series of steps to receive data entered by the user, analyze it, and generate optimal diet advice.

[0785] First, users log in to the application and enter their skeletal and physical data, including height, weight, age, and gender, which are designed to reflect the user's physical details.

[0786] Next, the terminal receives the data entered by the user and sends it to the server. A data entry form is displayed on the terminal, and when the user enters data and presses the submit button, the terminal sends the data to the server as a POST request. The server saves the received data in a database. The database centrally manages body shape and constitution data for each user and retains the data for use in subsequent analysis.

[0787] The server retrieves user data from the database and passes it to the generative AI model for analysis. The generative AI model has learned successful diet methods for users with similar body types and constitutions in the past, and generates optimal diet advice using new user data as input. Based on the results of this analysis, the server constructs advice on the optimal diet method for the user.

[0788] The server then returns the generated advice to the user's device. The device then displays the advice data received from the server so that the user can easily view it. For example, if a diet method that combines both "fat-burning exercise" and "balanced diet intake" is recommended to the user, the user can check the detailed steps and precautions within the application.

[0789] As a concrete example, suppose a user enters data such as height 170 cm, weight 75 kg, and age 30. This data is sent to the server, which stores it in a database. The stored data is analyzed by a generative AI model, which generates advice such as "exercise aerobically three times a week and limit calorie intake to 1800 kcal" based on past success stories of people with a similar body shape and constitution. The server then sends this advice back to the user's device and displays it on the user's device. The user can then follow this advice and put the specific diet plan into practice.

[0790] In this way, the present invention provides a system that helps users achieve their diet goals in an efficient and healthy manner.

[0791] The processing flow will be explained below.

[0792] Step 1:

[0793] User

[0794] Users log in to the application and enter their skeletal and physical data (height, weight, age, etc.).

[0795] Step 2:

[0796] Terminal

[0797] The terminal provides the user with an input form and prompts them to enter data.

[0798] Provide a button (submit button) for users to submit input data.

[0799] Step 3:

[0800] Terminal

[0801] When the user presses the send button, the terminal sends the input data to the server as a POST request.

[0802] Step 4:

[0803] server

[0804] The server receives the POST request, analyzes the contents of the request, and obtains the user's input data.

[0805] Step 5:

[0806] server

[0807] The server stores the retrieved user data in a database.

[0808] Step 6:

[0809] server

[0810] The server retrieves the necessary user data from the database and passes it to the generative AI model for analysis.

[0811] Step 7:

[0812] server

[0813] The generative AI model analyzes user data based on past success stories and generates optimal diet advice.

[0814] Step 8:

[0815] server

[0816] The server sends the generated diet advice back to the user device.

[0817] Step 9:

[0818] Terminal

[0819] The terminal receives the advice data from the server and displays it in a format that is easy for the user to view.

[0820] Step 10:

[0821] User

[0822] The user checks the displayed diet advice and puts into practice the specific diet plan.

[0823] Example 1

[0824] 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."

[0825] Conventional diet support systems have had difficulty providing personalized diet advice based on the user's body shape and constitution. In particular, they lacked the means to generate individually optimized advice in real time based on body shape and constitution data, forcing users to follow general advice, resulting in low success rates for dieting. There is a need to solve this problem and provide more accurate personalized diet advice to efficiently support users in maintaining and improving their health.

[0826] 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.

[0827] In this invention, the server includes a means for sending data entered by the user as an HTTP POST request, a means for storing the data received by the server in a database via a query, a means for sending an analysis request to the generative AI model using a prompt sentence, and a means for returning the generated advice in JSON format, thereby enabling the generation and provision of highly accurate personalized diet advice in real time based on the user's body shape and constitution data.

[0828] "User" refers to an individual who uses the personal diet application to input their own body shape and constitution data and receive diet advice.

[0829] The "server" is a computer system that has the function of receiving data sent by users, storing it in a database, analyzing the data using a generative AI model, generating optimal diet advice, and delivering it to the user's device.

[0830] "Terminal" refers to a device used by a user to input body shape and constitution data, and includes smartphones, tablets, PCs, etc.

[0831] A "database" is a system for centrally managing and storing each user's body shape and constitution data, and includes relational databases such as MySQL and PostgreSQL.

[0832] A "generative AI model" is an artificial intelligence model that learns from past body shape and constitution data and successful diet cases, and analyzes new data to generate optimal diet advice.

[0833] A "prompt sentence" is an instruction sentence used when sending an analysis request to a generative AI model, and is a sentence that instructs the model to generate diet advice based on the user's specific body shape and constitution data.

[0834] An "HTTP POST request" is a communication method for sending data entered by a user to a server, and is a protocol for sending data in JSON format or other formats.

[0835] The "JSON format" is a lightweight text-based data format used for data exchange, and is primarily composed of key-value pairs.

[0836] The present invention is a personal diet application that uses a generative AI model to provide optimal diet advice based on a user's body shape and constitution data. The system includes a series of steps to receive data entered by the user, analyze it, and generate optimal diet advice.

[0837] First, the user logs in to the application using a dedicated device (smartphone, tablet, PC) and enters their body shape and constitution data. The input data includes height, weight, age, gender, etc. This input form is designed to allow the user to easily enter the required information. For example, suppose a user enters data such as height 170 cm, weight 75 kg, and age 30.

[0838] Next, the device sends the data entered by the user to the server. The data is sent in JSON format using an HTTP POST request. Specifically, the data format is as follows:

[0839] json

[0840] {

[0841] "Height": 170,

[0842] "Weight": 75,

[0843] "Age": 30,

[0844] "Gender": "Male"

[0845] }

[0846] The server receives the data sent from the device and stores it in a database (MySQL, PostgreSQL, etc.). The database serves to centrally manage the body shape and constitution data for each user.

[0847] Based on the saved data, the server sends an analysis request to the generative AI model. For example, OpenAI's GPT-4 can be used as the generative AI model. The prompt statement input to the generative AI model is, "Please provide the optimal diet plan for a user who is 170 cm tall, weighs 75 kg, and is 30 years old." The generative AI model performs analysis based on this prompt statement and generates optimal diet advice.

[0848] For example, the generative AI model generates advice such as "Do aerobic exercise three times a week and limit your calorie intake to 1,800 kcal." The analysis results are then sent back to the server.

[0849] The server returns the diet advice received from the generative AI model to the user device (terminal) in JSON format. The terminal analyzes the advice data received from the server and displays it so that the user can easily view it. For example, the user's screen might display a specific plan and its details, such as "Perform aerobic exercise three times a week and keep calorie intake below 1800 kcal."

[0850] This allows users to implement an individually optimized diet plan based on their own body shape and constitution, and the system helps users achieve their diet goals efficiently and healthily.

[0851] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0852] Step 1:

[0853] The user logs in to the personal diet application on their device. After logging in, a form is displayed in which they can enter their body shape and constitution data, such as height, weight, age, and gender. The user enters this data into the form and presses the "Submit" button. The input here is assumed to be height 170cm, weight 75kg, age 30, and gender male.

[0854] Input: User data (height, weight, age, gender)

[0855] Output: Data sent from the device to the server

[0856] Specific operation: The user enters their height (170 cm), weight (75 kg), age (30 years old, etc.) into the input fields displayed on the screen of their smartphone or PC, and presses the "Send" button.

[0857] Step 2:

[0858] The terminal sends the data entered by the user to the server in JSON format as an HTTP POST request. The JSON data has the following format:

[0859] json

[0860] {

[0861] "Height": 170,

[0862] "Weight": 75,

[0863] "Age": 30,

[0864] "Gender": "Male"

[0865] }

[0866] Input: User data formatted in JSON format

[0867] Output: User data sent to the server

[0868] Specific operation: The terminal converts the information entered by the user into JSON format and sends it as an HTTP POST request.

[0869] Step 3:

[0870] The server receives the data sent from the device and stores it in a database (e.g., MySQL or PostgreSQL). The server analyzes the received data and stores it through appropriate database queries.

[0871] Input: Received user data (JSON format)

[0872] Output: User data stored in the database

[0873] Specific operation: The server receives the HTTP request, parses the JSON data, and stores it in a database, for example, by executing an INSERT query using an SQL statement.

[0874] Step 4:

[0875] The server retrieves the user data stored in the database and sends an analysis request to the generative AI model. The generative AI model (for example, OpenAI's GPT-4) is asked to perform the analysis using a prompt sentence. An example of a prompt sentence is, "Please provide the optimal diet plan for a user who is 170 cm tall, weighs 75 kg, and is 30 years old."

[0876] Input: User data retrieved from the database

[0877] Output: The analysis request sent to the generative AI model

[0878] Specific operation: The server retrieves user data from the database and creates and sends the API requests required for the generative AI model.

[0879] Step 5:

[0880] The generative AI model generates optimal diet advice based on the prompt and input data, such as "exercise aerobic exercise three times a week and limit your calorie intake to 1,800 kcal."

[0881] Input: Parse request (prompt statement and user data)

[0882] Output: Generated diet advice

[0883] How it works: The generative AI model analyzes user data based on prompt statements and generates specific diet advice.

[0884] Step 6:

[0885] The server then sends diet advice obtained from the generative AI model back to the user device. The advice is sent from the server to the device in JSON format.

[0886] Input: Generated diet advice

[0887] Output: Advice data sent to the terminal

[0888] Specific operation: The server converts the generated advice into JSON format and sends it to the user device as an HTTP response.

[0889] Step 7:

[0890] The device analyzes the advice data received from the server and displays it on the screen. The user can then check the specific diet plan that has been generated on the application screen and implement it.

[0891] Input: Advice data received from the server

[0892] Output: Diet advice displayed to the user

[0893] Specific operation: The device decodes the received advice and displays it on the screen. For example, it displays a specific plan such as "Do aerobic exercise three times a week and keep your calorie intake below 1800 kcal."

[0894] (Application example 1)

[0895] 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."

[0896] Health management is an important issue for factory workers in harsh working environments. However, conventional health management methods are difficult to provide individualized support and are unable to provide efficient advice. For this reason, there is a need for a system that can provide optimal health advice based on the body shape and constitution of each individual employee.

[0897] 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.

[0898] In this invention, the server includes means for a user to input body shape and constitution data, means for receiving the input data and storing it in a database, means for analyzing the stored data using a generative AI model and comparing it with past success cases to generate diet advice, means for displaying the generated advice on a user device, means for providing health advice for managing the health of factory employees, and means for displaying the health advice on a factory robot or smart glasses. This makes it possible to provide optimal health advice to factory employees based on their individual body shape and constitution data.

[0899] The "means for the user to input data on body shape and constitution" is an interface for the user to input physical information such as their height, weight, age, and sex.

[0900] The "means for receiving the input data and storing it in a database" is a function for transmitting the physical information input by the user to a server and storing the data in a database.

[0901] A "generative AI model" is an artificial intelligence model that learns from past success stories and analyzes new user data to provide optimal diet and health management advice.

[0902] "Past success stories" are cases where optimal health advice based on body shape and constitution was successfully provided based on data collected to date.

[0903] "Diet advice" involves a generative AI model analyzing user data and providing optimal diet and exercise guidelines for managing the user's health.

[0904] "User Device" means the device used by the user to view the advice, such as a smartphone, tablet, or other display device.

[0905] "Means for providing health advice for health management" refers to means for providing guidelines on lifestyle, exercise, and diet necessary for maintaining and improving the health of factory employees.

[0906] "Means for displaying on factory robots and smart glasses" refers to a function that displays the generated health advice on robots and wearable devices used in factories, allowing employees to visually receive the advice.

[0907] This invention is designed as a system to support the health management of factory workers. First, the user, a factory worker, accesses an interface to input their own body shape and physical characteristics data (height, weight, age, gender, etc.). This is done using a form displayed on the display of the smart glasses or robot terminal. Once the user enters and submits the data, it is sent to the server and stored in a database.

[0908] The server retrieves the stored data and passes it to a generative AI model for analysis. The generative AI model, trained on past success stories, generates optimal health advice based on new user data. This generated advice is then sent by the server back to the user's device (smart glasses or factory robot) and displayed to the user.

[0909] As a concrete example, suppose a user enters the following data: height 170 cm, weight 75 kg, age 30, and gender: male. This data is sent to the server, which stores it in a database. As a result of analysis by the generative AI model, health management advice is generated, such as "do aerobic exercise three times a week for 30 minutes each time" and "limit daily calorie intake to 1800 kcal." This advice is sent back from the server to the user's device and displayed on the display of smart glasses or a factory robot.

[0910] This system makes it possible to provide optimal health advice based on individual body shapes and constitutions, and efficiently manage and improve the health of factory employees, which is expected to improve work efficiency and employee satisfaction.

[0911] An example prompt is:

[0912] "Based on the given user data (health profile: height 170cm, weight 75kg, age 30, gender: male), please provide advice on optimal lifestyle, exercise, and dietary habits to maintain physical health."

[0913] The hardware uses smart glasses and factory robots, and the software uses generative AI models, which can handle everything from data input to generating and displaying advice.

[0914] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0915] Step 1:

[0916] Users input their body shape and physical characteristics (height, weight, age, gender, etc.) using an interface displayed on the display of smart glasses or a factory robot, and the input data is temporarily stored in the device's memory.

[0917] Step 2:

[0918] The terminal sends the data entered by the user to the server. Specifically, the input data is sent to the server as an HTTP POST request. The input data is encoded in JSON format and sent.

[0919] Step 3:

[0920] The server stores the received data in a database. The server's receiving process extracts the data from the POST request and stores it in the database in the appropriate format. At this point, the user's body shape and constitution data are saved in the system.

[0921] Step 4:

[0922] The server retrieves user data from the database and performs preprocessing to pass it to the generative AI model. This preprocessing includes enhancing the data and removing unnecessary information. The preprocessed data is then formatted in a way that is suitable for the generative AI model.

[0923] Step 5:

[0924] The server inputs the preprocessed data into a generative AI model to generate optimal health advice. The generative AI model generates personalized advice based on the given prompt, referencing past success stories. This process results in specific exercise and dietary guidelines.

[0925] Step 6:

[0926] The server retrieves the generated advice and sends it to the user device as an HTTP response, returning the advice content encoded in JSON format.

[0927] Step 7:

[0928] The device decodes the advice data received from the server and displays it to the user. Specific health advice is then visually displayed on the display of smart glasses or a factory robot.

[0929] This allows users to receive optimal health advice based on their individual body shape and physical constitution data, and manage their health based on that advice.

[0930] 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.

[0931] The present invention is a system that combines a personal diet application that uses a generative AI model to provide an optimal diet method based on the user's body shape and constitution data with an emotion engine that recognizes the user's emotions. This system includes a series of steps: receiving user input data, analyzing it, generating optimal diet advice using the generative AI model, and further adjusting it according to the user's emotional state.

[0932] First, the user logs in to the application and enters their skeletal and physical data, including height, weight, age, and gender, which are designed to reflect the user's physical details. The terminal then provides the user with an input form and prompts them to enter the data. A button is also provided for the user to submit the data they have entered.

[0933] The device then sends the entered data to the server. The server receives the POST request, analyzes the request, and retrieves the user's input data. The retrieved data is then stored in a database. The database centrally manages each user's body shape and constitution data and retains the data for use in subsequent analyses.

[0934] Furthermore, the server is equipped with an emotion engine that analyzes the user's voice and face to recognize the user's current emotional state. Using voice and face recognition technology, it determines the user's emotional state and stores that information in a database.

[0935] The server then retrieves the necessary user data from the database and passes it to the generative AI model for analysis. The generative AI model has learned successful diet methods from users with similar body types and constitutions in the past, and generates optimal diet advice based on new user data. The generated advice is adjusted based on the analysis results of the emotion engine. For example, if the user is feeling stressed, the system can add advice to reduce stress.

[0936] The server then sends the adjusted diet advice back to the user device. The terminal then displays the advice data received from the server so that the user can easily view it. Specifically, the user may be recommended a diet method that combines both "fat-burning exercise" and "balanced diet intake," and additional advice such as "relaxation meditation techniques" may be displayed based on the user's emotional state.

[0937] In this way, the present invention provides a system that helps users achieve their diet goals efficiently and healthily. The combination of a generative AI model and an emotion engine provides highly accurate advice, contributing to increased user satisfaction. Furthermore, by taking the user's emotional state into account, more personalized support can be achieved.

[0938] The processing flow will be explained below.

[0939] Step 1:

[0940] User

[0941] Users log in to the application and enter their skeletal and physical data, such as height, weight, age, and gender.

[0942] Step 2:

[0943] Terminal

[0944] The terminal presents the user with an input form, prompting them to enter data, indicating which fields are required, and prompting them to press a submit button once the data is complete.

[0945] Step 3:

[0946] Terminal

[0947] When the user presses the send button, the device sends the entered data to the server as a POST request. HTTPS is used for data transmission to ensure security.

[0948] Step 4:

[0949] server

[0950] The server receives the POST request, analyzes the request, and retrieves the user's input data. The retrieved data is stored in a temporary variable.

[0951] Step 5:

[0952] server

[0953] The server stores the acquired user data in a database, which centrally manages each user's body shape and constitution data.

[0954] Step 6:

[0955] server

[0956] After the server has finished saving the user data, it starts the emotion engine, which analyzes the user's voice and face data to recognize the user's current emotional state.

[0957] Step 7:

[0958] Terminal

[0959] The device displays an interface to collect the user's voice and facial data for the emotion engine. The user provides the data using the camera and microphone.

[0960] Step 8:

[0961] server

[0962] The server receives and analyzes the voice and face data sent from the device, and the result of the analysis is the user's emotional state (e.g., stress or fatigue).

[0963] Step 9:

[0964] server

[0965] The server retrieves the necessary user data from the database and passes it to the generative AI model for analysis. The generative AI model generates optimal diet advice for each user based on past success stories.

[0966] Step 10:

[0967] server

[0968] The server combines the results of the emotion engine's analysis with those of the generative AI model to tailor advice based on the user's emotional state. For example, if a user is feeling stressed, it might also provide advice on how to relax.

[0969] Step 11:

[0970] server

[0971] The server then sends tailored advice back to the user's device, including specific exercise regimes, meal plans, relaxation techniques, and more.

[0972] Step 12:

[0973] Terminal

[0974] The device displays the received advice data and presents it in an easy-to-read format for easy user access. Each piece of advice is provided with a detailed explanation and implementation method.

[0975] Step 13:

[0976] User

[0977] Users can check the displayed diet advice and put it into practice. By incorporating the recommended exercise, diet, and relaxation methods into their daily lives, they can achieve their diet goals efficiently and healthily.

[0978] Example 2

[0979] 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."

[0980] Conventional personalized diet applications provide diet advice based on a user's body shape and constitution data, but they fail to take the user's emotional state into account. As a result, the advice provided may be ineffective when the user is stressed or in other emotional states. Therefore, there is a need for a system that recognizes a user's emotional state and adjusts diet advice based on that state.

[0981] 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.

[0982] In this invention, the server includes means for a user to input body shape and constitution data, means for receiving the input data and storing it in a database, means for analyzing the stored data using a generative AI model and comparing it with past success cases to generate diet advice, means for analyzing the user's voice and face to recognize their emotional state and adjusting the generated diet advice based on the emotional state, and means for displaying the adjusted diet advice on the user device, thereby enabling the provision of personalized diet advice that takes the user's emotional state into consideration.

[0983] "User device" refers to an electronic device used by a user, including a smartphone, tablet, personal computer, etc.

[0984] A "generative AI model" refers to an artificial intelligence model that learns from large amounts of data and generates optimal diet advice from the input data.

[0985] An "emotion engine" refers to a device or software that analyzes a user's voice and face to recognize their emotional state.

[0986] "Database" refers to a system for centrally managing and storing data on a user's body shape and constitution, as well as data on their emotional state.

[0987] "Body shape and constitution data" refers to information intended to reflect the user's physical details, such as the user's height, weight, age, and gender.

[0988] "Diet Advice" refers to instructions or suggestions regarding optimal diet methods provided to a user based on the analysis results of a generative AI model.

[0989] "Speech recognition" refers to technology for analyzing a user's vocalizations to determine their emotional state.

[0990] "Facial recognition" refers to technology that analyzes a user's facial expressions to determine their emotional state.

[0991] The "data transmission function" refers to a function for transmitting the body shape and constitution data entered by the user to the server.

[0992] This invention is a system that combines a personal diet application that uses a generative AI model to provide an optimal diet method based on the user's body shape and constitution data with an emotion engine that recognizes the user's emotions. Specific embodiments of the present invention are described below.

[0993] User Data Entry

[0994] The user first logs in to the application and enters their body shape and constitution data, including height, weight, age, gender, etc. These data are necessary to reflect the user's physical details. The terminal provides the user with an easy-to-understand input form and prompts them to enter data. After the user completes the input, they press the submit button and the data is sent to the server.

[0995] Data transmission and storage

[0996] The terminal sends the data entered by the user to the server. This transmission is performed using a POST request. The server receives the POST request, analyzes the request contents, and obtains the data entered by the user. The obtained data is saved in a database, and the body shape and constitution data for each user are managed in a unified manner.

[0997] Emotion engine recognizes emotional states

[0998] The server is equipped with an emotion engine that analyzes the user's voice and facial data to recognize their emotional state. It uses voice and facial recognition technology to determine the user's emotional state. This information is stored in a database for later analysis.

[0999] Diet advice generation using generative AI models

[1000] The server retrieves the necessary user data from the database and inputs it into the generative AI model. The generative AI model has learned successful diet methods from users with similar body types and constitutions in the past, and generates optimal diet advice using new user data as input. For example, the generated advice might be specific, such as "jog three times a week as a fat-burning exercise."

[1001] Adjusting diet advice

[1002] The server adjusts the diet advice generated based on the analysis results of the emotion engine. For example, if the user is feeling stressed, advice to reduce stress will be added. Specific advice such as "Meditate for 10 minutes a day to reduce stress" may be added.

[1003] Providing diet advice

[1004] The adjusted diet advice is sent back from the server to the terminal, which displays the received advice data to the user, who can then directly confirm the displayed advice.

[1005] Examples of specific examples and prompts

[1006] For example, if a user enters the following data:

[1007] Height: 170 cm

[1008] Weight: 70 kg

[1009] Age: 30

[1010] Gender: Male

[1011] This data is sent to a server, where it is analyzed and the following diet advice is presented:

[1012] Jogging three times a week as a fat-burning exercise

[1013] A balanced diet centered around vegetables

[1014] Meditation techniques for relaxation

[1015] Example prompt sentence:

[1016] User data: Height 170 cm, Weight 70 kg, Age 30, Gender Male

[1017] User's emotional state: stressed

[1018] Generate optimal diet advice based on generative AI models and adjust based on emotional state.

[1019] In this way, the present invention realizes a system that combines a generative AI model and an emotion engine to provide optimal diet advice based on the user's body shape and constitution data. Personalized advice that takes into account the user's emotional state improves user satisfaction.

[1020] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1021] Step 1:

[1022] A user logs in to the application. After logging in, the user enters their body shape and physical constitution data, such as height, weight, age, and gender. The terminal provides a form for entering this data, and allows the user to press a submit button after completing the input.

[1023] Input: Data such as height, weight, age, and gender

[1024] Output: User-entered dataset

[1025] Specific action: The user enters "height 170cm" and presses the "Send" button.

[1026] Step 2:

[1027] The terminal sends the data entered by the user to the server using a POST request.

[1028] Input: A user-entered dataset

[1029] Output: Data sent to the server as a POST request

[1030] Specific operation: The device sends a POST request to the server containing data such as "User ID 123 - height 170cm".

[1031] Step 3:

[1032] The server analyzes the received POST request and extracts the user's input data, which is then stored in a database.

[1033] Input: User data sent as a POST request

[1034] Output: User data stored in the database

[1035] Specific operation: The server saves "User ID 123 - height 170cm, weight 70kg, age 30, gender male" in the database.

[1036] Step 4:

[1037] The emotion engine built into the server analyzes the user's voice and facial data to recognize their emotional state. It uses voice and facial recognition technology to determine the user's emotions and stores the information in a database.

[1038] Input: User voice and facial data

[1039] Output: Recognized emotional state data

[1040] Specific operation: The emotion engine recognizes "User ID 123 - Emotional state: Stress" and saves it in the database.

[1041] Step 5:

[1042] The server retrieves the necessary user data from the database and inputs it into the generative AI model, which learns from past success stories and generates optimal diet advice based on the user data.

[1043] Input: User data retrieved from the database

[1044] Output: Generated diet advice

[1045] Specific operation: The generative AI model generates advice such as "Jogging three times a week as a fat-burning exercise."

[1046] Step 6:

[1047] The server adjusts the diet advice generated based on the analysis results of the emotion engine, and can add advice for stress reduction depending on the emotional state.

[1048] Input: Generated diet advice and analysis results of the emotion engine

[1049] Output: Tailored diet advice

[1050] Specific behavior: An adjustment such as "User ID 123 - Add 10 minutes of meditation per day to reduce stress" will be made.

[1051] Step 7:

[1052] The server returns the adjusted diet advice to the terminal.

[1053] The terminal displays the received advice data to the user, so that the user can easily view it.

[1054] Enter: tailored diet advice

[1055] Output: Diet advice displayed to the user

[1056] Specific actions: The device will display a message such as "Jogging three times a week to burn fat and meditating for 10 minutes a day to reduce stress."

[1057] (Application example 2)

[1058] 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."

[1059] Conventional personalized diet applications provide optimal diet methods based on the user's body shape and physical constitution data, but because they do not take into account the user's current emotional state, users may become stressed or lose motivation, leading to problems with not being able to continue the diet. Furthermore, real-time feedback and individualized support are insufficient, making it difficult to improve user satisfaction.

[1060] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1061] In this invention, the server includes means for a user to input body shape and constitution data, means for receiving the input data and storing the input data in a database, means for analyzing the stored data using a generative AI model and comparing it with past success cases to generate diet advice, means for displaying the generated advice on a user device, means for adjusting the advice based on the user's emotional state, means for analyzing voice and face to recognize the emotional state, and means for storing the analyzed emotional state data in a database. This allows for the provision of specific and personalized diet advice tailored to the user's current situation and further enables individual responses based on the emotional state.

[1062] "User device" is a general term for electronic devices that users use to operate applications, including smartphones, tablets, smart glasses, etc.

[1063] A "database" is a system for efficiently storing, managing, and retrieving data such as a user's body shape, constitution, and emotional state.

[1064] A "generative AI model" is an artificial intelligence system that analyzes new data based on past success stories and generates appropriate diet advice.

[1065] The "emotion engine" is a system that analyzes the user's emotional state based on voice and facial information and recognizes that state.

[1066] "Preprocessing" refers to the initial data processing used to prepare the format and quality of the data to be analyzed by the generative AI model.

[1067] "Individualized" refers to providing advice and feedback that is customized to the user's individual characteristics, such as their current body shape, constitution, and emotional state.

[1068] "Real-time feedback" refers to responses and advice provided immediately to a user's actions or state.

[1069] The system embodying the present invention recognizes a user's body shape and constitution data, as well as their emotional state, in real time and provides diet advice. This system is composed of a user device, a server, a database, a generative AI model, and an emotion engine.

[1070] System Configuration

[1071] 1. User Devices

[1072] The user wears the smart glasses and inputs their body shape and physical characteristics. The smart glasses have a built-in camera that captures the user's face in real time.

[1073] The smart glasses display a form for inputting body shape and physical characteristics data, in which the user inputs data such as height, weight, age, and gender.

[1074] Additionally, data is collected through the smart glasses' camera and microphone to recognize the user's emotional state.

[1075] 2. Server

[1076] Body shape and constitution data and emotional state data transmitted from the user device are received.

[1077] The received data is stored in a database, which centrally manages this data.

[1078] 3. Database

[1079] Stores data on each user's body shape, constitution, and emotional state.

[1080] Data will be acquired as needed and used for analysis.

[1081] 4. Generative AI Models

[1082] The system analyzes user data obtained from the database and generates optimal diet advice based on past success stories.

[1083] The generated diet advice is adjusted based on the emotional state data analyzed by the emotion engine.

[1084] 5. Emotion Engine

[1085] It analyzes data collected by the camera and microphone of the user's device to recognize the user's emotional state.

[1086] The recognized emotion data is stored in a database.

[1087] Hardware and Software

[1088] The hardware and software used includes:

[1089] Smart glasses (with built-in camera and microphone)

[1090] Server (connected to database)

[1091] Database management system (MySQL, etc.)

[1092] Generative AI model (AI model that learns from past success stories)

[1093] Emotion Engine (EmotionRecognition Library)

[1094] Specific examples

[1095] Suppose a user is wearing smart glasses while training on a treadmill. The glasses' camera captures facial expressions in real time, and an emotion engine detects stress levels. This information is sent to a server and stored in a database. The generative AI model provides real-time advice to ease up on the training based on the user's body shape and stress level data. Feedback such as "Your current pace is appropriate. Take a few minutes to relax and take some deep breaths" appears on the smart glasses' display.

[1096] Prompt Sentence Examples

[1097] "I'm a 170cm tall, 70kg, 30-year-old male who is under stress. What is the best fitness advice?"

[1098] This allows for specific and personalized diet and fitness advice tailored to the user's current situation, and individualized responses based on emotional state.

[1099] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1100] Step 1:

[1101] The user puts on the smart glasses and inputs their body shape and constitution data.

[1102] Input: The user uses the input form on the smart glasses to enter data such as height, weight, age, and gender.

[1103] Data processing: The entered data is cached locally.

[1104] Output: Body shape and constitution data is input, and the terminal prepares to send it to the server.

[1105] Step 2:

[1106] The smart glasses' camera and microphone collect data on the user's emotional state.

[1107] Input: Camera video data and microphone audio data.

[1108] Data processing: Video data is analyzed using facial recognition software, and audio data is processed using audio analysis software.

[1109] Output: The generated emotional state data is sent to the device.

[1110] Step 3:

[1111] The terminal transmits the body shape and constitution data and the emotional state data to the server.

[1112] Input: Body shape and constitution data, emotional state data.

[1113] Data Calculation: The terminal organizes the data into a single JSON document.

[1114] Output: The constructed JSON document is sent to the server.

[1115] Step 4:

[1116] The server analyzes the received data and stores it in a database.

[1117] Input: A JSON document containing body shape and constitution data, and emotional state data.

[1118] Data Transformation: Data is deserialized and mapped to the appropriate fields to be saved in the database.

[1119] Output: User data is saved in the database.

[1120] Step 5:

[1121] The server retrieves the necessary data from the database and analyzes it using a generative AI model.

[1122] Input: User's body shape and constitution data.

[1123] Data calculation: The generative AI model compares input data with past success cases and generates optimal diet advice.

[1124] Output: The generated diet advice.

[1125] Step 6:

[1126] The server adjusts the generated diet advice using an emotion engine.

[1127] Input: diet advice data, emotional state data.

[1128] Data calculation: Advice content is adjusted according to emotional state (for example, adding relaxation techniques when stressed).

[1129] Output: Tailored diet advice.

[1130] Step 7:

[1131] The server sends the final diet advice back to the user device.

[1132] Enter: tailored diet advice.

[1133] Data Processing: Advice data is serialized into a format that can be displayed on the user device.

[1134] Output: The advice data is sent to the user device.

[1135] Step 8:

[1136] The user device displays the received advice on the display of the smart glasses.

[1137] Enter: tailored diet advice.

[1138] Data calculation: The rendering process is carried out to display the advice in a visually appealing way.

[1139] Output: The user can see the diet advice on the smart glasses.

[1140] 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.

[1141] 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.

[1142] 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.

[1143] [Fourth embodiment]

[1144] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1145] 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.

[1146] 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).

[1147] 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.

[1148] 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.

[1149] 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).

[1150] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1151] 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.

[1152] 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.

[1153] 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.

[1154] 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.

[1155] 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.

[1156] 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."

[1157] The present invention is a personal diet application that uses a generative AI model to provide optimal dieting methods based on a user's body shape and constitution data. The system includes a series of steps to receive data entered by the user, analyze it, and generate optimal diet advice.

[1158] First, users log in to the application and enter their skeletal and physical data, including height, weight, age, and gender, which are designed to reflect the user's physical details.

[1159] Next, the terminal receives the data entered by the user and sends it to the server. A data entry form is displayed on the terminal, and when the user enters data and presses the submit button, the terminal sends the data to the server as a POST request. The server saves the received data in a database. The database centrally manages body shape and constitution data for each user and retains the data for use in subsequent analysis.

[1160] The server retrieves user data from the database and passes it to the generative AI model for analysis. The generative AI model has learned successful diet methods for users with similar body types and constitutions in the past, and generates optimal diet advice using new user data as input. Based on the results of this analysis, the server constructs advice on the optimal diet method for the user.

[1161] The server then returns the generated advice to the user's device. The device then displays the advice data received from the server so that the user can easily view it. For example, if a diet method that combines both "fat-burning exercise" and "balanced diet intake" is recommended to the user, the user can check the detailed steps and precautions within the application.

[1162] As a concrete example, suppose a user enters data such as height 170 cm, weight 75 kg, and age 30. This data is sent to the server, which stores it in a database. The stored data is analyzed by a generative AI model, which generates advice such as "exercise aerobically three times a week and limit calorie intake to 1800 kcal" based on past success stories of people with a similar body shape and constitution. The server then sends this advice back to the user's device and displays it on the user's device. The user can then follow this advice and put the specific diet plan into practice.

[1163] In this way, the present invention provides a system that helps users achieve their diet goals in an efficient and healthy manner.

[1164] The processing flow will be explained below.

[1165] Step 1:

[1166] User

[1167] Users log in to the application and enter their skeletal and physical data (height, weight, age, etc.).

[1168] Step 2:

[1169] Terminal

[1170] The terminal provides the user with an input form and prompts them to enter data.

[1171] Provide a button (submit button) for users to submit input data.

[1172] Step 3:

[1173] Terminal

[1174] When the user presses the send button, the terminal sends the input data to the server as a POST request.

[1175] Step 4:

[1176] server

[1177] The server receives the POST request, analyzes the contents of the request, and obtains the user's input data.

[1178] Step 5:

[1179] server

[1180] The server stores the retrieved user data in a database.

[1181] Step 6:

[1182] server

[1183] The server retrieves the necessary user data from the database and passes it to the generative AI model for analysis.

[1184] Step 7:

[1185] server

[1186] The generative AI model analyzes user data based on past success stories and generates optimal diet advice.

[1187] Step 8:

[1188] server

[1189] The server sends the generated diet advice back to the user device.

[1190] Step 9:

[1191] Terminal

[1192] The terminal receives the advice data from the server and displays it in a format that is easy for the user to view.

[1193] Step 10:

[1194] User

[1195] The user checks the displayed diet advice and puts into practice the specific diet plan.

[1196] Example 1

[1197] 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."

[1198] Conventional diet support systems have had difficulty providing personalized diet advice based on the user's body shape and constitution. In particular, they lacked the means to generate individually optimized advice in real time based on body shape and constitution data, forcing users to follow general advice, resulting in low success rates for dieting. There is a need to solve this problem and provide more accurate personalized diet advice to efficiently support users in maintaining and improving their health.

[1199] 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.

[1200] In this invention, the server includes a means for sending data entered by the user as an HTTP POST request, a means for storing the data received by the server in a database via a query, a means for sending an analysis request to the generative AI model using a prompt sentence, and a means for returning the generated advice in JSON format, thereby enabling the generation and provision of highly accurate personalized diet advice in real time based on the user's body shape and constitution data.

[1201] "User" refers to an individual who uses the personal diet application to input their own body shape and constitution data and receive diet advice.

[1202] The "server" is a computer system that has the function of receiving data sent by users, storing it in a database, analyzing the data using a generative AI model, generating optimal diet advice, and delivering it to the user's device.

[1203] "Terminal" refers to a device used by a user to input body shape and constitution data, and includes smartphones, tablets, PCs, etc.

[1204] A "database" is a system for centrally managing and storing each user's body shape and constitution data, and includes relational databases such as MySQL and PostgreSQL.

[1205] A "generative AI model" is an artificial intelligence model that learns from past body shape and constitution data and successful diet cases, and analyzes new data to generate optimal diet advice.

[1206] A "prompt sentence" is an instruction sentence used when sending an analysis request to a generative AI model, and is a sentence that instructs the model to generate diet advice based on the user's specific body shape and constitution data.

[1207] An "HTTP POST request" is a communication method for sending data entered by a user to a server, and is a protocol for sending data in JSON format or other formats.

[1208] The "JSON format" is a lightweight text-based data format used for data exchange, and is primarily composed of key-value pairs.

[1209] The present invention is a personal diet application that uses a generative AI model to provide optimal diet advice based on a user's body shape and constitution data. The system includes a series of steps to receive data entered by the user, analyze it, and generate optimal diet advice.

[1210] First, the user logs in to the application using a dedicated device (smartphone, tablet, PC) and enters their body shape and constitution data. The input data includes height, weight, age, gender, etc. This input form is designed to allow the user to easily enter the required information. For example, suppose a user enters data such as height 170 cm, weight 75 kg, and age 30.

[1211] Next, the device sends the data entered by the user to the server. The data is sent in JSON format using an HTTP POST request. Specifically, the data format is as follows:

[1212] json

[1213] {

[1214] "Height": 170,

[1215] "Weight": 75,

[1216] "Age": 30,

[1217] "Gender": "Male"

[1218] }

[1219] The server receives the data sent from the device and stores it in a database (MySQL, PostgreSQL, etc.). The database serves to centrally manage the body shape and constitution data for each user.

[1220] Based on the saved data, the server sends an analysis request to the generative AI model. For example, OpenAI's GPT-4 can be used as the generative AI model. The prompt statement input to the generative AI model is, "Please provide the optimal diet plan for a user who is 170 cm tall, weighs 75 kg, and is 30 years old." The generative AI model performs analysis based on this prompt statement and generates optimal diet advice.

[1221] For example, the generative AI model generates advice such as "Do aerobic exercise three times a week and limit your calorie intake to 1,800 kcal." The analysis results are then sent back to the server.

[1222] The server returns the diet advice received from the generative AI model to the user device (terminal) in JSON format. The terminal analyzes the advice data received from the server and displays it so that the user can easily view it. For example, the user's screen might display a specific plan and its details, such as "Perform aerobic exercise three times a week and keep calorie intake below 1800 kcal."

[1223] This allows users to implement an individually optimized diet plan based on their own body shape and constitution, and the system helps users achieve their diet goals efficiently and healthily.

[1224] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1225] Step 1:

[1226] The user logs in to the personal diet application on their device. After logging in, a form is displayed in which they can enter their body shape and constitution data, such as height, weight, age, and gender. The user enters this data into the form and presses the "Submit" button. The input here is assumed to be height 170cm, weight 75kg, age 30, and gender male.

[1227] Input: User data (height, weight, age, gender)

[1228] Output: Data sent from the device to the server

[1229] Specific operation: The user enters their height (170 cm), weight (75 kg), age (30 years old, etc.) into the input fields displayed on the screen of their smartphone or PC, and presses the "Send" button.

[1230] Step 2:

[1231] The terminal sends the data entered by the user to the server in JSON format as an HTTP POST request. The JSON data has the following format:

[1232] json

[1233] {

[1234] "Height": 170,

[1235] "Weight": 75,

[1236] "Age": 30,

[1237] "Gender": "Male"

[1238] }

[1239] Input: User data formatted in JSON format

[1240] Output: User data sent to the server

[1241] Specific operation: The terminal converts the information entered by the user into JSON format and sends it as an HTTP POST request.

[1242] Step 3:

[1243] The server receives the data sent from the device and stores it in a database (e.g., MySQL or PostgreSQL). The server analyzes the received data and stores it through appropriate database queries.

[1244] Input: Received user data (JSON format)

[1245] Output: User data stored in the database

[1246] Specific operation: The server receives the HTTP request, parses the JSON data, and stores it in a database, for example, by executing an INSERT query using an SQL statement.

[1247] Step 4:

[1248] The server retrieves the user data stored in the database and sends an analysis request to the generative AI model. The generative AI model (for example, OpenAI's GPT-4) is asked to perform the analysis using a prompt sentence. An example of a prompt sentence is, "Please provide the optimal diet plan for a user who is 170 cm tall, weighs 75 kg, and is 30 years old."

[1249] Input: User data retrieved from the database

[1250] Output: The analysis request sent to the generative AI model

[1251] Specific operation: The server retrieves user data from the database and creates and sends the API requests required for the generative AI model.

[1252] Step 5:

[1253] The generative AI model generates optimal diet advice based on the prompt and input data, such as "exercise aerobic exercise three times a week and limit your calorie intake to 1,800 kcal."

[1254] Input: Parse request (prompt statement and user data)

[1255] Output: Generated diet advice

[1256] How it works: The generative AI model analyzes user data based on prompt statements and generates specific diet advice.

[1257] Step 6:

[1258] The server then sends diet advice obtained from the generative AI model back to the user device. The advice is sent from the server to the device in JSON format.

[1259] Input: Generated diet advice

[1260] Output: Advice data sent to the terminal

[1261] Specific operation: The server converts the generated advice into JSON format and sends it to the user device as an HTTP response.

[1262] Step 7:

[1263] The device analyzes the advice data received from the server and displays it on the screen. The user can then check the specific diet plan that has been generated on the application screen and implement it.

[1264] Input: Advice data received from the server

[1265] Output: Diet advice displayed to the user

[1266] Specific operation: The device decodes the received advice and displays it on the screen. For example, it displays a specific plan such as "Do aerobic exercise three times a week and keep your calorie intake below 1800 kcal."

[1267] (Application example 1)

[1268] 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."

[1269] Health management is an important issue for factory workers in harsh working environments. However, conventional health management methods are difficult to provide individualized support and are unable to provide efficient advice. For this reason, there is a need for a system that can provide optimal health advice based on the body shape and constitution of each individual employee.

[1270] 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.

[1271] In this invention, the server includes means for a user to input body shape and constitution data, means for receiving the input data and storing it in a database, means for analyzing the stored data using a generative AI model and comparing it with past success cases to generate diet advice, means for displaying the generated advice on a user device, means for providing health advice for managing the health of factory employees, and means for displaying the health advice on a factory robot or smart glasses. This makes it possible to provide optimal health advice to factory employees based on their individual body shape and constitution data.

[1272] The "means for the user to input data on body shape and constitution" is an interface for the user to input physical information such as their height, weight, age, and sex.

[1273] The "means for receiving the input data and storing it in a database" is a function for transmitting the physical information input by the user to a server and storing the data in a database.

[1274] A "generative AI model" is an artificial intelligence model that learns from past success stories and analyzes new user data to provide optimal diet and health management advice.

[1275] "Past success stories" are cases where optimal health advice based on body shape and constitution was successfully provided based on data collected to date.

[1276] "Diet advice" involves a generative AI model analyzing user data and providing optimal diet and exercise guidelines for managing the user's health.

[1277] "User Device" means the device used by the user to view the advice, such as a smartphone, tablet, or other display device.

[1278] "Means for providing health advice for health management" refers to means for providing guidelines on lifestyle, exercise, and diet necessary for maintaining and improving the health of factory employees.

[1279] "Means for displaying on factory robots and smart glasses" refers to a function that displays the generated health advice on robots and wearable devices used in factories, allowing employees to visually receive the advice.

[1280] This invention is designed as a system to support the health management of factory workers. First, the user, a factory worker, accesses an interface to input their own body shape and physical characteristics data (height, weight, age, gender, etc.). This is done using a form displayed on the display of the smart glasses or robot terminal. Once the user enters and submits the data, it is sent to the server and stored in a database.

[1281] The server retrieves the stored data and passes it to a generative AI model for analysis. The generative AI model, trained on past success stories, generates optimal health advice based on new user data. This generated advice is then sent by the server back to the user's device (smart glasses or factory robot) and displayed to the user.

[1282] As a concrete example, suppose a user enters the following data: height 170 cm, weight 75 kg, age 30, and gender: male. This data is sent to the server, which stores it in a database. As a result of analysis by the generative AI model, health management advice is generated, such as "do aerobic exercise three times a week for 30 minutes each time" and "limit daily calorie intake to 1800 kcal." This advice is sent back from the server to the user's device and displayed on the display of smart glasses or a factory robot.

[1283] This system makes it possible to provide optimal health advice based on individual body shapes and constitutions, and efficiently manage and improve the health of factory employees, which is expected to improve work efficiency and employee satisfaction.

[1284] An example prompt is:

[1285] "Based on the given user data (health profile: height 170cm, weight 75kg, age 30, gender: male), please provide advice on optimal lifestyle, exercise, and dietary habits to maintain physical health."

[1286] The hardware uses smart glasses and factory robots, and the software uses generative AI models, which can handle everything from data input to generating and displaying advice.

[1287] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1288] Step 1:

[1289] Users input their body shape and physical characteristics (height, weight, age, gender, etc.) using an interface displayed on the display of smart glasses or a factory robot, and the input data is temporarily stored in the device's memory.

[1290] Step 2:

[1291] The terminal sends the data entered by the user to the server. Specifically, the input data is sent to the server as an HTTP POST request. The input data is encoded in JSON format and sent.

[1292] Step 3:

[1293] The server stores the received data in a database. The server's receiving process extracts the data from the POST request and stores it in the database in the appropriate format. At this point, the user's body shape and constitution data are saved in the system.

[1294] Step 4:

[1295] The server retrieves user data from the database and performs preprocessing to pass it to the generative AI model. This preprocessing includes enhancing the data and removing unnecessary information. The preprocessed data is then formatted in a way that is suitable for the generative AI model.

[1296] Step 5:

[1297] The server inputs the preprocessed data into a generative AI model to generate optimal health advice. The generative AI model generates personalized advice based on the given prompt, referencing past success stories. This process results in specific exercise and dietary guidelines.

[1298] Step 6:

[1299] The server retrieves the generated advice and sends it to the user device as an HTTP response, returning the advice content encoded in JSON format.

[1300] Step 7:

[1301] The device decodes the advice data received from the server and displays it to the user. Specific health advice is then visually displayed on the display of smart glasses or a factory robot.

[1302] This allows users to receive optimal health advice based on their individual body shape and physical constitution data, and manage their health based on that advice.

[1303] 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.

[1304] The present invention is a system that combines a personal diet application that uses a generative AI model to provide an optimal diet method based on the user's body shape and constitution data with an emotion engine that recognizes the user's emotions. This system includes a series of steps: receiving user input data, analyzing it, generating optimal diet advice using the generative AI model, and further adjusting it according to the user's emotional state.

[1305] First, the user logs in to the application and enters their skeletal and physical data, including height, weight, age, and gender, which are designed to reflect the user's physical details. The terminal then provides the user with an input form and prompts them to enter the data. A button is also provided for the user to submit the data they have entered.

[1306] The device then sends the entered data to the server. The server receives the POST request, analyzes the request, and retrieves the user's input data. The retrieved data is then stored in a database. The database centrally manages each user's body shape and constitution data and retains the data for use in subsequent analyses.

[1307] Furthermore, the server is equipped with an emotion engine that analyzes the user's voice and face to recognize the user's current emotional state. Using voice and face recognition technology, it determines the user's emotional state and stores that information in a database.

[1308] The server then retrieves the necessary user data from the database and passes it to the generative AI model for analysis. The generative AI model has learned successful diet methods from users with similar body types and constitutions in the past, and generates optimal diet advice based on new user data. The generated advice is adjusted based on the analysis results of the emotion engine. For example, if the user is feeling stressed, the system can add advice to reduce stress.

[1309] The server then sends the adjusted diet advice back to the user device. The terminal then displays the advice data received from the server so that the user can easily view it. Specifically, the user may be recommended a diet method that combines both "fat-burning exercise" and "balanced diet intake," and additional advice such as "relaxation meditation techniques" may be displayed based on the user's emotional state.

[1310] In this way, the present invention provides a system that helps users achieve their diet goals efficiently and healthily. The combination of a generative AI model and an emotion engine provides highly accurate advice, contributing to increased user satisfaction. Furthermore, by taking the user's emotional state into account, more personalized support can be achieved.

[1311] The processing flow will be explained below.

[1312] Step 1:

[1313] User

[1314] Users log in to the application and enter their skeletal and physical data, such as height, weight, age, and gender.

[1315] Step 2:

[1316] Terminal

[1317] The terminal presents the user with an input form, prompting them to enter data, indicating which fields are required, and prompting them to press a submit button once the data is complete.

[1318] Step 3:

[1319] Terminal

[1320] When the user presses the send button, the device sends the entered data to the server as a POST request. HTTPS is used for data transmission to ensure security.

[1321] Step 4:

[1322] server

[1323] The server receives the POST request, analyzes the request, and retrieves the user's input data. The retrieved data is stored in a temporary variable.

[1324] Step 5:

[1325] server

[1326] The server stores the acquired user data in a database, which centrally manages each user's body shape and constitution data.

[1327] Step 6:

[1328] server

[1329] After the server has finished saving the user data, it starts the emotion engine, which analyzes the user's voice and face data to recognize the user's current emotional state.

[1330] Step 7:

[1331] Terminal

[1332] The device displays an interface to collect the user's voice and facial data for the emotion engine. The user provides the data using the camera and microphone.

[1333] Step 8:

[1334] server

[1335] The server receives and analyzes the voice and face data sent from the device, and the result of the analysis is the user's emotional state (e.g., stress or fatigue).

[1336] Step 9:

[1337] server

[1338] The server retrieves the necessary user data from the database and passes it to the generative AI model for analysis. The generative AI model generates optimal diet advice for each user based on past success stories.

[1339] Step 10:

[1340] server

[1341] The server combines the results of the emotion engine's analysis with those of the generative AI model to tailor advice based on the user's emotional state. For example, if a user is feeling stressed, it might also provide advice on how to relax.

[1342] Step 11:

[1343] server

[1344] The server then sends tailored advice back to the user's device, including specific exercise regimes, meal plans, relaxation techniques, and more.

[1345] Step 12:

[1346] Terminal

[1347] The device displays the received advice data and presents it in an easy-to-read format for easy user access. Each piece of advice is provided with a detailed explanation and implementation method.

[1348] Step 13:

[1349] User

[1350] Users can check the displayed diet advice and put it into practice. By incorporating the recommended exercise, diet, and relaxation methods into their daily lives, they can achieve their diet goals efficiently and healthily.

[1351] Example 2

[1352] 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."

[1353] Conventional personalized diet applications provide diet advice based on a user's body shape and constitution data, but they fail to take the user's emotional state into account. As a result, the advice provided may be ineffective when the user is stressed or in other emotional states. Therefore, there is a need for a system that recognizes a user's emotional state and adjusts diet advice based on that state.

[1354] 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.

[1355] In this invention, the server includes means for a user to input body shape and constitution data, means for receiving the input data and storing it in a database, means for analyzing the stored data using a generative AI model and comparing it with past success cases to generate diet advice, means for analyzing the user's voice and face to recognize their emotional state and adjusting the generated diet advice based on the emotional state, and means for displaying the adjusted diet advice on the user device, thereby enabling the provision of personalized diet advice that takes the user's emotional state into consideration.

[1356] "User device" refers to an electronic device used by a user, including a smartphone, tablet, personal computer, etc.

[1357] A "generative AI model" refers to an artificial intelligence model that learns from large amounts of data and generates optimal diet advice from the input data.

[1358] An "emotion engine" refers to a device or software that analyzes a user's voice and face to recognize their emotional state.

[1359] "Database" refers to a system for centrally managing and storing data on a user's body shape and constitution, as well as data on their emotional state.

[1360] "Body shape and constitution data" refers to information intended to reflect the user's physical details, such as the user's height, weight, age, and gender.

[1361] "Diet Advice" refers to instructions or suggestions regarding optimal diet methods provided to a user based on the analysis results of a generative AI model.

[1362] "Speech recognition" refers to technology for analyzing a user's vocalizations to determine their emotional state.

[1363] "Facial recognition" refers to technology that analyzes a user's facial expressions to determine their emotional state.

[1364] The "data transmission function" refers to a function for transmitting the body shape and constitution data entered by the user to the server.

[1365] This invention is a system that combines a personal diet application that uses a generative AI model to provide an optimal diet method based on the user's body shape and constitution data with an emotion engine that recognizes the user's emotions. Specific embodiments of the present invention are described below.

[1366] User Data Entry

[1367] The user first logs in to the application and enters their body shape and constitution data, including height, weight, age, gender, etc. These data are necessary to reflect the user's physical details. The terminal provides the user with an easy-to-understand input form and prompts them to enter data. After the user completes the input, they press the submit button and the data is sent to the server.

[1368] Data transmission and storage

[1369] The terminal sends the data entered by the user to the server. This transmission is performed using a POST request. The server receives the POST request, analyzes the request contents, and obtains the data entered by the user. The obtained data is saved in a database, and the body shape and constitution data for each user are managed in a unified manner.

[1370] Emotion engine recognizes emotional states

[1371] The server is equipped with an emotion engine that analyzes the user's voice and facial data to recognize their emotional state. It uses voice and facial recognition technology to determine the user's emotional state. This information is stored in a database for later analysis.

[1372] Diet advice generation using generative AI models

[1373] The server retrieves the necessary user data from the database and inputs it into the generative AI model. The generative AI model has learned successful diet methods from users with similar body types and constitutions in the past, and generates optimal diet advice using new user data as input. For example, the generated advice might be specific, such as "jog three times a week as a fat-burning exercise."

[1374] Adjusting diet advice

[1375] The server adjusts the diet advice generated based on the analysis results of the emotion engine. For example, if the user is feeling stressed, advice to reduce stress will be added. Specific advice such as "Meditate for 10 minutes a day to reduce stress" may be added.

[1376] Providing diet advice

[1377] The adjusted diet advice is sent back from the server to the terminal, which displays the received advice data to the user, who can then directly confirm the displayed advice.

[1378] Examples of specific examples and prompts

[1379] For example, if a user enters the following data:

[1380] Height: 170 cm

[1381] Weight: 70 kg

[1382] Age: 30

[1383] Gender: Male

[1384] This data is sent to a server, where it is analyzed and the following diet advice is presented:

[1385] Jogging three times a week as a fat-burning exercise

[1386] A balanced diet centered around vegetables

[1387] Meditation techniques for relaxation

[1388] Example prompt sentence:

[1389] User data: Height 170 cm, Weight 70 kg, Age 30, Gender Male

[1390] User's emotional state: stressed

[1391] Generate optimal diet advice based on generative AI models and adjust based on emotional state.

[1392] In this way, the present invention realizes a system that combines a generative AI model and an emotion engine to provide optimal diet advice based on the user's body shape and constitution data. Personalized advice that takes into account the user's emotional state improves user satisfaction.

[1393] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1394] Step 1:

[1395] A user logs in to the application. After logging in, the user enters their body shape and physical constitution data, such as height, weight, age, and gender. The terminal provides a form for entering this data, and allows the user to press a submit button after completing the input.

[1396] Input: Data such as height, weight, age, and gender

[1397] Output: User-entered dataset

[1398] Specific action: The user enters "height 170cm" and presses the "Send" button.

[1399] Step 2:

[1400] The terminal sends the data entered by the user to the server using a POST request.

[1401] Input: A user-entered dataset

[1402] Output: Data sent to the server as a POST request

[1403] Specific operation: The device sends a POST request to the server containing data such as "User ID 123 - height 170cm".

[1404] Step 3:

[1405] The server analyzes the received POST request and extracts the user's input data, which is then stored in a database.

[1406] Input: User data sent as a POST request

[1407] Output: User data stored in the database

[1408] Specific operation: The server saves "User ID 123 - height 170cm, weight 70kg, age 30, gender male" in the database.

[1409] Step 4:

[1410] The emotion engine built into the server analyzes the user's voice and facial data to recognize their emotional state. It uses voice and facial recognition technology to determine the user's emotions and stores the information in a database.

[1411] Input: User voice and facial data

[1412] Output: Recognized emotional state data

[1413] Specific operation: The emotion engine recognizes "User ID 123 - Emotional state: Stress" and saves it in the database.

[1414] Step 5:

[1415] The server retrieves the necessary user data from the database and inputs it into the generative AI model, which learns from past success stories and generates optimal diet advice based on the user data.

[1416] Input: User data retrieved from the database

[1417] Output: Generated diet advice

[1418] Specific operation: The generative AI model generates advice such as "Jogging three times a week as a fat-burning exercise."

[1419] Step 6:

[1420] The server adjusts the diet advice generated based on the analysis results of the emotion engine, and can add advice for stress reduction depending on the emotional state.

[1421] Input: Generated diet advice and analysis results of the emotion engine

[1422] Output: Tailored diet advice

[1423] Specific behavior: An adjustment such as "User ID 123 - Add 10 minutes of meditation per day to reduce stress" will be made.

[1424] Step 7:

[1425] The server returns the adjusted diet advice to the terminal.

[1426] The terminal displays the received advice data to the user, so that the user can easily view it.

[1427] Enter: tailored diet advice

[1428] Output: Diet advice displayed to the user

[1429] Specific actions: The device will display a message such as "Jogging three times a week to burn fat and meditating for 10 minutes a day to reduce stress."

[1430] (Application example 2)

[1431] 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."

[1432] Conventional personalized diet applications provide optimal diet methods based on the user's body shape and physical constitution data, but because they do not take into account the user's current emotional state, users may become stressed or lose motivation, leading to problems with not being able to continue the diet. Furthermore, real-time feedback and individualized support are insufficient, making it difficult to improve user satisfaction.

[1433] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1434] In this invention, the server includes means for a user to input body shape and constitution data, means for receiving the input data and storing the input data in a database, means for analyzing the stored data using a generative AI model and comparing it with past success cases to generate diet advice, means for displaying the generated advice on a user device, means for adjusting the advice based on the user's emotional state, means for analyzing voice and face to recognize the emotional state, and means for storing the analyzed emotional state data in a database. This allows for the provision of specific and personalized diet advice tailored to the user's current situation and further enables individual responses based on the emotional state.

[1435] "User device" is a general term for electronic devices that users use to operate applications, including smartphones, tablets, smart glasses, etc.

[1436] A "database" is a system for efficiently storing, managing, and retrieving data such as a user's body shape, constitution, and emotional state.

[1437] A "generative AI model" is an artificial intelligence system that analyzes new data based on past success stories and generates appropriate diet advice.

[1438] The "emotion engine" is a system that analyzes the user's emotional state based on voice and facial information and recognizes that state.

[1439] "Preprocessing" refers to the initial data processing used to prepare the format and quality of the data to be analyzed by the generative AI model.

[1440] "Individualized" refers to providing advice and feedback that is customized to the user's individual characteristics, such as their current body shape, constitution, and emotional state.

[1441] "Real-time feedback" refers to responses and advice provided immediately to a user's actions or state.

[1442] The system embodying the present invention recognizes a user's body shape and constitution data, as well as their emotional state, in real time and provides diet advice. This system is composed of a user device, a server, a database, a generative AI model, and an emotion engine.

[1443] System Configuration

[1444] 1. User Devices

[1445] The user wears the smart glasses and inputs their body shape and physical characteristics. The smart glasses have a built-in camera that captures the user's face in real time.

[1446] The smart glasses display a form for inputting body shape and physical characteristics data, in which the user inputs data such as height, weight, age, and gender.

[1447] Additionally, data is collected through the smart glasses' camera and microphone to recognize the user's emotional state.

[1448] 2. Server

[1449] Body shape and constitution data and emotional state data transmitted from the user device are received.

[1450] The received data is stored in a database, which centrally manages this data.

[1451] 3. Database

[1452] Stores data on each user's body shape, constitution, and emotional state.

[1453] Data will be acquired as needed and used for analysis.

[1454] 4. Generative AI Models

[1455] The system analyzes user data obtained from the database and generates optimal diet advice based on past success stories.

[1456] The generated diet advice is adjusted based on the emotional state data analyzed by the emotion engine.

[1457] 5. Emotion Engine

[1458] It analyzes data collected by the camera and microphone of the user's device to recognize the user's emotional state.

[1459] The recognized emotion data is stored in a database.

[1460] Hardware and Software

[1461] The hardware and software used includes:

[1462] Smart glasses (with built-in camera and microphone)

[1463] Server (connected to database)

[1464] Database management system (MySQL, etc.)

[1465] Generative AI model (AI model that learns from past success stories)

[1466] Emotion Engine (EmotionRecognition Library)

[1467] Specific examples

[1468] Suppose a user is wearing smart glasses while training on a treadmill. The glasses' camera captures facial expressions in real time, and an emotion engine detects stress levels. This information is sent to a server and stored in a database. The generative AI model provides real-time advice to ease up on the training based on the user's body shape and stress level data. Feedback such as "Your current pace is appropriate. Take a few minutes to relax and take some deep breaths" appears on the smart glasses' display.

[1469] Prompt Sentence Examples

[1470] "I'm a 170cm tall, 70kg, 30-year-old male who is under stress. What is the best fitness advice?"

[1471] This allows for specific and personalized diet and fitness advice tailored to the user's current situation, and individualized responses based on emotional state.

[1472] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1473] Step 1:

[1474] The user puts on the smart glasses and inputs their body shape and constitution data.

[1475] Input: The user uses the input form on the smart glasses to enter data such as height, weight, age, and gender.

[1476] Data processing: The entered data is cached locally.

[1477] Output: Body shape and constitution data is input, and the terminal prepares to send it to the server.

[1478] Step 2:

[1479] The smart glasses' camera and microphone collect data on the user's emotional state.

[1480] Input: Camera video data and microphone audio data.

[1481] Data processing: Video data is analyzed using facial recognition software, and audio data is processed using audio analysis software.

[1482] Output: The generated emotional state data is sent to the device.

[1483] Step 3:

[1484] The terminal transmits the body shape and constitution data and the emotional state data to the server.

[1485] Input: Body shape and constitution data, emotional state data.

[1486] Data Calculation: The terminal organizes the data into a single JSON document.

[1487] Output: The constructed JSON document is sent to the server.

[1488] Step 4:

[1489] The server analyzes the received data and stores it in a database.

[1490] Input: A JSON document containing body shape and constitution data, and emotional state data.

[1491] Data Transformation: Data is deserialized and mapped to the appropriate fields to be saved in the database.

[1492] Output: User data is saved in the database.

[1493] Step 5:

[1494] The server retrieves the necessary data from the database and analyzes it using a generative AI model.

[1495] Input: User's body shape and constitution data.

[1496] Data calculation: The generative AI model compares input data with past success cases and generates optimal diet advice.

[1497] Output: The generated diet advice.

[1498] Step 6:

[1499] The server adjusts the generated diet advice using an emotion engine.

[1500] Input: diet advice data, emotional state data.

[1501] Data calculation: Advice content is adjusted according to emotional state (for example, adding relaxation techniques when stressed).

[1502] Output: Tailored diet advice.

[1503] Step 7:

[1504] The server sends the final diet advice back to the user device.

[1505] Enter: tailored diet advice.

[1506] Data Processing: Advice data is serialized into a format that can be displayed on the user device.

[1507] Output: The advice data is sent to the user device.

[1508] Step 8:

[1509] The user device displays the received advice on the display of the smart glasses.

[1510] Enter: tailored diet advice.

[1511] Data calculation: The rendering process is carried out to display the advice in a visually appealing way.

[1512] Output: The user can see the diet advice on the smart glasses.

[1513] 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.

[1514] 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.

[1515] 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.

[1516] 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.

[1517] FIG. 9 is a diagram illustrating 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 actions 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.

[1518] 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.

[1519] 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).

[1520] 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.

[1521] 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."

[1522] 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.

[1523] 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).

[1524] 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.

[1525] 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.

[1526] 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.

[1527] 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.

[1528] 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.

[1529] 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.

[1530] 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.

[1531] 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.

[1532] 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.

[1533] 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.

[1534] The following is further disclosed regarding the above embodiment.

[1535] (Claim 1)

[1536] A means for a user to input body shape and constitution data;

[1537] means for receiving the input data and storing it in a database;

[1538] A means to analyze the stored data using a generative AI model and generate diet advice by comparing it with past success stories;

[1539] means for displaying the generated advice on a user device;

[1540] A system including:

[1541] (Claim 2)

[1542] 10. The system of claim 1, further comprising means for displaying a form for collecting the user's body shape and constitution data and providing a data submission function.

[1543] (Claim 3)

[1544] The system of claim 1, further comprising means for preprocessing data to be analyzed by the generative AI model and means for generating advice based on the analysis results.

[1545] "Example 1"

[1546] (Claim 1)

[1547] A means for a user to input body shape and constitution data;

[1548] means for receiving the input data and storing it in a database;

[1549] A means to analyze the stored data using a generative AI model and generate diet advice by comparing it with past success stories;

[1550] means for displaying the generated advice on a user device;

[1551] A system including:

[1552] (Claim 2)

[1553] 10. The system of claim 1, further comprising means for displaying a form for collecting the user's body shape and constitution data and providing a data submission function.

[1554] (Claim 3)

[1555] The system of claim 1, further comprising means for preprocessing data to be analyzed by the generative AI model and means for generating advice based on the analysis results.

[1556] (Claim 4)

[1557] means for transmitting the data entered by the user as an HTTP POST request;

[1558] means for storing the data received by the server in a database via a query;

[1559] means for sending an analysis request to the generative AI model using a prompt sentence;

[1560] The system of claim 1 , further comprising: means for returning the generated advice in a JSON format.

[1561] "Application Example 1"

[1562] (Claim 1)

[1563] A means for a user to input body shape and constitution data;

[1564] means for receiving the input data and storing it in a database;

[1565] A means to analyze the stored data using a generative AI model and generate diet advice by comparing it with past success stories;

[1566] means for displaying the generated advice on a user device;

[1567] A means of providing health advice to factory employees to monitor their health;

[1568] means for displaying said health advice on a factory robot or smart glasses;

[1569] A system including:

[1570] (Claim 2)

[1571] 10. The system of claim 1, further comprising means for displaying a form for collecting the user's body shape and constitution data and providing a data submission function.

[1572] (Claim 3)

[1573] The system of claim 1, further comprising means for preprocessing data to be analyzed by the generative AI model and means for generating advice based on the analysis results.

[1574] "Example 2: Combining Emotion Engines"

[1575] (Claim 1)

[1576] A means for a user to input body shape and constitution data;

[1577] means for receiving the input data and storing it in a database;

[1578] A means to analyze the stored data using a generative AI model and generate diet advice by comparing it with past success stories;

[1579] means for displaying the generated advice on a user device;

[1580] means for analyzing the user's voice and face to recognize an emotional state and adjusting the generated diet advice based on said emotional state;

[1581] means for displaying the tailored diet advice on the user device;

[1582] A system including:

[1583] (Claim 2)

[1584] 10. The system of claim 1, further comprising means for displaying a form for collecting the user's body shape and constitution data and providing a data submission function.

[1585] (Claim 3)

[1586] The system of claim 1, further comprising means for preprocessing data to be analyzed by the generative AI model and means for generating advice based on the analysis results.

[1587] "Application example 2 when combining emotion engines"

[1588] (Claim 1)

[1589] A means for a user to input body shape and constitution data;

[1590] means for receiving the input data and storing it in a database;

[1591] A means to analyze the stored data using a generative AI model and generate diet advice by comparing it with past success stories;

[1592] means for displaying the generated advice on a user device;

[1593] means for adjusting the advice based on the emotional state of the user;

[1594] means for analyzing voice and face to recognize said emotional state;

[1595] means for storing the analyzed emotional state data in a database;

[1596] A system including:

[1597] (Claim 2)

[1598] 10. The system of claim 1, further comprising means for displaying a form for collecting the user's body shape and constitution data and providing data transmission capabilities, and a device for recognizing the user's emotional state.

[1599] (Claim 3)

[1600] The system of claim 1, further comprising a means for preprocessing data to be analyzed by the generative AI model, and a means for generating diet advice based on the analysis results and providing individualized responses according to emotional state. [Explanation of symbols]

[1601] 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 a user to input body shape and constitution data; means for receiving the input data and storing it in a database; A means to analyze the stored data using a generative AI model and generate diet advice by comparing it with past success stories; means for displaying the generated advice on a user device; A system including:

2. 2. The system of claim 1, further comprising means for displaying a form for collecting the user's body type and constitution data and providing a data submission function.

3. The system according to claim 1 , further comprising means for preprocessing data to be analyzed by the generative AI model and means for generating advice based on the analysis results.

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